diff --git a/pkgdown.yml b/pkgdown.yml
index a6dbbb0..61f55dc 100644
--- a/pkgdown.yml
+++ b/pkgdown.yml
@@ -2,7 +2,7 @@ pandoc: 2.19.2
 pkgdown: 2.0.7
 pkgdown_sha: ~
 articles: {}
-last_built: 2024-02-16T03:40Z
+last_built: 2024-02-21T05:53Z
 urls:
   reference: https://bookish-disco-p832pyq.pages.github.io/reference
   article: https://bookish-disco-p832pyq.pages.github.io/articles
diff --git a/reference/get_table_transformations.html b/reference/get_table_transformations.html
index 35dfd85..8d75214 100644
--- a/reference/get_table_transformations.html
+++ b/reference/get_table_transformations.html
@@ -60,14 +60,15 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
     <dl><dt>transformation_result</dt>
-<dd><p>A list in which each element is the result of
-a transformation applied to a different dataset, computed with the
-<code><a href="transform_dataset.html">transform_dataset</a></code> function.</p></dd>
+<dd><p>A list in which each element is the result of a
+transformation applied to a different dataset, computed with the
+<a href="transform_dataset.html">transform_dataset</a> function.</p></dd>
 
 
 <dt>best_normalize_details</dt>
-<dd><p>Logical, should information about the transformations
-selected by bestNormalize for each feature be displayed? Default value is <code>FALSE</code>.</p></dd>
+<dd><p>Logical, should information about the
+transformations selected by bestNormalize for each feature be displayed?
+Default value is <code>FALSE</code>.</p></dd>
 
 </dl></div>
     <div class="section level2">
@@ -75,9 +76,9 @@ <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2
     
 
 <p>A tibble with columns <code>'Dataset'</code> and <code>'Transformation'</code>. If
-<code>best_normalize_details = TRUE</code>, an additional column <code>'Details'</code> lists the chsoen
-transformation applied to each feature of the corresponding dataset for a bestNormalize
-transformation.</p>
+<code>best_normalize_details = TRUE</code>, an additional column <code>'Details'</code> lists the
+chsoen transformation applied to each feature of the corresponding dataset
+for a bestNormalize transformation.</p>
     </div>
 
   </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
diff --git a/reference/index.html b/reference/index.html
index 4eafc13..e63c246 100644
--- a/reference/index.html
+++ b/reference/index.html
@@ -372,6 +372,11 @@ <h2 id="transformation">Transformation<a class="anchor" aria-label="anchor" href
         <dd>Applies a transformation to a dataset from a MultiDataSet object</dd>
       </dl><dl><dt>
           
+          <code><a href="transform_logx.html">transform_logx()</a></code> 
+        </dt>
+        <dd>Applies a log-x transformation to matrix</dd>
+      </dl><dl><dt>
+          
           <code><a href="transform_vsn.html">transform_vsn()</a></code> 
         </dt>
         <dd>Applies Variance Stabilising Normalisation (vsn) to matrix</dd>
diff --git a/reference/transform_dataset.html b/reference/transform_dataset.html
index c98186d..a36b112 100644
--- a/reference/transform_dataset.html
+++ b/reference/transform_dataset.html
@@ -67,6 +67,8 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 <span>  return_multidataset <span class="op">=</span> <span class="cn">FALSE</span>,</span>
 <span>  return_matrix_only <span class="op">=</span> <span class="cn">FALSE</span>,</span>
 <span>  verbose <span class="op">=</span> <span class="cn">TRUE</span>,</span>
+<span>  log_base <span class="op">=</span> <span class="fl">2</span>,</span>
+<span>  pre_log_function <span class="op">=</span> <span class="va">zero_to_half_min</span>,</span>
 <span>  <span class="va">method</span>,</span>
 <span>  <span class="va">...</span></span>
 <span><span class="op">)</span></span></code></pre></div>
@@ -84,8 +86,8 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 
 <dt>transformation</dt>
 <dd><p>Character, transformation to be applied. Possible
-values are: <code>vsn</code>, <code>vst-deseq2</code>, <code>best-normalize-auto</code> or
-<code>best-normalize-manual</code>. See <code>Details</code>.</p></dd>
+values are: <code>vsn</code>, <code>vst-deseq2</code>, <code>logx</code> <code>best-normalize-auto</code>
+or <code>best-normalize-manual</code>. See <code>Details</code>.</p></dd>
 
 
 <dt>return_multidataset</dt>
@@ -108,6 +110,18 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 printed? Default value is <code>TRUE</code>.</p></dd>
 
 
+<dt>log_base</dt>
+<dd><p>Numeric, the base with respect to which logarithms are
+computed. Default value is <code>2</code>. Only used if <code>transformation = 'logx'</code>.</p></dd>
+
+
+<dt>pre_log_function</dt>
+<dd><p>Function that will be applied to the matrix before
+the log transformation (e.g. to apply an offset to the values to avoid
+issues with zeros). Default value is the <code><a href="zero_to_half_min.html">zero_to_half_min()</a></code> function.
+Only used if <code>transformation = 'logx'</code>.</p></dd>
+
+
 <dt>method</dt>
 <dd><p>Character, if <code>transformation = 'best-normalize-manual'</code>, which
 normalisation method should be applied. See possible values in
@@ -146,6 +160,8 @@ <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></
 package. This method is applicable to count data only. This transformation is
 recommended for RNAseq or similar count-based datasets. In practice, applies
 the <code><a href="transform_vst.html">transform_vst()</a></code> function.</p></li>
+<li><p><code>logx</code>: log-transformation (default to log2, but base can be specified).
+In practice, applies the <code><a href="transform_logx.html">transform_logx()</a></code> function.</p></li>
 <li><p><code>best-normalize-auto</code>: most appropriate normalisation method automatically
 selected from a number of options, implemented in the
 <code><a href="https://petersonr.github.io/bestNormalize/reference/bestNormalize.html" class="external-link">bestNormalize::bestNormalize()</a></code> function from the <code>bestNormalize</code> package.
@@ -163,10 +179,10 @@ <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></
 <li><p><code>"center_scale"</code>: data is centered and scaled;</p></li>
 <li><p><code>"exp_x"</code>: data is transformed as <code>exp(x)</code>;</p></li>
 <li><p><code>"log_x"</code>: data is transformed as <code>log_b(x+a)</code> (<code>a</code> and <code>b</code> either
-selected automatically or passed as arguments);</p></li>
+selected automatically per variable or passed as arguments);</p></li>
 <li><p><code>"orderNorm"</code>: Ordered Quantile technique;</p></li>
 <li><p><code>"sqrt_x"</code>: data transformed as <code>sqrt(x + a)</code> (<code>a</code> selected automatically
-or passed as argument),</p></li>
+per variable or passed as argument),</p></li>
 <li><p><code>"yeojohnson"</code>: Yeo-Johnson transformation.</p></li>
 </ul></li>
 </ul></div>
diff --git a/reference/transform_logx.html b/reference/transform_logx.html
new file mode 100644
index 0000000..2e429af
--- /dev/null
+++ b/reference/transform_logx.html
@@ -0,0 +1,115 @@
+<!DOCTYPE html>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Applies a log-x transformation (by default log2) through the log()
+function."><title>Applies a log-x transformation to matrix — transform_logx • moiraine</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Applies a log-x transformation to matrix — transform_logx"><meta property="og:description" content="Applies a log-x transformation (by default log2) through the log()
+function."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
+<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
+<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
+<![endif]--></head><body>
+    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
+    
+
+    <nav class="navbar fixed-top navbar-light navbar-expand-lg bg-light"><div class="container">
+    
+    <a class="navbar-brand me-2" href="../index.html">moiraine</a>
+
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.0.0.9000</small>
+
+    
+    <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
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+
+    <div id="navbar" class="collapse navbar-collapse ms-3">
+      <ul class="navbar-nav me-auto"><li class="active nav-item">
+  <a class="nav-link" href="../reference/index.html">Reference</a>
+</li>
+      </ul><form class="form-inline my-2 my-lg-0" role="search">
+        <input type="search" class="form-control me-sm-2" aria-label="Toggle navigation" name="search-input" data-search-index="../search.json" id="search-input" placeholder="Search for" autocomplete="off"></form>
+
+      <ul class="navbar-nav"><li class="nav-item">
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+    <span class="fab fa fab fa-github fa-lg"></span>
+     
+  </a>
+</li>
+      </ul></div>
+
+    
+  </div>
+</nav><div class="container template-reference-topic">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Applies a log-x transformation to matrix</h1>
+      <small class="dont-index">Source: <a href="https://github.com/PlantandFoodResearch/moiraine/blob/HEAD/R/transformation.R" class="external-link"><code>R/transformation.R</code></a></small>
+      <div class="d-none name"><code>transform_logx.Rd</code></div>
+    </div>
+
+    <div class="ref-description section level2">
+    <p>Applies a log-x transformation (by default log2) through the <code><a href="https://rdrr.io/r/base/Log.html" class="external-link">log()</a></code>
+function.</p>
+    </div>
+
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">transform_logx</span><span class="op">(</span></span>
+<span>  <span class="va">mat</span>,</span>
+<span>  return_matrix_only <span class="op">=</span> <span class="cn">FALSE</span>,</span>
+<span>  log_base <span class="op">=</span> <span class="fl">2</span>,</span>
+<span>  pre_log_function <span class="op">=</span> <span class="va">zero_to_half_min</span></span>
+<span><span class="op">)</span></span></code></pre></div>
+    </div>
+
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>mat</dt>
+<dd><p>Numeric matrix.</p></dd>
+
+
+<dt>return_matrix_only</dt>
+<dd><p>Logical, should only the transformed matrix be
+returned? If <code>TRUE</code>, the function will return a matrix. If <code>FALSE</code>, the
+function instead returns a list with the transformed data and potentially
+other information relevant to the transformation. Default value is <code>FALSE</code>.</p></dd>
+
+
+<dt>log_base</dt>
+<dd><p>Numeric, the base with respect to which logarithms are
+computed.</p></dd>
+
+
+<dt>pre_log_function</dt>
+<dd><p>Function that will be applied to the matrix before
+the log transformation (e.g. to apply an offset to the values to avoid
+issues with zeros). Default value is the <code><a href="zero_to_half_min.html">zero_to_half_min()</a></code> function.</p></dd>
+
+</dl></div>
+    <div class="section level2">
+    <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
+    
+
+<p>Depending on the <code>return_matrix_only</code>, either a matrix of
+transformed data, or a list with the following elements:</p><ul><li><p><code>transformed_data</code>: matrix of the transformed data;</p></li>
+<li><p><code>info_transformation</code>: a list with the log base used and the function
+applied prior to log-transformation.</p></li>
+</ul></div>
+
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
+
+
+    <footer><div class="pkgdown-footer-left">
+  <p></p><p>Developed by Olivia Angelin-Bonnet.</p>
+</div>
+
+<div class="pkgdown-footer-right">
+  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
+</div>
+
+    </footer></div>
+
+  
+
+  
+
+  </body></html>
+
diff --git a/reference/transformation_datasets_factory.html b/reference/transformation_datasets_factory.html
index 9a2dfa0..b413643 100644
--- a/reference/transformation_datasets_factory.html
+++ b/reference/transformation_datasets_factory.html
@@ -57,6 +57,8 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 <span>  return_matrix_only <span class="op">=</span> <span class="cn">FALSE</span>,</span>
 <span>  target_name_prefix <span class="op">=</span> <span class="st">""</span>,</span>
 <span>  transformed_data_name <span class="op">=</span> <span class="cn">NULL</span>,</span>
+<span>  log_bases <span class="op">=</span> <span class="fl">2</span>,</span>
+<span>  pre_log_functions <span class="op">=</span> <span class="va">zero_to_half_min</span>,</span>
 <span>  <span class="va">methods</span>,</span>
 <span>  <span class="va">...</span></span>
 <span><span class="op">)</span></span></code></pre></div>
@@ -65,83 +67,118 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
     <dl><dt>mo_data_target</dt>
-<dd><p>Symbol, the name of the target containing the <code>MultiDataSet</code> object.</p></dd>
+<dd><p>Symbol, the name of the target containing the
+<code>MultiDataSet</code> object.</p></dd>
 
 
 <dt>transformations</dt>
-<dd><p>Named character vector, name of each element is the name of a dataset
-to transform, corresponding element gives the type of transformation to apply to the dataset
-(e.g. <code>c(rnaseq = 'vst-deseq2', phenotypes = 'best-normalize-auto')</code>). See Details
-for a list of available transformations. If <code>'best-normalize-auto'</code> is selected, need to
-provide the <code>methods</code> argument as well.</p></dd>
+<dd><p>Named character vector, name of each element is the
+name of a dataset to transform, corresponding element gives the type of
+transformation to apply to the dataset (e.g. <code>c(rnaseq = 'vst-deseq2', phenotypes = 'best-normalize-auto')</code>). See Details for a list of available
+transformations. If <code>'best-normalize-auto'</code> is selected, need to provide
+the <code>methods</code> argument as well.</p></dd>
 
 
 <dt>return_matrix_only</dt>
-<dd><p>Logical, should only the transformed matrix be returned for each
-transformation? If <code>TRUE</code>, only transformed matrices will be stored. If <code>FALSE</code>,
-instead for each transformation, a list with the transformed data and potentially other
-information relevant to the transformation will be saved. Default value is <code>FALSE</code>.</p></dd>
+<dd><p>Logical, should only the transformed matrix be
+returned for each transformation? If <code>TRUE</code>, only transformed matrices will
+be stored. If <code>FALSE</code>, instead for each transformation, a list with the
+transformed data and potentially other information relevant to the
+transformation will be saved. Default value is <code>FALSE</code>.</p></dd>
 
 
 <dt>target_name_prefix</dt>
-<dd><p>Character, a prefix to add to the name of the targets created by this target factory.
-Default value is <code>""</code>.</p></dd>
+<dd><p>Character, a prefix to add to the name of the
+targets created by this target factory. Default value is <code>""</code>.</p></dd>
 
 
 <dt>transformed_data_name</dt>
-<dd><p>Character, the name of the target containing the <code>MultiDataSet</code> with
-transformed data to be created. If <code>NULL</code>, will be selected automatically. Default value is <code>NULL</code>.</p></dd>
+<dd><p>Character, the name of the target containing the
+<code>MultiDataSet</code> with transformed data to be created. If <code>NULL</code>, will be
+selected automatically. Default value is <code>NULL</code>.</p></dd>
+
+
+<dt>log_bases</dt>
+<dd><p>Numeric or named numeric list, gives for each dataset for
+which the <code>'logx'</code> transformation is selected the log base to use. If one
+value, will be used for all concerned datasets. Otherwise, can specify a
+different log-base for each concerned dataset by passing a named list.</p></dd>
+
+
+<dt>pre_log_functions</dt>
+<dd><p>Function or named list of functions, gives for each
+dataset for which the `'logx`` transformation is selected the function that
+will be applied to the matrix before the log transformation (e.g. to apply
+an offset to the values to avoid issues with zeros). Default value is the
+<code><a href="zero_to_half_min.html">zero_to_half_min()</a></code> function. If one value, will be used for all concerned
+datasets. Otherwise, can specify a different log-base for each concerned
+dataset by passing a named list.</p></dd>
 
 
 <dt>methods</dt>
-<dd><p>Named character vector, gives for each dataset for which the <code>'best-normalize-manual'</code>
-transformation is selected which normalisation method should be applied. See possible values in
-Details.</p></dd>
+<dd><p>Character or named character list, gives for each dataset for
+which the <code>'best-normalize-manual'</code> transformation is selected the
+normalisation method that should be applied. See possible values in
+Details. If one value, will be used for all concerned datasets. Otherwise,
+can specify a different method for each concerned dataset by passing a
+named list.</p></dd>
 
 
 <dt>...</dt>
-<dd><p>Further arguments passed to the <code><a href="transform_dataset.html">transform_dataset</a></code> function or the
-<code>method</code> function from the <code>bestNormalize</code> package. Only relevant for <code>'best-normalize-XX'</code> transformations.</p></dd>
+<dd><p>Further arguments passed to the <code><a href="transform_dataset.html">transform_dataset</a></code>
+function or the <code>method</code> function from the <code>bestNormalize</code> package. Only
+relevant for <code>'best-normalize-XX'</code> transformations.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
     
 
-<p>A list of target objects. With <code>target_name_prefix = ""</code> and <code>transformed_data_name = NULL</code>,
-the following targets are created:</p><ul><li><p><code>transformations_spec</code>: generates a grouped tibble where each row corresponds to one dataset to be tranformed,
-with the columns specifying each dataset name and the transformation to apply.</p></li>
-<li><p><code>transformations_runs_list</code>: a dynamic branching target that runs the <code><a href="transform_dataset.html">transform_dataset</a></code> function
-on each dataset. Returns a list.</p></li>
-<li><p><code>transformed_set</code>: a target that returns the <code>MultiDataSet</code> object with the original data replaced by the
-transformed data.</p></li>
+<p>A list of target objects. With <code>target_name_prefix = ""</code> and
+<code>transformed_data_name = NULL</code>, the following targets are created:</p><ul><li><p><code>transformations_spec</code>: generates a grouped tibble where each row
+corresponds to one dataset to be tranformed, with the columns specifying each
+dataset name and the transformation to apply.</p></li>
+<li><p><code>transformations_runs_list</code>: a dynamic branching target that runs the
+<code><a href="transform_dataset.html">transform_dataset()</a></code> function on each dataset. Returns a list.</p></li>
+<li><p><code>transformed_set</code>: a target that returns the <code>MultiDataSet</code> object with the
+original data replaced by the transformed data.</p></li>
 </ul></div>
     <div class="section level2">
     <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
-    <p>#' Currently implemented transformations and recommendations based on dataset type:</p><ul><li><p><code>vsn</code>: Variance Stabilising normalisation, implemented in the <code><a href="https://rdrr.io/pkg/vsn/man/justvsn.html" class="external-link">justvsn</a></code>
-function from the <code>vsn</code> package. This method was originally developed for microarray intensities.
-In practice, applies the <code><a href="transform_vsn.html">transform_vsn</a></code> function. This transformation is recommended
-for microarray, metabolome, chemical or other intensity-based datasets.</p></li>
+    <p>Currently implemented transformations and recommendations based on dataset
+type:</p><ul><li><p><code>vsn</code>: Variance Stabilising normalisation, implemented in the
+<code><a href="https://rdrr.io/pkg/vsn/man/justvsn.html" class="external-link">vsn::justvsn()</a></code> function from the <code>vsn</code> package. This method was originally
+developed for microarray intensities. This transformation is recommended for
+microarray, metabolome, chemical or other intensity-based datasets. In
+practice, applies the <code><a href="transform_vsn.html">transform_vsn()</a></code> function.</p></li>
 <li><p><code>vst-deseq2</code>: Variance Stabilising Transformation, implemented in the
-<code><a href="https://rdrr.io/pkg/DESeq2/man/varianceStabilizingTransformation.html" class="external-link">varianceStabilizingTransformation</a></code> function from the <code>DESeq2</code> package.
-This method is applicable to count data only. In practice, applies the <code><a href="transform_vst.html">transform_vst</a></code> function.
-This transformation is recommended for RNAseq or similar count-based datasets.</p></li>
-<li><p><code>best-normalize-auto</code>: most appropriate normalisation method automatically selected from a number
-of options, implemented in the <code><a href="https://petersonr.github.io/bestNormalize/reference/bestNormalize.html" class="external-link">bestNormalize</a></code> function from the
-<code>bestNormalize</code> package. In practice, applies the <code><a href="transform_bestNormalise_auto.html">transform_bestNormalise_auto</a></code> function.
-This transformation is recommended for phenotypes that are each measured on different scales (since the
-transformation method selected will potentially be different across the phenotypes), preferably with a
-reasonable number of features (less than 100) to avoid large computation times.</p></li>
-<li><p><code>best-normalize-manual</code>: performs the same transformation (specified with the <code>method</code> argument)
-to each feature of a dataset. This transformation is recommended for phenotypes data in which the
-different phenotypes are measured on the same scale. The different normalisation methods are:</p><ul><li><p><code>"arcsinh_x"</code>: data is transformed as <code>log(x + sqrt(x^2 + 1))</code>;</p></li>
+<code><a href="https://rdrr.io/pkg/DESeq2/man/varianceStabilizingTransformation.html" class="external-link">DESeq2::varianceStabilizingTransformation()</a></code> function from the <code>DESeq2</code>
+package. This method is applicable to count data only. This transformation is
+recommended for RNAseq or similar count-based datasets. In practice, applies
+the <code><a href="transform_vst.html">transform_vst()</a></code> function.</p></li>
+<li><p><code>logx</code>: log-transformation (default to log2, but base can be specified).
+In practice, applies the <code><a href="transform_logx.html">transform_logx()</a></code> function.</p></li>
+<li><p><code>best-normalize-auto</code>: most appropriate normalisation method automatically
+selected from a number of options, implemented in the
+<code><a href="https://petersonr.github.io/bestNormalize/reference/bestNormalize.html" class="external-link">bestNormalize::bestNormalize()</a></code> function from the <code>bestNormalize</code> package.
+This transformation is recommended for phenotypes that are each measured on
+different scales (since the transformation method selected will potentially
+be different across the features), preferably with a reasonable number of
+features (less than 100) to avoid large computation times. In practice,
+applies the <code><a href="transform_bestNormalise_auto.html">transform_bestNormalise_auto()</a></code> function.</p></li>
+<li><p><code>best-normalize-manual</code>: performs the same transformation (specified
+through the <code>method</code> argument) to each feature of a dataset. This
+transformation is recommended for phenotypes data in which the different
+phenotypes are measured on the same scale. The different normalisation
+methods are:</p><ul><li><p><code>"arcsinh_x"</code>: data is transformed as <code>log(x + sqrt(x^2 + 1))</code>;</p></li>
 <li><p><code>"boxcox"</code>: Box Cox transformation;</p></li>
 <li><p><code>"center_scale"</code>: data is centered and scaled;</p></li>
 <li><p><code>"exp_x"</code>: data is transformed as <code>exp(x)</code>;</p></li>
-<li><p><code>"log_x"</code>: data is transformed as <code>log_b(x+a)</code> (<code>a</code> and <code>b</code> either selected automatically or passed as
-arguments);</p></li>
+<li><p><code>"log_x"</code>: data is transformed as <code>log_b(x+a)</code> (<code>a</code> and <code>b</code> either
+selected automatically per variable or passed as arguments);</p></li>
 <li><p><code>"orderNorm"</code>: Ordered Quantile technique;</p></li>
-<li><p><code>"sqrt_x"</code>: data transformed as <code>sqrt(x + a)</code> (<code>a</code> selected automatically or passed as argument),</p></li>
+<li><p><code>"sqrt_x"</code>: data transformed as <code>sqrt(x + a)</code> (<code>a</code> selected automatically
+per variable or passed as argument),</p></li>
 <li><p><code>"yeojohnson"</code>: Yeo-Johnson transformation.</p></li>
 </ul></li>
 </ul></div>
@@ -173,15 +210,30 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span>    transformed_data_name <span class="op">=</span> <span class="st">"mo_set_transformed"</span></span></span>
 <span class="r-in"><span>  <span class="op">)</span>,</span></span>
 <span class="r-in"><span></span></span>
-<span class="r-in"><span>  <span class="co">## Example 2 - with a log2 transformation for the metabolome dataset</span></span></span>
+<span class="r-in"><span>  <span class="co">## Example 2 - with a log2 transformation for both datasets</span></span></span>
+<span class="r-in"><span>  <span class="fu">transformation_datasets_factory</span><span class="op">(</span></span></span>
+<span class="r-in"><span>    <span class="va">mo_set_complete</span>,</span></span>
+<span class="r-in"><span>    <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span></span></span>
+<span class="r-in"><span>      <span class="st">"rnaseq"</span> <span class="op">=</span> <span class="st">"logx"</span>,</span></span>
+<span class="r-in"><span>      <span class="st">"metabolome"</span> <span class="op">=</span> <span class="st">"logx"</span></span></span>
+<span class="r-in"><span>    <span class="op">)</span>,</span></span>
+<span class="r-in"><span>    log_bases <span class="op">=</span> <span class="fl">2</span>,</span></span>
+<span class="r-in"><span>    pre_log_functions <span class="op">=</span> <span class="va">zero_to_half_min</span></span></span>
+<span class="r-in"><span>  <span class="op">)</span>,</span></span>
+<span class="r-in"><span></span></span>
+<span class="r-in"><span>  <span class="co">## Example 3 - with different log bases for each dataset and a different</span></span></span>
+<span class="r-in"><span>  <span class="co">## preprocessing function to be run before applying the log</span></span></span>
 <span class="r-in"><span>  <span class="fu">transformation_datasets_factory</span><span class="op">(</span></span></span>
 <span class="r-in"><span>    <span class="va">mo_set_complete</span>,</span></span>
 <span class="r-in"><span>    <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span></span></span>
-<span class="r-in"><span>      <span class="st">"rnaseq"</span> <span class="op">=</span> <span class="st">"vst-deseq2"</span>,</span></span>
-<span class="r-in"><span>      <span class="st">"metabolome"</span> <span class="op">=</span> <span class="st">"best-normalize-manual"</span></span></span>
+<span class="r-in"><span>      <span class="st">"rnaseq"</span> <span class="op">=</span> <span class="st">"logx"</span>,</span></span>
+<span class="r-in"><span>      <span class="st">"metabolome"</span> <span class="op">=</span> <span class="st">"logx"</span></span></span>
 <span class="r-in"><span>    <span class="op">)</span>,</span></span>
-<span class="r-in"><span>    methods <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"metabolome"</span> <span class="op">=</span> <span class="st">"log_x"</span><span class="op">)</span>,</span></span>
-<span class="r-in"><span>    b <span class="op">=</span> <span class="fl">2</span></span></span>
+<span class="r-in"><span>    log_bases <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>rnaseq <span class="op">=</span> <span class="fl">10</span>, metabolome <span class="op">=</span> <span class="fl">2</span><span class="op">)</span>,</span></span>
+<span class="r-in"><span>    pre_log_functions <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span></span></span>
+<span class="r-in"><span>      rnaseq <span class="op">=</span> \<span class="op">(</span><span class="va">x</span><span class="op">)</span> <span class="va">x</span> <span class="op">+</span> <span class="fl">0.5</span>,</span></span>
+<span class="r-in"><span>      metabolome <span class="op">=</span> <span class="va">zero_to_half_min</span></span></span>
+<span class="r-in"><span>     <span class="op">)</span></span></span>
 <span class="r-in"><span>  <span class="op">)</span></span></span>
 <span class="r-in"><span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="op">}</span></span></span>
diff --git a/reference/zero_to_half_min.html b/reference/zero_to_half_min.html
new file mode 100644
index 0000000..8fc54b7
--- /dev/null
+++ b/reference/zero_to_half_min.html
@@ -0,0 +1,89 @@
+<!DOCTYPE html>
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+matrix."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
+<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
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+  <a class="nav-link" href="../reference/index.html">Reference</a>
+</li>
+      </ul><form class="form-inline my-2 my-lg-0" role="search">
+        <input type="search" class="form-control me-sm-2" aria-label="Toggle navigation" name="search-input" data-search-index="../search.json" id="search-input" placeholder="Search for" autocomplete="off"></form>
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+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Replace zeros with half-min in matrix</h1>
+      <small class="dont-index">Source: <a href="https://github.com/PlantandFoodResearch/moiraine/blob/HEAD/R/transformation.R" class="external-link"><code>R/transformation.R</code></a></small>
+      <div class="d-none name"><code>zero_to_half_min.Rd</code></div>
+    </div>
+
+    <div class="ref-description section level2">
+    <p>Replace zero values in a matrix by half of the minimum non-null value in the
+matrix.</p>
+    </div>
+
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">zero_to_half_min</span><span class="op">(</span><span class="va">mat</span><span class="op">)</span></span></code></pre></div>
+    </div>
+
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>mat</dt>
+<dd><p>Numeric matrix.</p></dd>
+
+</dl></div>
+    <div class="section level2">
+    <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
+    
+
+<p>The matrix with zero values replaced.</p>
+    </div>
+
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
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diff --git a/search.json b/search.json
index c925fce..e5d72cf 100644
--- a/search.json
+++ b/search.json
@@ -1 +1 @@
-[{"path":"https://bookish-disco-p832pyq.pages.github.io/LICENSE.html","id":null,"dir":"","previous_headings":"","what":"MIT License","title":"MIT License","text":"Copyright (c) 2024 moiraine authors Permission hereby granted, free charge, person obtaining copy software associated documentation files (“Software”), deal Software without restriction, including without limitation rights use, copy, modify, merge, publish, distribute, sublicense, /sell copies Software, permit persons Software furnished , subject following conditions: copyright notice permission notice shall included copies substantial portions Software. SOFTWARE PROVIDED “”, WITHOUT WARRANTY KIND, EXPRESS IMPLIED, INCLUDING LIMITED WARRANTIES MERCHANTABILITY, FITNESS PARTICULAR PURPOSE NONINFRINGEMENT. EVENT SHALL AUTHORS COPYRIGHT HOLDERS LIABLE CLAIM, DAMAGES LIABILITY, WHETHER ACTION CONTRACT, TORT OTHERWISE, ARISING , CONNECTION SOFTWARE USE DEALINGS SOFTWARE.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Olivia Angelin-Bonnet. Author, maintainer.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Angelin-Bonnet O (2024). moiraine: Construction Reproducible Pipelines Testing Comparing Multi-omics Integration Tools. R package version 0.0.0.9000, https://bookish-disco-p832pyq.pages.github.io/, https://github.com/PlantandFoodResearch/moiraine.","code":"@Manual{,   title = {moiraine: Construction of Reproducible Pipelines for Testing and Comparing Multi-omics Integration Tools},   author = {Olivia Angelin-Bonnet},   year = {2024},   note = {R package version 0.0.0.9000, https://bookish-disco-p832pyq.pages.github.io/},   url = {https://github.com/PlantandFoodResearch/moiraine}, }"},{"path":"https://bookish-disco-p832pyq.pages.github.io/index.html","id":"moiraine","dir":"","previous_headings":"","what":"Construction of Reproducible Pipelines for Testing and Comparing Multi-omics Integration Tools","title":"Construction of Reproducible Pipelines for Testing and Comparing Multi-omics Integration Tools","text":"moiraine package facilitating construction reproducible analysis pipeline multi-omics data integration. provides functions automate data import, pre-processing, transformation, integration several tools. relies targets package generate reproducible workflows. moiraine currently supports multi-omics data integration : sPLS DIABLO mixOmics package; sO2PLS omicsPLS package; MOFA MEFISTO MOFA2 package.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"Construction of Reproducible Pipelines for Testing and Comparing Multi-omics Integration Tools","text":"can install development version moiraine GitHub :","code":"# install.packages(\"devtools\") devtools::install_github(\"PlantandFoodResearch/moiraine\")"},{"path":"https://bookish-disco-p832pyq.pages.github.io/index.html","id":"example","dir":"","previous_headings":"","what":"Example","title":"Construction of Reproducible Pipelines for Testing and Comparing Multi-omics Integration Tools","text":"get started, create new analysis pipeline associated report working directory : using moiraine, encourage get familiar targets package; manual great place start.","code":"library(moiraine)  create_targets_pipeline() create_report(\"integration_analysis_report.Rmd\")"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/MetabolomeSet-class.html","id":null,"dir":"Reference","previous_headings":"","what":"Class to contain objects describing high-throughput metabolomics assays. — MetabolomeSet","title":"Class to contain objects describing high-throughput metabolomics assays. — MetabolomeSet","text":"Container high-throughput metabolomics assays experimental metadata. MetabolomeSet class derived Biobase::eSet().","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/PhenotypeSet-class.html","id":null,"dir":"Reference","previous_headings":"","what":"Class to contain objects describing phenotypic assays. — PhenotypeSet","title":"Class to contain objects describing phenotypic assays. — PhenotypeSet","text":"Container phenotypic assays experimental metadata. PhenotypeSet class derived Biobase::eSet().","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/add_features_metadata.html","id":null,"dir":"Reference","previous_headings":"","what":"Adding data-frame to features metadata — add_features_metadata","title":"Adding data-frame to features metadata — add_features_metadata","text":"Adds information data-frame features metadata MultiDataSet object.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/add_features_metadata.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Adding data-frame to features metadata — add_features_metadata","text":"","code":"add_features_metadata(mo_data, df)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/add_features_metadata.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Adding data-frame to features metadata — add_features_metadata","text":"mo_data MultiDataSet::MultiDataSet object. df tibble data-frame features information, least columns feature_id (giving feature IDs) dataset (giving name dataset features belong), one row per feature ID. Can contain info features different datasets.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/add_features_metadata.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Adding data-frame to features metadata — add_features_metadata","text":"MultiDataSet object, info df adding corresponding features metadata.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/add_metabo-MultiDataSet-MetabolomeSet-method.html","id":null,"dir":"Reference","previous_headings":"","what":"Adds a MetabolomeSet to a MultiDataSet object. — add_metabo,MultiDataSet,MetabolomeSet-method","title":"Adds a MetabolomeSet to a MultiDataSet object. — add_metabo,MultiDataSet,MetabolomeSet-method","text":"Adds MetabolomeSet MultiDataSet object.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/add_metabo-MultiDataSet-MetabolomeSet-method.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Adds a MetabolomeSet to a MultiDataSet object. — add_metabo,MultiDataSet,MetabolomeSet-method","text":"","code":"# S4 method for MultiDataSet,MetabolomeSet add_metabo(object, met_set, warnings = TRUE, ...)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/add_metabo-MultiDataSet-MetabolomeSet-method.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Adds a MetabolomeSet to a MultiDataSet object. — add_metabo,MultiDataSet,MetabolomeSet-method","text":"object MultiDataSet::MultiDataSet object. met_set MetabolomeSet object. warnings Logical, warnings displayed? Default TRUE. ... arguments passed MultiDataSet::add_eset() function.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/add_metabo-MultiDataSet-MetabolomeSet-method.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Adds a MetabolomeSet to a MultiDataSet object. — add_metabo,MultiDataSet,MetabolomeSet-method","text":"new MultiDataSet::MultiDataSet object slot filled.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/add_metabo-methods.html","id":null,"dir":"Reference","previous_headings":"","what":"Method to add a MetabolomeSet to a MultiDataSet object. — add_metabo","title":"Method to add a MetabolomeSet to a MultiDataSet object. — add_metabo","text":"Method add MetabolomeSet MultiDataSet object.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/add_metabo-methods.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Method to add a MetabolomeSet to a MultiDataSet object. — add_metabo","text":"","code":"add_metabo(object, met_set, warnings = TRUE, ...)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/add_metabo-methods.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Method to add a MetabolomeSet to a MultiDataSet object. — add_metabo","text":"object MultiDataSet::MultiDataSet object. met_set MetabolomeSet object. warnings Logical, warnings displayed? Default TRUE. ... arguments passed MultiDataSet::add_eset() function.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/add_metabo-methods.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Method to add a MetabolomeSet to a MultiDataSet object. — add_metabo","text":"new MultiDataSet::MultiDataSet object slot filled.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/add_omics_set.html","id":null,"dir":"Reference","previous_headings":"","what":"Adds an omics set to a MultiDataSet object — add_omics_set","title":"Adds an omics set to a MultiDataSet object — add_omics_set","text":"Adds omics set existing MultiDataSet object.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/add_omics_set.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Adds an omics set to a MultiDataSet object — add_omics_set","text":"","code":"add_omics_set(mo_data, omics_set, ds_name, ...)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/add_omics_set.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Adds an omics set to a MultiDataSet object — add_omics_set","text":"mo_data MultiDataSet::MultiDataSet object. omics_set Biobase::eSet object, created via create_omics_set(). Currently accepted objects: Biobase::SnpSet, Biobase::ExpressionSet, MetabolomeSet, PhenotypeSet. ds_name Character, name dataset (used suffix name dataset resulting MultiDataSet object). ... arguments passed [MultiDataSet::add_snps()], [MultiDataSet::add_rnaseq()], [add_metabo()] [add_pheno()] (depending omics_set` class).","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/add_omics_set.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Adds an omics set to a MultiDataSet object — add_omics_set","text":"MultiDataSet::MultiDataSet object, mo_data omics_set additional dataset.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/add_omics_set.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Adds an omics set to a MultiDataSet object — add_omics_set","text":"","code":"if (FALSE) { add_omics_set(mo_data, omics_set, \"exp1\") }"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/add_pheno-MultiDataSet-PhenotypeSet-method.html","id":null,"dir":"Reference","previous_headings":"","what":"Adds a PhenotypeSet to a MultiDataSet object. — add_pheno,MultiDataSet,PhenotypeSet-method","title":"Adds a PhenotypeSet to a MultiDataSet object. — add_pheno,MultiDataSet,PhenotypeSet-method","text":"Adds PhenotypeSet MultiDataSet object.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/add_pheno-MultiDataSet-PhenotypeSet-method.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Adds a PhenotypeSet to a MultiDataSet object. — add_pheno,MultiDataSet,PhenotypeSet-method","text":"","code":"# S4 method for MultiDataSet,PhenotypeSet add_pheno(object, pheno_set, warnings = TRUE, ...)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/add_pheno-MultiDataSet-PhenotypeSet-method.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Adds a PhenotypeSet to a MultiDataSet object. — add_pheno,MultiDataSet,PhenotypeSet-method","text":"object MultiDataSet::MultiDataSet object. pheno_set PhenotypeSet object. warnings Logical, warnings displayed? Default TRUE. ... arguments passed MultiDataSet::add_eset() function.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/add_pheno-MultiDataSet-PhenotypeSet-method.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Adds a PhenotypeSet to a MultiDataSet object. — add_pheno,MultiDataSet,PhenotypeSet-method","text":"new MultiDataSet::MultiDataSet object slot filled.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/add_pheno-methods.html","id":null,"dir":"Reference","previous_headings":"","what":"Method to add a PhenotypeSet to a MultiDataSet object. — add_pheno","title":"Method to add a PhenotypeSet to a MultiDataSet object. — add_pheno","text":"Method add PhenotypeSet MultiDataSet object.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/add_pheno-methods.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Method to add a PhenotypeSet to a MultiDataSet object. — add_pheno","text":"","code":"add_pheno(object, pheno_set, warnings = TRUE, ...)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/add_pheno-methods.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Method to add a PhenotypeSet to a MultiDataSet object. — add_pheno","text":"object MultiDataSet::MultiDataSet object. pheno_set PhenotypeSet object. warnings Logical, warnings displayed? Default TRUE. ... arguments passed MultiDataSet::add_eset() function.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/add_pheno-methods.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Method to add a PhenotypeSet to a MultiDataSet object. — add_pheno","text":"new MultiDataSet::MultiDataSet object slot filled.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/add_samples_metadata.html","id":null,"dir":"Reference","previous_headings":"","what":"Adding data-frame to samples metadata — add_samples_metadata","title":"Adding data-frame to samples metadata — add_samples_metadata","text":"Adds information data-frame samples metadata MultiDataSet object.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/add_samples_metadata.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Adding data-frame to samples metadata — add_samples_metadata","text":"","code":"add_samples_metadata(mo_data, df, datasets = NULL)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/add_samples_metadata.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Adding data-frame to samples metadata — add_samples_metadata","text":"mo_data MultiDataSet::MultiDataSet object. df tibble data-frame samples information, least column id (giving sample IDs), one row per sample ID. datasets Character vector, name datasets samples information added. NULL (default value), information added samples metadata datasets.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/add_samples_metadata.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Adding data-frame to samples metadata — add_samples_metadata","text":"MultiDataSet object, info df added corresponding samples metadata.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/check_feature_sets.html","id":null,"dir":"Reference","previous_headings":"","what":"Checks features assignment to sets — check_feature_sets","title":"Checks features assignment to sets — check_feature_sets","text":"Checks proportion features multi-omics dataset assigned feature sets.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/check_feature_sets.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Checks features assignment to sets — check_feature_sets","text":"","code":"check_feature_sets(feature_sets, mo_data, datasets = names(mo_data))"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/check_feature_sets.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Checks features assignment to sets — check_feature_sets","text":"feature_sets Named list, element corresponds feature set, contains vector features ID features belonging set. mo_data MultiDataSet-class object. datasets Character vector, names datasets features assignment checked. default, datasets mo_data considered.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/check_feature_sets.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Checks features assignment to sets — check_feature_sets","text":"tibble giving dataset number fraction features assigned least one feature set. message column meant facilitate reporting.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/check_input_multidataset.html","id":null,"dir":"Reference","previous_headings":"","what":"Check a MultiDataSet input — check_input_multidataset","title":"Check a MultiDataSet input — check_input_multidataset","text":"Checks MultiDataSet object provided input. particular, checks input object MultiDataSet object, 2) datasets stored match datasets named provided (). restrict MultiDataSet object necessary datasets.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/check_input_multidataset.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check a MultiDataSet input — check_input_multidataset","text":"","code":"check_input_multidataset(x, datasets = NULL)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/check_input_multidataset.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check a MultiDataSet input — check_input_multidataset","text":"x input object hopefully  MultiDataSet object. datasets Character vector dataset names x. NULL (default value), dataset names checked","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/check_input_multidataset.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check a MultiDataSet input — check_input_multidataset","text":"MultiDataSet object restricted datasets required (datasets NULL.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/check_is_multidataset.html","id":null,"dir":"Reference","previous_headings":"","what":"Checks whether object is MultiDataSet — check_is_multidataset","title":"Checks whether object is MultiDataSet — check_is_multidataset","text":"Checks whether input object MultiDataSet::MultiDataSet object.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/check_is_multidataset.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Checks whether object is MultiDataSet — check_is_multidataset","text":"","code":"check_is_multidataset(x)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/check_is_multidataset.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Checks whether object is MultiDataSet — check_is_multidataset","text":"x object.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/check_is_multidataset.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Checks whether object is MultiDataSet — check_is_multidataset","text":"nothing.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/check_missing_values.html","id":null,"dir":"Reference","previous_headings":"","what":"Check for missing values in MultiDataSet — check_missing_values","title":"Check for missing values in MultiDataSet — check_missing_values","text":"Checks missing values omics dataset MultiDataSet object.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/check_missing_values.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check for missing values in MultiDataSet — check_missing_values","text":"","code":"check_missing_values(mo_data)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/check_missing_values.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check for missing values in MultiDataSet — check_missing_values","text":"mo_data MultiDataSet::MultiDataSet object.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/check_missing_values.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check for missing values in MultiDataSet — check_missing_values","text":"Invisible logical vector indicating whether missing values present dataset.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/comparison_heatmap_corr.html","id":null,"dir":"Reference","previous_headings":"","what":"Heatmap of correlation between latent dimensions — comparison_heatmap_corr","title":"Heatmap of correlation between latent dimensions — comparison_heatmap_corr","text":"Constructs heatmap displaying correlation latent dimensions constructed several integration methods. lower triangle heatmap displays correlation features weight, upper triangle shows correlation samples score. triangle matrix reordered separately show highly correlated dimensions next .","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/comparison_heatmap_corr.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Heatmap of correlation between latent dimensions — comparison_heatmap_corr","text":"","code":"comparison_heatmap_corr(   output_list,   latent_dimensions = NULL,   include_missing_features = FALSE,   legend_ncol = 1 )"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/comparison_heatmap_corr.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Heatmap of correlation between latent dimensions — comparison_heatmap_corr","text":"output_list List integration methods output generated via get_output() function. named, names used annotate plot. See details. latent_dimensions Named list, element character vector giving latent dimensions retain corresponding element output_list. Names must match output_list. Can used filter latent dimensions certain elements output_list (see examples). NULL (default value), latent dimensions used. include_missing_features Logical, see get_features_weight_correlation() details. Default value FALSE. legend_ncol Integer, number columns legend. Default value 1.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/comparison_heatmap_corr.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Heatmap of correlation between latent dimensions — comparison_heatmap_corr","text":"ComplexHeatmap::Heatmap.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/comparison_heatmap_corr.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Heatmap of correlation between latent dimensions — comparison_heatmap_corr","text":"output_list unnamed, different elements list differentiated name method used produce (e.g. DIABLO, sO2PLS, etc). order compare different results integration method (e.g. DIABLO applied full vs pre-filtered data), possible assign names elements output_list (see examples). names used place method name plot identify latent dimensions come .","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/comparison_heatmap_corr.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Heatmap of correlation between latent dimensions — comparison_heatmap_corr","text":"","code":"if (FALSE) { ## Comparing the output from DIABLO, sO2PLS and MOFA  res <- list(   get_output_diablo(diablo_res), ## diablo_res: output from diablo_run()   get_output_so2pls(so2pls_res), ## so2pls_res: output from so2pls_o2m()   get_output_mofa2(mofa_res) ## mofa_res: output from run_mofa )  comparison_heatmap_corr(res)  ## Selecting only some factors from a MOFA run for the comparison ## (for the other methods, all latent dimensions will be retained) comparison_heatmap_corr(   res,   latent_dimensions = list(     \"MOFA\" = paste0(\"Factor \", 1:3)   ) )  ## Comparing two different results from a same integration method - ## diablo_run_full and diablo_run_prefiltered would both be output ## from the diablo_run() function.  res <- list(   \"DIABLO full\" = get_output_diablo(diablo_run_full),   \"DIABLO prefiltered\" = get_output_diablo(diablo_run_prefiltered) )  comparison_heatmap_corr(res) }"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/comparison_plot_correlation.html","id":null,"dir":"Reference","previous_headings":"","what":"Correlation plot between latent components — comparison_plot_correlation","title":"Correlation plot between latent components — comparison_plot_correlation","text":"Plots correlation either samples score features weight latent components obtained two different integration methods.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/comparison_plot_correlation.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Correlation plot between latent components — comparison_plot_correlation","text":"","code":"comparison_plot_correlation(   output_list,   by = \"both\",   latent_dimensions = NULL,   include_missing_features = FALSE,   show_cor = TRUE,   min_show_cor = 0.2,   round_cor = 2 )"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/comparison_plot_correlation.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Correlation plot between latent components — comparison_plot_correlation","text":"output_list List length 2 integration methods output, generated via get_output() function. named, names used annotate plot. See details. Character, correlation calculated based samples score ( = 'samples') features weight (= 'features'), (= '', .e. two matrices plotted). Default value ''. latent_dimensions Named list, element character vector giving latent dimensions retain corresponding element output_list. Names must match output_list. Can used filter latent dimensions certain elements output_list (see examples). NULL (default value), latent dimensions used. include_missing_features Logical, see get_features_weight_correlation details. Default value FALSE. show_cor Logical, correlation values added plot? Default value TRUE. min_show_cor Numeric, minimum value correlation coefficients values added plot (.e. circle appear values text). Ignored show_cor FALSE. Default value 0.2. round_cor Integer, many decimal places show correlation coefficients. Ignored show_cor FALSE. Default value 2.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/comparison_plot_correlation.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Correlation plot between latent components — comparison_plot_correlation","text":"ggplot.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/comparison_plot_correlation.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Correlation plot between latent components — comparison_plot_correlation","text":"output_list unnamed, different elements list differentiated name method used produce (e.g. DIABLO, sO2PLS, etc). order compare different results integration method (e.g. DIABLO applied full vs pre-filtered data), possible assign names elements output_list. names used place method name plot identify latent dimensions come .","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/compute_consensus_importance.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes consensus feature importance — compute_consensus_importance","title":"Computes consensus feature importance — compute_consensus_importance","text":"Computes consensus feature importance features weight obtained different integration methods (considering features importance one latent component per integration method), different latent dimensions constructed integration method.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/compute_consensus_importance.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes consensus feature importance — compute_consensus_importance","text":"","code":"compute_consensus_importance(   output_list,   latent_dimensions,   metric = \"geometric\",   include_missing_features = FALSE )"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/compute_consensus_importance.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes consensus feature importance — compute_consensus_importance","text":"output_list List integration methods output, generated via get_output() function, single integration method output (get_output()). latent_dimensions Named list (output_list list), element character giving latent dimension retain corresponding element output_list (1 value). output_list single output object, needs instead character vector giving latent dimensions retain. metric Character, one metrics use compute consensus score. Can one 'min', 'max', 'average', 'product', 'l2' (L2-norm), 'geometric' (geometric mean) 'harmonic' (harmonic mean). Default value 'geometric'. Names must match output_list. include_missing_features Logical, whether features missing output included calculation (see Details). Default value FALSE.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/compute_consensus_importance.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes consensus feature importance — compute_consensus_importance","text":"tibble giving consensus importance feature.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/compute_consensus_importance.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Computes consensus feature importance — compute_consensus_importance","text":"include_missing_features FALSE (default behaviour), features present output one integration method (e.g. different pre-filtering applied input data two methods), features ignored. mean features selected one method discarded; case feature assigned weight 0 method select . recommended behaviour, changed specific scenarios (e.g. check whether using features dataset vs variance-based preselection affect features deemed important). include_missing_features TRUE, missing features assigned weight 0. Note geometric harmonic means work strictly positive values. Therefore, importance scores 0 replaced offset computing metrics. offset calculated per dataset, corresponds minimum non-null importance score observed across features dataset (across latent dimensions), divided 2. calculation offset done removing missing features (include_missing_features = FALSE) results consistent two options include_missing_features.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/compute_samples_silhouette.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes samples silhouette score from method output — compute_samples_silhouette","title":"Computes samples silhouette score from method output — compute_samples_silhouette","text":"Calculates samples silhouette width results dimension reduction method, according samples grouping samples metadata MultiDataSet object.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/compute_samples_silhouette.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes samples silhouette score from method output — compute_samples_silhouette","text":"","code":"compute_samples_silhouette(   method_output,   mo_data,   group_col,   latent_dimensions = NULL,   distance_metric = c(\"euclidean\", \"maximum\", \"manhattan\", \"canberra\", \"binary\",     \"minkowski\") )"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/compute_samples_silhouette.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes samples silhouette score from method output — compute_samples_silhouette","text":"method_output method_output Integration method output generated via get_output() function. mo_data MultiDataSet-class object. group_col Character, name column one samples metadata table mo_data containing samples grouping used. latent_dimensions Character vector, latent dimensions use computing distance samples. NULL (default value), latent dimensions used. distance_metric Character, name metric use computing distance samples coordinates latent dimensions. passed stats::dist() function. Options include: \"euclidean\" (default value), \"maximum\", \"manhattan\", \"canberra\", \"binary\" \"minkowski\".","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/compute_samples_silhouette.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes samples silhouette score from method output — compute_samples_silhouette","text":"list two elements: samples_silhouette: tibble giving sample (sample_id column) group belongs (group column), closest () group space spanned latent dimensions (neighbour_group), silhouette width (silhouette_width column). groups_average_silhouette: tibble giving samples group (group column) average silhouette width (group_average_width column).","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/compute_samples_silhouette.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Computes samples silhouette score from method output — compute_samples_silhouette","text":"samples silhouette width groups average width calculated using cluster::silhouette() function.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/consensus_importance_metric.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate features importance score — consensus_importance_metric","title":"Calculate features importance score — consensus_importance_metric","text":"Given vector features importance (1 0), returns consensus importance score.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/consensus_importance_metric.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate features importance score — consensus_importance_metric","text":"","code":"consensus_importance_metric(x, metric, offset = 0)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/consensus_importance_metric.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate features importance score — consensus_importance_metric","text":"x Numeric vector importance values (0 1). metric Character, one metrics use compute consensus score. Can one 'min', 'max', 'average', 'product', 'l2' (L2-norm), 'geometric' (geometric mean) 'harmonic' (harmonic mean). offset Numeric (strictly positive), used replace zero values compute geometric harmonic mean. 0 (default value), zero values ignored calculating geometric harmonic mean. Accepting vector values facilitate use whithin dplyr::summarise().","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/consensus_importance_metric.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate features importance score — consensus_importance_metric","text":"numeric value, importance consensus score.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/create_multiomics_set.html","id":null,"dir":"Reference","previous_headings":"","what":"Create a MultiDataSet object to store multi-omics data — create_multiomics_set","title":"Create a MultiDataSet object to store multi-omics data — create_multiomics_set","text":"Creates MultiDataSet object list Biobase Set objects store different omics sets.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/create_multiomics_set.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create a MultiDataSet object to store multi-omics data — create_multiomics_set","text":"","code":"create_multiomics_set(sets_list, datasets_names = NULL, show_warnings = TRUE)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/create_multiomics_set.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create a MultiDataSet object to store multi-omics data — create_multiomics_set","text":"sets_list List Biobase::eSet objects, created via create_omics_set(). Currently accepted objects: Biobase::SnpSet, Biobase::ExpressionSet, MetabolomeSet, PhenotypeSet. datasets_names Optional, vector character, name Set object. appended data type resulting object. sets_list list contains several objects data type (e.g. several SnpSets), names must unique. \"\" provided, name appended data type corresponding dataset. show_warnings Logical, warnings displayed adding set MultiDataSet object? Default value TRUE.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/create_multiomics_set.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create a MultiDataSet object to store multi-omics data — create_multiomics_set","text":"MultiDataSet::MultiDataSet object.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/create_multiomics_set.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create a MultiDataSet object to store multi-omics data — create_multiomics_set","text":"","code":"if (FALSE) { ## set_geno, set_transcripto and set_metabo are all Set objects ## Generating a MultiDataSet object with standard name create_multiomics_set(   list(set_geno, set_transcripto, set_metabo) )  ## Adding custom names for genomics and metabolomics datasets ## but not for the transcriptomics dataset create_multiomics_set(   list(set_geno, set_transcripto, set_metabo),   datasets_names = c(\"genome1\", \"\", \"lcms\") ) }"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/create_omics_set.html","id":null,"dir":"Reference","previous_headings":"","what":"Create a Biobase set object to store omics data — create_omics_set","title":"Create a Biobase set object to store omics data — create_omics_set","text":"Creates Biobase object store omics dataset associated samples features metadata.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/create_omics_set.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create a Biobase set object to store omics data — create_omics_set","text":"","code":"create_omics_set(   dataset,   omics_type = c(\"phenomics\", \"genomics\", \"transcriptomics\", \"metabolomics\"),   features_metadata = NULL,   samples_metadata = NULL )"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/create_omics_set.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create a Biobase set object to store omics data — create_omics_set","text":"dataset Matrix, omics dataset matrix form features rows samples columns. omics_type Character, type omics data stored? Possible values 'genomics', 'transcriptomics', 'metabolomics' 'phenomics'. Use 'phenomics' omics. features_metadata Data.frame, feature annotation data-frame features rows information features columns. number rows row names must match dataset matrix. samples_metadata Data.frame, samples information data-frame samples rows information samples columns. number rows row names must match number columns column names dataset matrix.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/create_omics_set.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create a Biobase set object to store omics data — create_omics_set","text":"object derived Biobase::eSet: omics_type = 'genomics': Biobase::SnpSet object; omics_type = 'transcriptomics': Biobase::ExpressionSet object. omics_type = 'metabolomics': MetabolomeSet object. omics_type = 'phenomics' PhenotypeSet object.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/create_omics_set.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create a Biobase set object to store omics data — create_omics_set","text":"","code":"if (FALSE) { data_geno <- import_dataset_csv( \"genotype_dataset.csv\",   col_id = \"Marker\",   features_as_rows = TRUE ) geno_info_features <- import_fmetadata_csv( \"genotype_features_info.csv\",   col_id = \"Marker\" ) samples_information <- import_smetadata_csv( \"samples_information.csv\",   col_id = \"Sample\" ) create_omics_set(   dataset = data_geno,   omics_type = \"genomics\",   features_metadata = geno_info_features,   samples_metadata = samples_information ) }"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/create_omics_set_factory.html","id":null,"dir":"Reference","previous_headings":"","what":"Target factory for omics sets creation — create_omics_set_factory","title":"Target factory for omics sets creation — create_omics_set_factory","text":"Creates list targets generate omics sets targets containing datasets, features samples metadata.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/create_omics_set_factory.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Target factory for omics sets creation — create_omics_set_factory","text":"","code":"create_omics_set_factory(   datasets,   omics_types,   features_metadatas = NULL,   samples_metadatas = NULL,   target_name_suffixes = NULL )"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/create_omics_set_factory.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Target factory for omics sets creation — create_omics_set_factory","text":"datasets Vector symbols, names targets containing omics datasets. omics_types Character vector, type omics data stored dataset? Possible values 'genomics', 'transcriptomics', 'metabolomics' 'phenomics'.  Use 'phenomics' omics. Use 'phenomics' omics. features_metadatas Vector symbols, names targets containing features metadata data-frame associated omics dataset. Use NULL feature metadata exists dataset. samples_metadatas Vector symbols, names targets containing samples metadata data-frame associated omics dataset. Use NULL samples metadata exists dataset. target_name_suffixes Character vector, suffix add name targets created target factory dataset. none provided, suffixes extracted datasets argument. Default value NULL.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/create_omics_set_factory.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Target factory for omics sets creation — create_omics_set_factory","text":"list target objects, three datasets provided, target_name_suffixes = c(\"geno\", \"transcripto\", \"metabo\"), following targets returned: set_geno, set_transcripto set_metabo.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/create_omics_set_factory.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Target factory for omics sets creation — create_omics_set_factory","text":"","code":"if (FALSE) { ## in the _targets.R library(moiraine) library(targets)  list(   ## targets to import the different datasets    ## Example where genomics dataset has no features metadata information   ## Will generate the following targets: set_geno, set_transcripto   create_omics_set_factory(     datasets = c(data_geno, data_transcripto),     omics_types = c(\"genomics\", \"transcriptomics\"),     features_metadata = c(NULL, fmeta_transcripto),     samples_metadata = c(smeta_geno, smeta_transcripto)   ) ) }"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/create_report.html","id":null,"dir":"Reference","previous_headings":"","what":"Creates Rmd report from template — create_report","title":"Creates Rmd report from template — create_report","text":"Creates Rmarkdown report present results integration analysis. function creates .Rmd file knit document.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/create_report.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Creates Rmd report from template — create_report","text":"","code":"create_report(   file,   add_sections = c(\"spls\", \"so2pls\", \"mofa\", \"diablo\", \"comparison\"),   overwrite = FALSE,   target_project = Sys.getenv(\"TAR_PROJECT\", \"main\"),   use_quarto = FALSE )"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/create_report.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Creates Rmd report from template — create_report","text":"file Name (path) file created. end .Rmd use_quarto FALSE, .qmd use_quarto TRUE. add_sections Character vector, names sections include report. Possible values 'spls', 'so2pls', 'mofa', 'diablo' 'comparison'. default, sections included. overwrite Logical, existing file overwritten? target_project Character, name current targets project (.e. value used TAR_PROJECT environment variable). none provided, read TAR_PROJECT environment variable set \"main\" former set. use_quarto Boolean, whether use Quarto report. Default value FALSE, .e. uses Rmarkdown report.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/create_report.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Creates Rmd report from template — create_report","text":"Invisible character, path name generated .Rmd (.qmd) file.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/create_targets_pipeline.html","id":null,"dir":"Reference","previous_headings":"","what":"Creates a target script file from template — create_targets_pipeline","title":"Creates a target script file from template — create_targets_pipeline","text":"Creates target script file form template multi-omics integration pipeline. function creates script file execute .","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/create_targets_pipeline.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Creates a target script file from template — create_targets_pipeline","text":"","code":"create_targets_pipeline(file = \"_targets.R\", overwrite = FALSE)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/create_targets_pipeline.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Creates a target script file from template — create_targets_pipeline","text":"file Name (path) file created. end .R. Default value (recommended) \"_targets.R\" (current directory). overwrite Logical, existing file overwritten?","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/create_targets_pipeline.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Creates a target script file from template — create_targets_pipeline","text":"file name (invisibly).","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/diablo_generate_design_matrix.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate DIABLO design matrix — diablo_generate_design_matrix","title":"Generate DIABLO design matrix — diablo_generate_design_matrix","text":"Generates design matrix DIABLO algorithm, based correlation datasets inferred pairwise PLS runs.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/diablo_generate_design_matrix.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate DIABLO design matrix — diablo_generate_design_matrix","text":"","code":"diablo_generate_design_matrix(   cormat,   threshold = 0.8,   low_val = 0.1,   high_val = 1,   y_val = 1 )"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/diablo_generate_design_matrix.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate DIABLO design matrix — diablo_generate_design_matrix","text":"cormat correlation matrix datasets, obtained diablo_get_pairwise_pls_corr. threshold Numeric, correlation value datasets considered highly correlated (see Details). Default value 0.8. low_val Numeric, value design matrix datasets highly correlated. Default value 0.1. high_val Numeric, value design matrix datasets highly correlated. Default value 1. y_val Numeric, value design matrix datasets outcome (Y). Default value 1.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/diablo_generate_design_matrix.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generate DIABLO design matrix — diablo_generate_design_matrix","text":"numeric matrix, used design matrix running block.plsda, one row per dataset one column per dataset.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/diablo_generate_design_matrix.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Generate DIABLO design matrix — diablo_generate_design_matrix","text":"Use threshold detect pairs datasets highly correlated. pairs datasets, corresponding cell design matrix set high_val. pairs datasets correlation threshold, corresponding cell design matrix set low_val.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/diablo_get_optim_ncomp.html","id":null,"dir":"Reference","previous_headings":"","what":"Get the optimal ncomp value — diablo_get_optim_ncomp","title":"Get the optimal ncomp value — diablo_get_optim_ncomp","text":"Selects optimal comp value (number components compute) DIABLO cross-validation run, given error measure distance metric.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/diablo_get_optim_ncomp.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get the optimal ncomp value — diablo_get_optim_ncomp","text":"","code":"diablo_get_optim_ncomp(   perf_res,   measure = \"Overall.BER\",   distance = \"centroids.dist\",   min_ncomp = 1 )"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/diablo_get_optim_ncomp.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get the optimal ncomp value — diablo_get_optim_ncomp","text":"perf_res cross-validation results, computed perf. measure error measure obtain optimal value; possible values 'Overall.ER' 'Overall.BER'. Default value 'Overall.BER'. distance distance metric obtain optimal value; possible values 'max.dist', 'centroids.dist' 'mahalanobis.dist'. Default value 'centroids.dist'. min_ncomp Integer, minimum ncomp value returned. Default value 1, .e. argument play role selecting comp value. Can useful want least 2 latent components final plots.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/diablo_get_optim_ncomp.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get the optimal ncomp value — diablo_get_optim_ncomp","text":"integer, optimal value ncomp use DIABLO run.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/diablo_get_pairwise_pls_corr.html","id":null,"dir":"Reference","previous_headings":"","what":"Get pairwise correlations from PLS run — diablo_get_pairwise_pls_corr","title":"Get pairwise correlations from PLS run — diablo_get_pairwise_pls_corr","text":"Computes correlation matrix datasets based first component PLS run.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/diablo_get_pairwise_pls_corr.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get pairwise correlations from PLS run — diablo_get_pairwise_pls_corr","text":"","code":"diablo_get_pairwise_pls_corr(pairwise_pls_result)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/diablo_get_pairwise_pls_corr.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get pairwise correlations from PLS run — diablo_get_pairwise_pls_corr","text":"pairwise_pls_result List containing results pairwise PLS runs, computed run_pairwise_pls function.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/diablo_get_pairwise_pls_corr.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get pairwise correlations from PLS run — diablo_get_pairwise_pls_corr","text":"matrix correlation coefficients datasets.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/diablo_get_pairwise_pls_corr.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Get pairwise correlations from PLS run — diablo_get_pairwise_pls_corr","text":"correlation coefficient two datasets computed correlation coefficient first component dataset obtained Projection Latent Structure (PLS) run, mixOmics package.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/diablo_get_pairwise_pls_corr.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Get pairwise correlations from PLS run — diablo_get_pairwise_pls_corr","text":"","code":"if (FALSE) { ## get mixomics input from MultiDataSet object mixomics_data <- get_input_mixomics_supervised(mo_set, \"outcome_group\")  ## Get the list of dataset names datasets <- setdiff(names(mixomics_data), \"Y\")  ## Get all possible pairwise combinations of dataset names ds_pairs <- utils::combn(datasets, 2)  ## run PLS for each pair of datasets pls_res_list <- lapply(1:ncol(ds_pairs), function(i) {   run_pairwise_pls(mo_set, ds_pairs[, i]) })  ## extract the pairwise correlation matrix diablo_get_pairwise_pls_corr(pls_res_list) }"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/diablo_get_params.html","id":null,"dir":"Reference","previous_headings":"","what":"Get parameters from DIABLO run — diablo_get_params","title":"Get parameters from DIABLO run — diablo_get_params","text":"Extracts ncomp keepX parameters DIABLO run format table.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/diablo_get_params.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get parameters from DIABLO run — diablo_get_params","text":"","code":"diablo_get_params(diablo_res)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/diablo_get_params.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get parameters from DIABLO run — diablo_get_params","text":"diablo_res output block.splsda diablo_run.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/diablo_get_params.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get parameters from DIABLO run — diablo_get_params","text":"tibble.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/diablo_get_wa_coord.html","id":null,"dir":"Reference","previous_headings":"","what":"Get weighted average coordinates — diablo_get_wa_coord","title":"Get weighted average coordinates — diablo_get_wa_coord","text":"Computes samples coordinates weighted average latent components space DIABLO result object.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/diablo_get_wa_coord.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get weighted average coordinates — diablo_get_wa_coord","text":"","code":"diablo_get_wa_coord(diablo_res)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/diablo_get_wa_coord.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get weighted average coordinates — diablo_get_wa_coord","text":"diablo_res output block.splsda diablo_run.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/diablo_get_wa_coord.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get weighted average coordinates — diablo_get_wa_coord","text":"matrix one row per sample one column per latent component.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/diablo_pairwise_pls_factory.html","id":null,"dir":"Reference","previous_headings":"","what":"Target factory for pairwise PLS and design matrix estimation for DIABLO run — diablo_pairwise_pls_factory","title":"Target factory for pairwise PLS and design matrix estimation for DIABLO run — diablo_pairwise_pls_factory","text":"Creates list targets perform PLS run pair datasets, uses results assess correlation datasets create design matrix DIABLO algorithm.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/diablo_pairwise_pls_factory.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Target factory for pairwise PLS and design matrix estimation for DIABLO run — diablo_pairwise_pls_factory","text":"","code":"diablo_pairwise_pls_factory(   mixomics_data,   ...,   threshold = 0.8,   low_val = 0.1,   high_val = 1,   y_val = 1,   target_name_prefix = \"\" )"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/diablo_pairwise_pls_factory.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Target factory for pairwise PLS and design matrix estimation for DIABLO run — diablo_pairwise_pls_factory","text":"mixomics_data mixOmics input object created get_input_mixomics_supervised. ... Additional parameters passed run_pairwise_pls function. threshold Numeric, correlation value datasets considered highly correlated (see Details). Default value 0.8. low_val Numeric, value design matrix datasets highly correlated. Default value 0.1. high_val Numeric, value design matrix datasets highly correlated. Default value 1. y_val Numeric, value design matrix datasets outcome (Y). Default value 1. target_name_prefix Character, prefix add name targets created factory. Default value \"\".","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/diablo_pairwise_pls_factory.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Target factory for pairwise PLS and design matrix estimation for DIABLO run — diablo_pairwise_pls_factory","text":"list targets. example, target_name_prefix = \"\", following targets created: diablo_pairs_datasets: target generates list possible pairs dataset names. diablo_pls_runs_list: dynamic branching target runs PLS algorithm possible pair datasets. target returns list PLS results pair datasets. diablo_pls_correlation_matrix: target computes PLS results list correlation matrix datasets. diablo_design_matrix: target constructs datasets correlation matrix design matrix use DIABLO algorithm.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/diablo_pairwise_pls_factory.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Target factory for pairwise PLS and design matrix estimation for DIABLO run — diablo_pairwise_pls_factory","text":"","code":"if (FALSE) { ## in the _targets.R file library(moiraine)  list(   ## code to import the datasets, etc   ## mo_set is the target containing the MultiDataSet object   tar_target(     mixomics_input,     get_input_mixomics_supervised(mo_set, \"outcome_group\")   ),   diablo_pairwise_pls_factory(mixomics_input) ) }"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/diablo_plot.html","id":null,"dir":"Reference","previous_headings":"","what":"Plots DIABLO output — diablo_plot","title":"Plots DIABLO output — diablo_plot","text":"Displays samples coordinates given latent component across datasets. copy plotDiablo function, difference margins increased accomodate title.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/diablo_plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plots DIABLO output — diablo_plot","text":"","code":"diablo_plot(   diablo_res,   ncomp = 1,   legend = TRUE,   legend.ncol,   col.per.group = NULL )"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/diablo_plot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plots DIABLO output — diablo_plot","text":"diablo_res output block.splsda diablo_run. ncomp Integer, latent component plot. legend Logical, legend added plot? Default value TRUE. legend.ncol Integer, number columns legend. none specified, calculated min(5, nlevels(diablo_res$Y)). col.per.group Named character vector, provides colours use phenotypic group. Names must match levels diablo_res$Y.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/diablo_plot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plots DIABLO output — diablo_plot","text":"None.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/diablo_plot_circos.html","id":null,"dir":"Reference","previous_headings":"","what":"Plots DIABLO circos plot — diablo_plot_circos","title":"Plots DIABLO circos plot — diablo_plot_circos","text":"Displays DIABLO circos plot, uses available feature metadata display feature names.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/diablo_plot_circos.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plots DIABLO circos plot — diablo_plot_circos","text":"","code":"diablo_plot_circos(diablo_res, mo_data, label_cols, truncate = NULL, ...)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/diablo_plot_circos.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plots DIABLO circos plot — diablo_plot_circos","text":"diablo_res output block.splsda diablo_run. mo_data MultiDataSet::MultiDataSet object. label_cols Character named list character, giving dataset name column corresponding features metadata use label. one value, used datasets. list, names must correspond names datasets mo_data. Default value feature_id, .e. default ID features used label. truncate Integer, width labels truncated (avoid issues long labels plots). NULL (default value), truncation performed. ... Additional arguments passed circosPlot.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/diablo_plot_perf.html","id":null,"dir":"Reference","previous_headings":"","what":"Plots DIABLO perf results — diablo_plot_perf","title":"Plots DIABLO perf results — diablo_plot_perf","text":"Displays error rate DIABLO run cross-validation estimate optimal number components (ncomp)","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/diablo_plot_perf.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plots DIABLO perf results — diablo_plot_perf","text":"","code":"diablo_plot_perf(perf_res)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/diablo_plot_perf.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plots DIABLO perf results — diablo_plot_perf","text":"perf_res cross-validation results, computed perf.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/diablo_plot_perf.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plots DIABLO perf results — diablo_plot_perf","text":"ggplot2 object.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/diablo_plot_tune.html","id":null,"dir":"Reference","previous_headings":"","what":"Plots DIABLO tune results — diablo_plot_tune","title":"Plots DIABLO tune results — diablo_plot_tune","text":"Displays error rate DIABLO run cross-validation estimate optimal number features retain dataset (keepX).","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/diablo_plot_tune.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plots DIABLO tune results — diablo_plot_tune","text":"","code":"diablo_plot_tune(tune_res)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/diablo_plot_tune.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plots DIABLO tune results — diablo_plot_tune","text":"tune_res cross-validation results, computed diablo_tune.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/diablo_plot_tune.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plots DIABLO tune results — diablo_plot_tune","text":"ggplot2 object.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/diablo_plot_var.html","id":null,"dir":"Reference","previous_headings":"","what":"Plots DIABLO features correlation circle — diablo_plot_var","title":"Plots DIABLO features correlation circle — diablo_plot_var","text":"Displays DIABLO correlation circle plot, uses available feature metadata display feature names.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/diablo_plot_var.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plots DIABLO features correlation circle — diablo_plot_var","text":"","code":"diablo_plot_var(   diablo_res,   mo_data,   label_cols = \"feature_id\",   truncate = NULL,   ... )"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/diablo_plot_var.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plots DIABLO features correlation circle — diablo_plot_var","text":"diablo_res output block.splsda diablo_run. mo_data MultiDataSet::MultiDataSet object. label_cols Character named list character, giving dataset name column corresponding features metadata use label. one value, used datasets. list, names must correspond names datasets mo_data. Default value feature_id, .e. default ID features used label. truncate Integer, width labels truncated (avoid issues long labels plots). NULL (default value), truncation performed. ... Additional arguments passed plotVar.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/diablo_plot_var.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plots DIABLO features correlation circle — diablo_plot_var","text":"plot (see plotVar).","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/diablo_plot_var.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plots DIABLO features correlation circle — diablo_plot_var","text":"","code":"if (FALSE) { # Use the default features ID for the plot diablo_plot_var(   diablo_final_run,   mo_data,   \"feature_id\",   overlap = FALSE,   cex = rep(2, 3),   comp = 1:2 )  # Using a different column from the feature metadata of each omics dataset diablo_plot_var(   diablo_final_run,   mo_presel_supervised,   c(     \"snps\" = \"feature_id\",     \"rnaseq\" = \"gene_name\",     \"metabolome\" = \"comp_name\"   ),   overlap = FALSE,   cex = rep(2, 3),   comp = 1:2 ) }"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/diablo_predefined_design_matrix.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate a design matrix for DIABLO — diablo_predefined_design_matrix","title":"Generate a design matrix for DIABLO — diablo_predefined_design_matrix","text":"Generates design matrix DIABLO, following predesigned pattern recommended mixOmics authors.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/diablo_predefined_design_matrix.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate a design matrix for DIABLO — diablo_predefined_design_matrix","text":"","code":"diablo_predefined_design_matrix(   datasets_name,   design_matrix = c(\"null\", \"weighted_full\", \"full\") )"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/diablo_predefined_design_matrix.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate a design matrix for DIABLO — diablo_predefined_design_matrix","text":"datasets_name Character vector, names datasets integrate. include value \"Y\" represent samples outcome groups. design_matrix Character, type design matrix generate. one \"null\", \"weighted_full\" \"full\".","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/diablo_predefined_design_matrix.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generate a design matrix for DIABLO — diablo_predefined_design_matrix","text":"matrix many rows columns length datasets_name, filled either 0 (design_matrix = \"null\"), 0.1 (design_matrix = \"weighted_full\") 1 (design_matrix = \"full\"). Values diagonal set 0, values \"Y\" row columns set 1.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/diablo_run.html","id":null,"dir":"Reference","previous_headings":"","what":"Runs DIABLO algorithm — diablo_run","title":"Runs DIABLO algorithm — diablo_run","text":"Runs DIABLO algorithm (block.splsda) mixOmics package.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/diablo_run.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Runs DIABLO algorithm — diablo_run","text":"","code":"diablo_run(mixomics_data, design_matrix, ...)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/diablo_run.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Runs DIABLO algorithm — diablo_run","text":"mixomics_data mixOmics input object created get_input_mixomics_supervised. design_matrix Either numeric matrix created diablo_generate_design_matrix, character (accepted values 'null', 'weighted_full', 'full'). See Details. ... Arguments passed block.splsda function.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/diablo_run.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Runs DIABLO algorithm — diablo_run","text":"object class block.splsda (keepX argument provided) block.splsda (), see mixOmics::block.splsda() mixOmics::block.plsda().","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/diablo_run.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Runs DIABLO algorithm — diablo_run","text":"design_matrix argument can either custom design matrix (example constructed via diablo_generate_design_matrix function); character indicating type design matrix generate. Possible values include: 'null': -diagonal elements design matrix set 0; 'weighted_full': -diagonal elements design matrix set 0.1; 'full': -diagonal elements design matrix set 1.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/diablo_table_optim_keepX.html","id":null,"dir":"Reference","previous_headings":"","what":"Formatted table with optimal keepX values — diablo_table_optim_keepX","title":"Formatted table with optimal keepX values — diablo_table_optim_keepX","text":"Produces nicely formatted table optimal number features select dataset DIABLO run according results cross-validation analysis. Used mostly producing nice report.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/diablo_table_optim_keepX.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Formatted table with optimal keepX values — diablo_table_optim_keepX","text":"","code":"diablo_table_optim_keepX(tune_res)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/diablo_table_optim_keepX.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Formatted table with optimal keepX values — diablo_table_optim_keepX","text":"tune_res cross-validation results, computed diablo_tune.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/diablo_table_optim_keepX.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Formatted table with optimal keepX values — diablo_table_optim_keepX","text":"tibble Dataset column, one column latent component Total column giving number features retain corresponding dataset across latent components.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/diablo_tune.html","id":null,"dir":"Reference","previous_headings":"","what":"Tunes keepX arg for DIABLO — diablo_tune","title":"Tunes keepX arg for DIABLO — diablo_tune","text":"Performs cross-validation estimate optimal number features retain dataset DIABLO run.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/diablo_tune.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tunes keepX arg for DIABLO — diablo_tune","text":"","code":"diablo_tune(mixomics_data, design_matrix, keepX_list = NULL, cpus = NULL, ...)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/diablo_tune.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Tunes keepX arg for DIABLO — diablo_tune","text":"mixomics_data mixOmics input object created get_input_mixomics_supervised. design_matrix Either numeric matrix created diablo_generate_design_matrix, character (accepted values 'null', 'weighted_full', 'full'). See Details. keepX_list Named list, gives omics dataset mixOmics input (.e. excluding response Y) vector values test (.e. number features return dataset). NULL (default), standard grid applied dataset latent component, testing values: seq(5, 30, 5). cpus Integer, number CPUs use running code parallel. advanced users, see BPPARAM argument tune.block.splsda. ... Arguments passed tune.block.splsda function.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/diablo_tune.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Tunes keepX arg for DIABLO — diablo_tune","text":"list, see tune.block.splsda.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/diablo_tune.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Tunes keepX arg for DIABLO — diablo_tune","text":"design_matrix argument can either custom design matrix (example constructed via diablo_generate_design_matrix function); character indicating type design matrix generate. Possible values include: 'null': -diagonal elements design matrix set 0; 'weighted_full': -diagonal elements design matrix set 0.1; 'full': -diagonal elements design matrix set 1.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/dot-add_features_labels_toplot.html","id":null,"dir":"Reference","previous_headings":"","what":"Adds features label to data-frame — .add_features_labels_toplot","title":"Adds features label to data-frame — .add_features_labels_toplot","text":"Adds features label data-frame plotting. Can extracted features metadata MultiDataSet object; otherwise use feature IDs label. labels missing, feature IDs used instead.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/dot-add_features_labels_toplot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Adds features label to data-frame — .add_features_labels_toplot","text":"","code":".add_features_labels_toplot(toplot, label_cols, mo_data, truncate = NULL)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/dot-add_features_labels_toplot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Adds features label to data-frame — .add_features_labels_toplot","text":"toplot data-frame labels added. label_cols Character named list character, giving dataset name column corresponding features metadata use label. one value, used datasets. list, names must correspond names datasets mo_data. dataset missing list value provided, feature IDs used labels. Alternatively, use feature_id get feature IDs labels. mo_data MultiDataSet object. used label_cols NULL. truncate Integer, width labels truncated (avoid issues long labels plots). NULL (default value), truncation performed.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/dot-add_features_labels_toplot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Adds features label to data-frame — .add_features_labels_toplot","text":"toplot data-frame additional column label.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/dot-check_input_var_smetadata.html","id":null,"dir":"Reference","previous_headings":"","what":"Check that variable names corresponds to columns in samples metadata — .check_input_var_smetadata","title":"Check that variable names corresponds to columns in samples metadata — .check_input_var_smetadata","text":"Checks whether variable name corresponds column samples metadata corresponding dataset. one value provided, used datasets.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/dot-check_input_var_smetadata.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check that variable names corresponds to columns in samples metadata — .check_input_var_smetadata","text":"","code":".check_input_var_smetadata(x, mo_data)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/dot-check_input_var_smetadata.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check that variable names corresponds to columns in samples metadata — .check_input_var_smetadata","text":"x Named character list, one element per dataset, element giving name column samples metadata corresponding dataset. names correspond dataset names mo_data. checked .make_var_list(). mo_data MultiDataSet object containing samples information datasets. checked check_input_multidataset().","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/dot-check_input_var_smetadata.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check that variable names corresponds to columns in samples metadata — .check_input_var_smetadata","text":"Nothing. throw error need .","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/dot-check_input_var_smetadata_common.html","id":null,"dir":"Reference","previous_headings":"","what":"Check that variable names corresponds to columns in samples metadata — .check_input_var_smetadata_common","title":"Check that variable names corresponds to columns in samples metadata — .check_input_var_smetadata_common","text":"Checks whether variable name corresponds column samples metadata corresponding dataset. one value provided, used datasets.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/dot-check_input_var_smetadata_common.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check that variable names corresponds to columns in samples metadata — .check_input_var_smetadata_common","text":"","code":".check_input_var_smetadata_common(x, mo_data)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/dot-check_input_var_smetadata_common.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check that variable names corresponds to columns in samples metadata — .check_input_var_smetadata_common","text":"x Character, name column samples metadata. mo_data MultiDataSet object containing samples information datasets. checked check_input_multidataset().","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/dot-check_input_var_smetadata_common.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check that variable names corresponds to columns in samples metadata — .check_input_var_smetadata_common","text":"Nothing. throw error need .","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/dot-check_names_output_list.html","id":null,"dir":"Reference","previous_headings":"","what":"Check names of output list — .check_names_output_list","title":"Check names of output list — .check_names_output_list","text":"Checks names list outputs several integration methods unique. named, name method used name.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/dot-check_names_output_list.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check names of output list — .check_names_output_list","text":"","code":".check_names_output_list(output_list)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/dot-check_names_output_list.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check names of output list — .check_names_output_list","text":"output_list List integration methods output, generated via one get_output_*() function.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/dot-check_names_output_list.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check names of output list — .check_names_output_list","text":"output_list (named ).","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/dot-filter_output_datasets.html","id":null,"dir":"Reference","previous_headings":"","what":"Filter datasets — .filter_output_datasets","title":"Filter datasets — .filter_output_datasets","text":"Filters datasets name output integration method.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/dot-filter_output_datasets.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Filter datasets — .filter_output_datasets","text":"","code":".filter_output_datasets(   method_output,   datasets,   fixed_length = NULL,   method_name = attr(method_output, \"method\") )"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/dot-filter_output_datasets.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Filter datasets — .filter_output_datasets","text":"method_output Integration method output generated via get_output() function. datasets Character vector giving datasets retain features weight table method's output. fixed_length Integer, expected length datasets. NULL (default value), length datasets checked. method_name Character, name method use error message.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/dot-filter_output_datasets.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Filter datasets — .filter_output_datasets","text":"Similar method_output, features weight table filtered.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/dot-filter_output_dimensions.html","id":null,"dir":"Reference","previous_headings":"","what":"Filter latent dimensions — .filter_output_dimensions","title":"Filter latent dimensions — .filter_output_dimensions","text":"Filters latent dimensions name output integration method.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/dot-filter_output_dimensions.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Filter latent dimensions — .filter_output_dimensions","text":"","code":".filter_output_dimensions(   method_output,   latent_dimensions,   fixed_length = NULL,   method_name = attr(method_output, \"method\") )"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/dot-filter_output_dimensions.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Filter latent dimensions — .filter_output_dimensions","text":"method_output Integration method output generated via get_output() function. latent_dimensions Character vector giving latent dimensions retain method's output. fixed_length Integer, expected length latent_dimensions. NULL (default value), length latent_dimensions checked. method_name Character, name method use error message.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/dot-filter_output_dimensions.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Filter latent dimensions — .filter_output_dimensions","text":"Similar method_output, samples score table features weight table filtered.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/dot-filter_output_dimensions_list.html","id":null,"dir":"Reference","previous_headings":"","what":"Filter latent dimensions in list — .filter_output_dimensions_list","title":"Filter latent dimensions in list — .filter_output_dimensions_list","text":"Filters latent dimensions name list outputs integration methods.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/dot-filter_output_dimensions_list.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Filter latent dimensions in list — .filter_output_dimensions_list","text":"","code":".filter_output_dimensions_list(   output_list,   latent_dimensions,   all_present = FALSE,   fixed_length = NULL )"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/dot-filter_output_dimensions_list.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Filter latent dimensions in list — .filter_output_dimensions_list","text":"output_list List integration method outputs generated via one get_output() function. latent_dimensions Named list, element character vector giving latent dimensions retain corresponding element output_list. Names must match output_list. all_present Logical, whether one element latent_dimensions element output_list. TRUE, error returned length names output_list latent_dimensions match. Default value FALSE. fixed_length Integer, expected length element latent_dimensions. NULL (default value), length elements latent_dimensions can vary.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/dot-filter_output_dimensions_list.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Filter latent dimensions in list — .filter_output_dimensions_list","text":"list output similar output_list, samples score table features weight table filtered.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/dot-get_initials.html","id":null,"dir":"Reference","previous_headings":"","what":"Get initials from sentence — .get_initials","title":"Get initials from sentence — .get_initials","text":"Extracts initials sentence well number.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/dot-get_initials.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get initials from sentence — .get_initials","text":"","code":".get_initials(x)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/dot-get_initials.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get initials from sentence — .get_initials","text":"x Character vector.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/dot-get_initials.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get initials from sentence — .get_initials","text":"character vector, element containing initials words x upper case plus number, pasted.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/dot-heatmap_features_weight_corr.html","id":null,"dir":"Reference","previous_headings":"","what":"Heatmap of features weight correlation — .heatmap_features_weight_corr","title":"Heatmap of features weight correlation — .heatmap_features_weight_corr","text":"Constructs lower triangle heatmap features weight correlation latent dimensions constructed several integration methods.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/dot-heatmap_features_weight_corr.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Heatmap of features weight correlation — .heatmap_features_weight_corr","text":"","code":".heatmap_features_weight_corr(   output_list,   include_missing_features = FALSE,   legend_ncol = 1 )"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/dot-heatmap_features_weight_corr.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Heatmap of features weight correlation — .heatmap_features_weight_corr","text":"output_list List integration methods output generated via get_output() function. include_missing_features Logical, see get_features_weight_correlation() details. Default value FALSE. legend_ncol Integer, number columns legend. Default value 1.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/dot-heatmap_features_weight_corr.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Heatmap of features weight correlation — .heatmap_features_weight_corr","text":"ComplexHeatmap::Heatmap (lower triangle ).","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/dot-heatmap_samples_score_corr.html","id":null,"dir":"Reference","previous_headings":"","what":"Heatmap of samples score correlation — .heatmap_samples_score_corr","title":"Heatmap of samples score correlation — .heatmap_samples_score_corr","text":"Constructs upper triangle heatmap samples score correlation latent dimensions constructed several integration methods.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/dot-heatmap_samples_score_corr.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Heatmap of samples score correlation — .heatmap_samples_score_corr","text":"","code":".heatmap_samples_score_corr(output_list, hclust_fw)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/dot-heatmap_samples_score_corr.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Heatmap of samples score correlation — .heatmap_samples_score_corr","text":"output_list List integration methods output generated via get_output() function. hclust_fw Dendrogram latent dimensions according features weight correlation (obtained .heatmap_features_weight_corr).","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/dot-heatmap_samples_score_corr.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Heatmap of samples score correlation — .heatmap_samples_score_corr","text":"ComplexHeatmap::Heatmap (upper triangle ).","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/evaluate_feature_selection_table.html","id":null,"dir":"Reference","previous_headings":"","what":"Evaluate feature selection against features label — evaluate_feature_selection_table","title":"Evaluate feature selection against features label — evaluate_feature_selection_table","text":"Compares selection features different feature labels (e.g. result DE analysis) latent dimension.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/evaluate_feature_selection_table.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Evaluate feature selection against features label — evaluate_feature_selection_table","text":"","code":"evaluate_feature_selection_table(   method_output,   mo_data,   col_names,   latent_dimensions = NULL )"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/evaluate_feature_selection_table.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Evaluate feature selection against features label — evaluate_feature_selection_table","text":"method_output Integration method output generated via get_output() function. mo_data MultiDataSet-class object. col_names Named character vector, giving dataset name column features metadata table contains features label. dataset present vector, excluded resulting table. latent_dimensions Character vector, latent dimensions include resulting table. NULL (default value), latent dimensions represented.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/evaluate_feature_selection_table.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Evaluate feature selection against features label — evaluate_feature_selection_table","text":"tibble, dataset latent dimension number selected non-selected features per feature label.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/evaluate_method_enrichment.html","id":null,"dir":"Reference","previous_headings":"","what":"Enrichment analysis for integration results — evaluate_method_enrichment","title":"Enrichment analysis for integration results — evaluate_method_enrichment","text":"Performs enrichment analysis latent dimension integration result, based user-defined feature sets. enrichment analysis done gage::gage() function gage package, using features' signed importance score features metric.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/evaluate_method_enrichment.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Enrichment analysis for integration results — evaluate_method_enrichment","text":"","code":"evaluate_method_enrichment(   method_output,   feature_sets,   datasets = NULL,   latent_dimensions = NULL,   use_abs = TRUE,   rank_test = FALSE,   min_set_size = 5,   add_missing_features = FALSE,   mo_data = NULL,   sets_info_df = NULL,   col_set = NULL )"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/evaluate_method_enrichment.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Enrichment analysis for integration results — evaluate_method_enrichment","text":"method_output Integration method output generated via get_output() function. feature_sets Named list, element corresponds feature set, contains vector features ID features belonging set. datasets Character vector, names datasets consider enrichment analysis. NULL (default value), features datasets included analysis. latent_dimensions Character vector, latent dimensions enrichment analysis performed. NULL (default value), latent dimensions analysed. use_abs Logical, whether use absolute value features metric perform enrichment. TRUE (default value), allows highlight feature sets features high weight/importance score, positive negative. FALSE, instead highlight feature sets weights sign (coordinated change). rank_test Logical, whether non-parametric Wilcoxon Mann-Whitney test used instead default two-sample t-test (.e. based features rank rather metric). Default value FALSE. min_set_size Integer, minimum number features set required order compute enrichment score set. Default value 5. add_missing_features Logical, whether features multi-omics dataset (provided mo_data argument) weight integration results (e.g. selected pre-processing step) added results. TRUE (default value), added importance score 0. mo_data MultiDataSet-class object. add_missing_features true, features multi-omics dataset weight integration method result added importance score 0. sets_info_df Data-frame, information feature sets added enrichment results. NULL (default value), information added results. col_set Character, name column sets_info_df containing set IDs. match names feature_sets list.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/evaluate_method_enrichment.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Enrichment analysis for integration results — evaluate_method_enrichment","text":"tibble enrichment results.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/evaluate_method_enrichment.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Enrichment analysis for integration results — evaluate_method_enrichment","text":"add_missing_features TRUE (default behaviour) MultiDataSet object passed mo_data argument, features present multi-omics dataset absent integration method's results added method's result weight 0. make sure , set 30 features, 25 features removed feature pre-selection stage, enrichment considers 25 features given high weights method. Otherwise, add_missing_features FALSE, 25 features ignored, enrichment analysis may find one latent dimension enriched particular set, even though 5 features 30 set contribute latent dimension. Also note multiple-testing correction applied latent dimension level, correction across latent dimensions. setting use_abs FALSE, latent dimension, enrichment features test tested twice: enrichment features positive weight/importance, features negative weight/importance score. indicated direction column resulting tibble. Note built function using gage vignette RNA-Seq Data Pathway Gene-set Analysis Workflow, section 7.1.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/feature_preselection_cov_factory.html","id":null,"dir":"Reference","previous_headings":"","what":"Target factory for feature preselection based on Coefficient of Variation — feature_preselection_cov_factory","title":"Target factory for feature preselection based on Coefficient of Variation — feature_preselection_cov_factory","text":"Creates list targets perform feature preselection datasets MultiDataSet object retaining features highest Coefficient Variation (COV).","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/feature_preselection_cov_factory.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Target factory for feature preselection based on Coefficient of Variation — feature_preselection_cov_factory","text":"","code":"feature_preselection_cov_factory(   mo_data_target,   to_keep_ns,   to_keep_props = NULL,   with_ties = TRUE,   target_name_prefix = \"\",   filtered_set_target_name = NULL )"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/feature_preselection_cov_factory.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Target factory for feature preselection based on Coefficient of Variation — feature_preselection_cov_factory","text":"mo_data_target Symbol, name target containing MultiDataSet object. to_keep_ns Named integer vector, number feature retain dataset prefiltered (names correspond dataset name). Value less number features corresponding dataset. Set NULL order use to_keep_props instead. to_keep_props Named numeric vector, proportion features retain dataset prefiltered (names correspond dataset name). Value > 0 < 1. ignored to_keep_ns NULL. with_ties ties kept together? TRUE, may return features requested. Default value TRUE. target_name_prefix Character, prefix add name targets created target factory. Default value \"\". filtered_set_target_name Character, name final target containing filtered MultiDataSet object. NULL, name automatically supplied. Default value NULL.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/feature_preselection_cov_factory.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Target factory for feature preselection based on Coefficient of Variation — feature_preselection_cov_factory","text":"list target objects. target_name_prefix = \"\" filtered_set_target_name = NULL, following targets created: cov_spec: target generates grouped tibble row corresponds one dataset filtered, columns specifying dataset name, associated values to_keep_ns, to_keep_props with_ties. cov_mat: dynamic branching target run get_dataset_matrix() function dataset. individual_cov_values: dynamic branching target runs select_features_cov_matrix() function dataset. filtered_set_cov: target retain original MultiDataSet object features selected based COV values.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/feature_preselection_cov_factory.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Target factory for feature preselection based on Coefficient of Variation — feature_preselection_cov_factory","text":"","code":"if (FALSE) { ## in the _targets.R library(moiraine)  list(   ## add code here to load the different datasets    ## the following target creates a MultiDataSet object from previously   ## created omics sets (geno_set, trans_set, etc)   tar_target(     mo_set,     create_multiomics_set(geno_set, trans_set, metabo_set, pheno_set)   ),   feature_preselection_cov_factory(     mo_set,     to_keep_ns = c(\"rnaseq\" = 1000, \"metabolome\" = 500),     filtered_set_target_name = \"mo_set_filtered\"   ),    ## Another example using to_keep_props   feature_preselection_cov_factory(     mo_set,     to_keep_ns = NULL,     to_keep_props = c(\"rnaseq\" = 0.3, \"metabolome\" = 0.5),     filtered_set_target_name = \"mo_set_filtered\"   ) ) }"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/feature_preselection_mad_factory.html","id":null,"dir":"Reference","previous_headings":"","what":"Target factory for feature preselection based on Median Absolute Deviation — feature_preselection_mad_factory","title":"Target factory for feature preselection based on Median Absolute Deviation — feature_preselection_mad_factory","text":"Creates list targets perform feature preselection datasets MultiDataSet object retaining features highest Median Absolute Deviation (MAD).","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/feature_preselection_mad_factory.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Target factory for feature preselection based on Median Absolute Deviation — feature_preselection_mad_factory","text":"","code":"feature_preselection_mad_factory(   mo_data_target,   to_keep_ns,   to_keep_props = NULL,   with_ties = TRUE,   target_name_prefix = \"\",   filtered_set_target_name = NULL )"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/feature_preselection_mad_factory.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Target factory for feature preselection based on Median Absolute Deviation — feature_preselection_mad_factory","text":"mo_data_target Symbol, name target containing MultiDataSet object. to_keep_ns Named integer vector, number feature retain dataset prefiltered (names correspond dataset name). Value less number features corresponding dataset. Set NULL order use to_keep_props instead. to_keep_props Named numeric vector, proportion features retain dataset prefiltered (names correspond dataset name). Value > 0 < 1. ignored to_keep_ns NULL. with_ties ties kept together? TRUE, may return features requested. Default value TRUE. target_name_prefix Character, prefix add name targets created target factory. Default value \"\". filtered_set_target_name Character, name final target containing filtered MultiDataSet object. NULL, name automatically supplied. Default value NULL.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/feature_preselection_mad_factory.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Target factory for feature preselection based on Median Absolute Deviation — feature_preselection_mad_factory","text":"list target objects. target_name_prefix = \"\" filtered_set_target_name = NULL, following targets created: mad_spec: target generates grouped tibble row corresponds one dataset filtered, columns specifying dataset name, associated values to_keep_ns, to_keep_props with_ties. mad_mat: dynamic branching target run get_dataset_matrix() function dataset. individual_mad_values: dynamic branching target runs select_features_mad_matrix() function dataset. filtered_set_mad: target retain original MultiDataSet object features selected based MAD values.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/feature_preselection_mad_factory.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Target factory for feature preselection based on Median Absolute Deviation — feature_preselection_mad_factory","text":"","code":"if (FALSE) { ## in the _targets.R library(moiraine)  list(   ## add code here to load the different datasets    ## the following target creates a MultiDataSet object from previously   ## created omics sets (geno_set, trans_set, etc)   tar_target(     mo_set,     create_multiomics_set(geno_set, trans_set, metabo_set, pheno_set)   ),   feature_preselection_mad_factory(     mo_set,     to_keep_ns = c(\"rnaseq\" = 1000, \"metabolome\" = 500),     filtered_set_target_name = \"mo_set_filtered\"   ),    ## Another example using to_keep_props   feature_preselection_mad_factory(     mo_set,     to_keep_ns = NULL,     to_keep_props = c(\"rnaseq\" = 0.3, \"metabolome\" = 0.5),     filtered_set_target_name = \"mo_set_filtered\"   ) ) }"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/feature_preselection_splsda_factory.html","id":null,"dir":"Reference","previous_headings":"","what":"Target factory for feature preselection based on sPLS-DA — feature_preselection_splsda_factory","title":"Target factory for feature preselection based on sPLS-DA — feature_preselection_splsda_factory","text":"Creates list targets perform feature preselection datasets MultiDataSet object sPLS-DA (mixOmics package).","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/feature_preselection_splsda_factory.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Target factory for feature preselection based on sPLS-DA — feature_preselection_splsda_factory","text":"","code":"feature_preselection_splsda_factory(   mo_data_target,   group,   to_keep_ns,   to_keep_props = NULL,   target_name_prefix = \"\",   filtered_set_target_name = NULL,   multilevel = NULL,   ... )"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/feature_preselection_splsda_factory.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Target factory for feature preselection based on sPLS-DA — feature_preselection_splsda_factory","text":"mo_data_target Symbol, name target containing MultiDataSet object. group Character, column name samples information data-frame use samples group. to_keep_ns Named integer vector, number feature retain dataset prefiltered (names correspond dataset name). Value less number features corresponding dataset. Set NULL order use to_keep_props instead. to_keep_props Named numeric vector, proportion features retain dataset prefiltered (names correspond dataset name). Value > 0 < 1. ignored to_keep_ns NULL. target_name_prefix Character, prefix add name targets created target factory. Default value \"\". filtered_set_target_name Character, name final target containing filtered MultiDataSet object. NULL, name automatically supplied. Default value NULL. multilevel Character vector length 1 3 used information repeated measurements. See get_input_splsda() details. Default value NULL (repeated measurements). ... arguments passed perf_splsda function.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/feature_preselection_splsda_factory.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Target factory for feature preselection based on sPLS-DA — feature_preselection_splsda_factory","text":"list target objects. target_name_prefix = \"\" filtered_set_target_name = NULL, following targets created: splsda_spec: generates grouped tibble row corresponds one dataset filtered, columns specifying dataset name, associated values to_keep_ns to_keep_props. individual_splsda_input: dynamic branching target runs get_input_splsda() function dataset. individual_splsda_perf: dynamic branching target runs perf_splsda() function dataset. individual_splsda_run: dynamic branching target runs run_splsda() function dataset, using results individual_splsda_perf guide number latent components construct. filtered_set_slpsda: target retain original MultiDataSet object features selected sPLS-DA run.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/feature_preselection_splsda_factory.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Target factory for feature preselection based on sPLS-DA — feature_preselection_splsda_factory","text":"","code":"if (FALSE) { ## in the _targets.R library(moiraine)  list(   ## add code here to load the different datasets    ## the following target creates a MultiDataSet object from previously   ## created omics sets (geno_set, trans_set, etc)   tar_target(     mo_set,     create_multiomics_set(geno_set, trans_set, metabo_set, pheno_set)   ),   feature_preselection_splsda_factory(     mo_set,     group = \"outcome_group\",     to_keep_ns = c(\"rnaseq\" = 1000, \"metabolome\" = 500),     filtered_set_target_name = \"mo_set_filtered\",     folds = 10 ## example of an argument passed to perf_splsda   ),    ## Another example using to_keep_props   feature_preselection_splsda_factory(     mo_set,     group = \"outcome_group\",     to_keep_ns = NULL,     to_keep_props = c(\"rnaseq\" = 0.3, \"metabolome\" = 0.5),     filtered_set_target_name = \"mo_set_filtered\",     folds = 10 ## example of an argument passed to perf_splsda   ) ) }"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_complete_data.html","id":null,"dir":"Reference","previous_headings":"","what":"Get MultiDataSet object with imputed values — get_complete_data","title":"Get MultiDataSet object with imputed values — get_complete_data","text":"Replace missing values imputed values dataset MultiDataSet object, based results Principal Component Analysis applied corresponding dataset.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_complete_data.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get MultiDataSet object with imputed values — get_complete_data","text":"","code":"get_complete_data(mo_data, pca_result)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_complete_data.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get MultiDataSet object with imputed values — get_complete_data","text":"mo_data MultiDataSet::MultiDataSet object. pca_result list element result PCA run different dataset, computed run_pca() function.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_complete_data.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get MultiDataSet object with imputed values — get_complete_data","text":"MultiDataSet::MultiDataSet object, assay dataset imputed dataset.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_complete_data.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Get MultiDataSet object with imputed values — get_complete_data","text":"Uses pcaMethods::completeObs() function impute missing values.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_dataset_matrix.html","id":null,"dir":"Reference","previous_headings":"","what":"Get multi-omics dataset as matrix — get_dataset_matrix","title":"Get multi-omics dataset as matrix — get_dataset_matrix","text":"Extracts omics dataset matrix measurements MultiDataSet object.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_dataset_matrix.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get multi-omics dataset as matrix — get_dataset_matrix","text":"","code":"get_dataset_matrix(mo_data, dataset_name, keep_dataset_name = FALSE)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_dataset_matrix.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get multi-omics dataset as matrix — get_dataset_matrix","text":"mo_data MultiDataSet::MultiDataSet object. dataset_name Character, name omics dataset extract. keep_dataset_name Logical, dataset name stored 'dataset_name' attribute resulting matrix? Default value FALSE.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_dataset_matrix.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get multi-omics dataset as matrix — get_dataset_matrix","text":"matrix measurements features rows samples columns. name dataset stored 'dataset_name' attribute keep_dataset_name TRUE.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_dataset_matrix.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Get multi-omics dataset as matrix — get_dataset_matrix","text":"","code":"if (FALSE) { ## mo_data is a MultiDataSet object with a dataset called \"rnaseq\" mat <- get_dataset_matrix(mo_data, \"rnaseq\", keep_dataset_name = TRUE) ## with keep_dataset_name = TRUE, can recover dataset name as follows: attr(mat, \"dataset_name\") }"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_datasets.html","id":null,"dir":"Reference","previous_headings":"","what":"Get multi-omics measurement datasets — get_datasets","title":"Get multi-omics measurement datasets — get_datasets","text":"Returns multi-omics datasets list matrices MultiDataSet object.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_datasets.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get multi-omics measurement datasets — get_datasets","text":"","code":"get_datasets(mo_data)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_datasets.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get multi-omics measurement datasets — get_datasets","text":"mo_data MultiDataSet::MultiDataSet object.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_datasets.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get multi-omics measurement datasets — get_datasets","text":"named list matrices, features rows samples columns.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_features.html","id":null,"dir":"Reference","previous_headings":"","what":"Get feature IDs from MultiDataSet — get_features","title":"Get feature IDs from MultiDataSet — get_features","text":"Extract list feature IDs dataset MultiDataSet object.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_features.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get feature IDs from MultiDataSet — get_features","text":"","code":"get_features(mo_data)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_features.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get feature IDs from MultiDataSet — get_features","text":"mo_data MultiDataSet::MultiDataSet object.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_features.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get feature IDs from MultiDataSet — get_features","text":"named list, one element per dataset, element character vector feature IDs.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_features_labels.html","id":null,"dir":"Reference","previous_headings":"","what":"Get feature labels — get_features_labels","title":"Get feature labels — get_features_labels","text":"Extracts feature labels MultiDataSet object given name column feature metadata dataset containing feature labels dataset.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_features_labels.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get feature labels — get_features_labels","text":"","code":"get_features_labels(mo_data, label_cols = \"feature_id\", truncate = NULL)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_features_labels.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get feature labels — get_features_labels","text":"mo_data MultiDataSet::MultiDataSet object. label_cols Character named list character, giving dataset name column corresponding features metadata use label. one value, used datasets. list, names must correspond names datasets mo_data. Default value feature_id, .e. default ID features used label. truncate Integer, width labels truncated (avoid issues long labels plots). NULL (default value), truncation performed.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_features_labels.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get feature labels — get_features_labels","text":"tibble columns dataset, feature_id label.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_features_labels.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Get feature labels — get_features_labels","text":"","code":"if (FALSE) { ## This works if each dataset in mo_data has in their features metadata table ## a column called `name` that contains the feature labels. get_features_labels(mo_data, label_cols = \"name\")  ## If instead we want to use a different column for each dataset: get_features_labels(   mo_data,   label_cols = list(     \"snps\" = \"feature_id\",     \"rnaseq\" = \"gene_name\",     \"metabolome\" = \"comp_formula\"   ) )  ## If we want to use the feature IDs as labels for the genomics dataset, ## we can simply remove it from the list (this is equivalent to the example ## above): get_features_labels(   mo_data,   label_cols = list(     \"rnaseq\" = \"gene_name\",     \"metabolome\" = \"comp_formula\"   ) ) }"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_features_metadata.html","id":null,"dir":"Reference","previous_headings":"","what":"Get features metadata dataframes from MultiDataSet — get_features_metadata","title":"Get features metadata dataframes from MultiDataSet — get_features_metadata","text":"Extracts features metadata dataframe (featureData field) dataset MultiDataSet object.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_features_metadata.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get features metadata dataframes from MultiDataSet — get_features_metadata","text":"","code":"get_features_metadata(mo_data)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_features_metadata.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get features metadata dataframes from MultiDataSet — get_features_metadata","text":"mo_data MultiDataSet::MultiDataSet object.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_features_metadata.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get features metadata dataframes from MultiDataSet — get_features_metadata","text":"named list data-frames, one per dataset mo_data object.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_features_weight_correlation.html","id":null,"dir":"Reference","previous_headings":"","what":"Get features weight correlation — get_features_weight_correlation","title":"Get features weight correlation — get_features_weight_correlation","text":"Constructs correlation matrix features weight latent dimensions obtained different integration methods.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_features_weight_correlation.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get features weight correlation — get_features_weight_correlation","text":"","code":"get_features_weight_correlation(output_list, include_missing_features = FALSE)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_features_weight_correlation.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get features weight correlation — get_features_weight_correlation","text":"output_list List integration methods output, generated via get_output() function. named, names added beginning latent dimension' label. unnamed, name integration method used instead. include_missing_features Logical, whether features missing output included calculation (see Details). Default value FALSE.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_features_weight_correlation.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get features weight correlation — get_features_weight_correlation","text":"correlation matrix.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_features_weight_correlation.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Get features weight correlation — get_features_weight_correlation","text":"include_missing_features FALSE (default behaviour), features present output one integration method (e.g. different pre-filtering applied input data two methods), features ignored. mean features selected one method discarded; case feature assigned weight 0 method select . recommended behaviour, changed specific scenarios (e.g. check whether using features dataset vs variance-based preselection affect features deemed important). include_missing_features TRUE, missing features assigned weight 0.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_filtered_dataset_splsda.html","id":null,"dir":"Reference","previous_headings":"","what":"Get filtered MultiDataSet object based on sPLS-DA runs — get_filtered_dataset_splsda","title":"Get filtered MultiDataSet object based on sPLS-DA runs — get_filtered_dataset_splsda","text":"Selects features associated phenotype interest omics datasets based results sPLS-DA applied corresponding omics datasets.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_filtered_dataset_splsda.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get filtered MultiDataSet object based on sPLS-DA runs — get_filtered_dataset_splsda","text":"","code":"get_filtered_dataset_splsda(mo_data, splsda_res_list)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_filtered_dataset_splsda.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get filtered MultiDataSet object based on sPLS-DA runs — get_filtered_dataset_splsda","text":"mo_data MultiDataSet-class object. splsda_res_list list result sPLS-DA run dataset filtered, returned run_splsda function.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_filtered_dataset_splsda.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get filtered MultiDataSet object based on sPLS-DA runs — get_filtered_dataset_splsda","text":"MultiDataSet-class object.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_filtered_dataset_splsda.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Get filtered MultiDataSet object based on sPLS-DA runs — get_filtered_dataset_splsda","text":"Note sPLS-DA method can select feature several latent components, number features retained dataset might less number specified to_keep argument.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_filtered_dataset_splsda.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Get filtered MultiDataSet object based on sPLS-DA runs — get_filtered_dataset_splsda","text":"","code":"if (FALSE) { # Goal: keep 20% of features in dataset1, and 50% of features in dataset2 # outcome_group is the outcome of interest in the samples metadata to_keep_prop <- c(\"dataset1\" = 0.2, \"dataset_2\" = 0.5)  # 1) assess optimal number of latent components for dataset1 and dataset2 splsda_perf_runs <- lapply(names(to_keep_prop), function(i) {   perf_splsda(mo_data, i, \"outcome_group\") })  # 2) run sPLS-DA with optimal number of latent components for dataset1 and dataset2 splsda_runs <- lapply(splsda_perf_runs, function(x) {   run_splsda(mo_data, x, to_keep_prop = to_keep_prop[attr(x, \"dataset_name\")]) })  # 3) Get the filtered dataset mo_data_filtered <- get_filtered_dataset_splsda(mo_data, splsda_runs) }"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_filtered_dataset_variability.html","id":null,"dir":"Reference","previous_headings":"","what":"Get filtered MultiDataSet object based on variability measure — get_filtered_dataset_variability","title":"Get filtered MultiDataSet object based on variability measure — get_filtered_dataset_variability","text":"Selects highly variable features omics datasets based features' variability (e.g. MAD COV).","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_filtered_dataset_variability.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get filtered MultiDataSet object based on variability measure — get_filtered_dataset_variability","text":"","code":"get_filtered_dataset_variability(mo_data, var_list)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_filtered_dataset_variability.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get filtered MultiDataSet object based on variability measure — get_filtered_dataset_variability","text":"mo_data MultiDataSet-class object. var_list list result MAD COV calculation dataset filtered, returned select_features_mad select_features_cov function.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_filtered_dataset_variability.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get filtered MultiDataSet object based on variability measure — get_filtered_dataset_variability","text":"MultiDataSet-class object.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_filtered_dataset_variability.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Get filtered MultiDataSet object based on variability measure — get_filtered_dataset_variability","text":"","code":"if (FALSE) { # Goal: keep 20% of features in dataset1, and 50% of features in dataset2 to_keep_prop <- c(\"dataset1\" = 0.2, \"dataset_2\" = 0.5)  # 1) compute MAD values and select features for dataset1 and dataset2 mad_list <- lapply(names(to_keep_prop), function(i) {   select_features_mad(mo_data, i, to_keep_prop[i]) })  # 2) Get the filtered dataset mo_data_filtered <- get_filtered_dataset_variability(mo_data, mad_list) }"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_input_mefisto.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate MEFISTO input data — get_input_mefisto","title":"Generate MEFISTO input data — get_input_mefisto","text":"Creates object can used input MEFISTO analysis implemented MOFA2 package. contains omics datasets well features samples metadata.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_input_mefisto.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate MEFISTO input data — get_input_mefisto","text":"","code":"get_input_mefisto(   mo_data,   covariates,   datasets = names(mo_data),   groups = NULL,   options_list = NULL,   only_common_samples = FALSE )"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_input_mefisto.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate MEFISTO input data — get_input_mefisto","text":"mo_data MultiDataSet-class object. covariates Character character vector length 2, column name(s) samples metadata data-frames use continuous covariates. datasets Character vector, names datasets mo_data include analysis. groups Character, column name samples metadata data-frames use groups (use get_samples_metadata view samples metadata data-frame dataset). options_list named list. contain 4 elements, named 'data_options', 'model_options', 'training_options' 'mefisto_options'. Provide respectively data, model, training mefisto options apply MEFISTO run. See get_default_data_options, get_default_model_options, get_default_training_options get_default_mefisto_options. only_common_samples Logical, whether samples present datasets returned. Default value FALSE.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_input_mefisto.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generate MEFISTO input data — get_input_mefisto","text":"MOFA object.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_input_mixomics_supervised.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate mixomics input data for supervised methods — get_input_mixomics_supervised","title":"Generate mixomics input data for supervised methods — get_input_mixomics_supervised","text":"Creates object can used input MixOmics package. contains omics datasets restricted common samples (missing group information) outcome group sample.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_input_mixomics_supervised.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate mixomics input data for supervised methods — get_input_mixomics_supervised","text":"","code":"get_input_mixomics_supervised(mo_data, group, datasets = names(mo_data))"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_input_mixomics_supervised.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate mixomics input data for supervised methods — get_input_mixomics_supervised","text":"mo_data MultiDataSet-class object. group Character, column name samples metadata data-frames use samples group (use get_samples_metadata view samples information data-frame dataset). column either type factor, character integer. datasets Character vector, names datasets mo_data include analysis.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_input_mixomics_supervised.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generate mixomics input data for supervised methods — get_input_mixomics_supervised","text":"list, element corresponds one omics dataset, samples rows features columns. Y element factor vector outcome group sample.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_input_mixomics_supervised.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Generate mixomics input data for supervised methods — get_input_mixomics_supervised","text":"Samples missing values group column sample metadata removed dataset.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_input_mixomics_unsupervised.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate mixomics input data for unsupervised methods — get_input_mixomics_unsupervised","title":"Generate mixomics input data for unsupervised methods — get_input_mixomics_unsupervised","text":"Creates object can used input MixOmics package. contains omics datasets restricted common samples.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_input_mixomics_unsupervised.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate mixomics input data for unsupervised methods — get_input_mixomics_unsupervised","text":"","code":"get_input_mixomics_unsupervised(mo_data, datasets = names(mo_data))"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_input_mixomics_unsupervised.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate mixomics input data for unsupervised methods — get_input_mixomics_unsupervised","text":"mo_data MultiDataSet-class object. datasets Character vector, names datasets mo_data include analysis.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_input_mixomics_unsupervised.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generate mixomics input data for unsupervised methods — get_input_mixomics_unsupervised","text":"list, element corresponds one omics dataset, samples rows features columns.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_input_mofa.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate MOFA input data — get_input_mofa","title":"Generate MOFA input data — get_input_mofa","text":"Creates object can used input MOFA analysis implemented MOFA2 package. contains omics datasets well features samples metadata.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_input_mofa.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate MOFA input data — get_input_mofa","text":"","code":"get_input_mofa(   mo_data,   datasets = names(mo_data),   groups = NULL,   options_list = NULL,   only_common_samples = FALSE )"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_input_mofa.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate MOFA input data — get_input_mofa","text":"mo_data MultiDataSet-class object. datasets Character vector, names datasets mo_data include analysis. groups Character, column name samples metadata data-frames use groups (use get_samples_metadata view samples metadata data-frame dataset). WARNING: use familiar MOFA use groups. See https://biofam.github.io/MOFA2/faq.html, section \"FAQ multi-group functionality\". options_list named list. contain 3 elements, named 'data_options', 'model_options' 'training_options'. Provide respectively data, model training options apply MOFA run. See get_default_data_options, get_default_model_options get_default_training_options. only_common_samples Logical, whether samples present datasets returned. Default value FALSE.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_input_mofa.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generate MOFA input data — get_input_mofa","text":"MOFA object.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_input_mofa2.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate input data for MOFA2 package — get_input_mofa2","title":"Generate input data for MOFA2 package — get_input_mofa2","text":"Creates object can used input MOFA MEFISTO analysis implemented MOFA2 package. contains omics datasets well features samples metadata. called directly; instead use get_input_mofa get_input_mefisto.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_input_mofa2.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate input data for MOFA2 package — get_input_mofa2","text":"","code":"get_input_mofa2(   mo_data,   datasets,   covariates,   groups,   options_list,   only_common_samples )"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_input_mofa2.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate input data for MOFA2 package — get_input_mofa2","text":"mo_data MultiDataSet-class object. datasets Character vector, names datasets mo_data include analysis. covariates Character character vector length 2, column name(s) samples metadata data-frames use continuous covariates. NULL, creates input object MOFA. null, creates input object MEFISTO. groups Character, column name samples metadata data-frames use group. options_list named list. contain 3 4 elements (depending whether input MOFA MEFISTO), named 'data_options', 'model_options', 'training_options' 'mefisto_options' (latter MEFISTO input). only_common_samples Logical, whether samples present datasets returned. Default value FALSE.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_input_mofa2.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generate input data for MOFA2 package — get_input_mofa2","text":"MOFA object.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_input_omicspls.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate omicsPLS input data — get_input_omicspls","title":"Generate omicsPLS input data — get_input_omicspls","text":"Creates object can used input omicsPLS package. contains omics datasets restricted common samples. dataset feature-centred.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_input_omicspls.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate omicsPLS input data — get_input_omicspls","text":"","code":"get_input_omicspls(mo_data, datasets = names(mo_data), scale_data = FALSE)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_input_omicspls.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate omicsPLS input data — get_input_omicspls","text":"mo_data MultiDataSet-class object. datasets Character vector length 2, names datasets mo_data include analysis. scale_data Boolean, datasets scaled? Default value FALSE.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_input_omicspls.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generate omicsPLS input data — get_input_omicspls","text":"list, element corresponds one omics dataset, samples rows features columns.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_input_spls.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate sPLS input data (for mixomics) — get_input_spls","title":"Generate sPLS input data (for mixomics) — get_input_spls","text":"Creates object can used input (s)PLS functions mixOmics package. contains two omics datasets selected, restricted common samples.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_input_spls.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate sPLS input data (for mixomics) — get_input_spls","text":"","code":"get_input_spls(mo_data, mode, datasets = names(mo_data), multilevel = NULL)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_input_spls.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate sPLS input data (for mixomics) — get_input_spls","text":"mo_data MultiDataSet::MultiDataSet object. mode Character, mode PLS use analysis (see sPLS documentation). one 'regression', 'canonical', 'invariant' 'classic'. datasets Character vector length 2, names datasets mo_data include analysis. multilevel Character vector length 1 3 used information repeated measurements. See Details. Default value NULL, .e. multilevel option used.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_input_spls.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generate sPLS input data (for mixomics) — get_input_spls","text":"list, element corresponds one omics dataset, samples rows features columns. mode use analysis stored mode attribute returned object.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_input_spls.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Generate sPLS input data (for mixomics) — get_input_spls","text":"multilevel argument: enables multilevel option (see mixOmics site) deal repeated measurements. mixOmics::spls() enables one- two-factor decomposition. one-factor decomposition, multilevel argument name column samples metadata gives ID observation units (e.g. ID subjects measured several times). resulting design matrix (stored multilevel argument returned object) data-frame one column gives ID (integer) observation units corresponding sample omics datasets. two-factor decomposition, multilevel length 3. first value, similarly one-factor decomposition, name column samples metadata gives ID observation units (e.g. ID subjects measured several times). second third values name columns samples metadata give two factors considered. resulting design matrix (stored multilevel argument returned object) data-frame three columns: first column gives ID (integer) observation units corresponding sample omics datasets; second third columns give levels two factors.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_input_splsda.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate sPLS-DA input data (for mixomics) — get_input_splsda","title":"Generate sPLS-DA input data (for mixomics) — get_input_splsda","text":"Creates object can used input (s)PLS-DA functions mixOmics package. contains omics dataset well samples group membership list.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_input_splsda.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate sPLS-DA input data (for mixomics) — get_input_splsda","text":"","code":"get_input_splsda(mo_data, dataset_name, group, multilevel = NULL)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_input_splsda.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate sPLS-DA input data (for mixomics) — get_input_splsda","text":"mo_data MultiDataSet-class object. dataset_name Character, name dataset mo_data analyse. group Character, column name samples information data-frame use samples group (use get_samples_metadata view samples information data-frame omics dataset). multilevel Character vector length 1 3 used information repeated measurements. See Details. Default value NULL (repeated measurements).","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_input_splsda.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generate sPLS-DA input data (for mixomics) — get_input_splsda","text":"list, first element corresponds omics dataset, samples rows features columns, second element (named 'Y') named factor vector, giving sample group. name dataset analysed stored dataset_name attribute returned object.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_input_splsda.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Generate sPLS-DA input data (for mixomics) — get_input_splsda","text":"multilevel argument: enables multilevel option (see mixOmics site) deal repeated measurements. mixOmics::splsda() enables one- two-factor decomposition. one-factor decomposition, multilevel argument name column samples metadata gives ID observation units (e.g. ID subjects measured several times). resulting design matrix (stored multilevel argument returned object) data-frame one column gives ID (integer) observation units corresponding sample omics datasets. two-factor decomposition, multilevel length 3. first value, similarly one-factor decomposition, name column samples metadata gives ID observation units (e.g. ID subjects measured several times). second third values name columns samples metadata give two factors considered. resulting design matrix (stored multilevel argument returned object) data-frame three columns: first column gives ID (integer) observation units corresponding sample omics datasets; second third columns give levels two factors.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_latent_dimensions.html","id":null,"dir":"Reference","previous_headings":"","what":"Get latent dimensions levels from dimension reduction output — get_latent_dimensions","title":"Get latent dimensions levels from dimension reduction output — get_latent_dimensions","text":"Extracts latent dimension levels output dimension reduction method.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_latent_dimensions.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get latent dimensions levels from dimension reduction output — get_latent_dimensions","text":"","code":"get_latent_dimensions(method_output)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_latent_dimensions.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get latent dimensions levels from dimension reduction output — get_latent_dimensions","text":"method_output Integration method output generated via get_output() function.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_latent_dimensions.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get latent dimensions levels from dimension reduction output — get_latent_dimensions","text":"character vector giving labels latent dimensions.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_output.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract output of integration method in standard format — get_output","title":"Extract output of integration method in standard format — get_output","text":"Extract samples score features weight result integration method. get_output() function provides wrapper around methods' specific get_output_*() functions.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_output.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract output of integration method in standard format — get_output","text":"","code":"get_output(method_output, use_average_dimensions = TRUE)  get_output_pca(method_output)  get_output_splsda(method_output)  get_output_spls(method_output, use_average_dimensions = TRUE)  get_output_diablo(method_output, use_average_dimensions = TRUE)  get_output_mofa2(method_output)  get_output_so2pls(method_output, use_average_dimensions = TRUE)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_output.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract output of integration method in standard format — get_output","text":"method_output output integration method. use_average_dimensions Logical, (weighted) average samples scores latent dimension across datasets used? FALSE, separate set sample scores returned dataset latent dimensions. applies sPLS, DIABLO sO2PLS results. Default value TRUE.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_output.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract output of integration method in standard format — get_output","text":"S3 object class output_dimension_reduction, .e. named list, following elements: features_weight: tibble features weight (loadings) latent dimension, columns feature_id, dataset, latent_dimension, weight (unscaled feature weight corresponding latent dimension), importance (corresponds scaled absolute weight, .e. 1 = feature maximum absolute weight corresponding latent dimension dataset, 0 = feature selected corresponding latent dimension) samples_score: tibble samples score latent component, columns sample_id, latent_dimension, score (unscaled samples score corresponding latent dimension) variance_explained: tibble fraction variance explained latent component relevant datasets.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_pca_arguments.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract arguments used in PCA run — get_pca_arguments","title":"Extract arguments used in PCA run — get_pca_arguments","text":"Extracts list arguments used PCA run list PCA results, formats tibble.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_pca_arguments.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract arguments used in PCA run — get_pca_arguments","text":"","code":"get_pca_arguments(pca_result)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_pca_arguments.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract arguments used in PCA run — get_pca_arguments","text":"pca_result result PCA run datasets, computed pcaMethods::pca() function.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_pca_arguments.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract arguments used in PCA run — get_pca_arguments","text":"tibble following columns: \"Omics dataset\", \"PCA method used\", \"Number Principal Components computed\", \"Scaling applied\" \"Dataset centered\".","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_samples.html","id":null,"dir":"Reference","previous_headings":"","what":"Get sample IDs from MultiDataSet — get_samples","title":"Get sample IDs from MultiDataSet — get_samples","text":"Extract list sample IDs dataset MultiDataSet object.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_samples.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get sample IDs from MultiDataSet — get_samples","text":"","code":"get_samples(mo_data)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_samples.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get sample IDs from MultiDataSet — get_samples","text":"mo_data MultiDataSet::MultiDataSet object.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_samples.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get sample IDs from MultiDataSet — get_samples","text":"named list, one element per dataset, element character vector sample IDs.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_samples_metadata.html","id":null,"dir":"Reference","previous_headings":"","what":"Get samples metadata dataframes from MultiDataSet — get_samples_metadata","title":"Get samples metadata dataframes from MultiDataSet — get_samples_metadata","text":"Extracts samples metadata data-frame (phenoData field) dataset MultiDataSet object.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_samples_metadata.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get samples metadata dataframes from MultiDataSet — get_samples_metadata","text":"","code":"get_samples_metadata(mo_data)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_samples_metadata.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get samples metadata dataframes from MultiDataSet — get_samples_metadata","text":"mo_data MultiDataSet::MultiDataSet object.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_samples_metadata.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get samples metadata dataframes from MultiDataSet — get_samples_metadata","text":"named list data-frames, one per dataset mo_data object.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_samples_metadata_combined.html","id":null,"dir":"Reference","previous_headings":"","what":"Get combined samples metadata data-frame from MultiDataSet — get_samples_metadata_combined","title":"Get combined samples metadata data-frame from MultiDataSet — get_samples_metadata_combined","text":"Extracts samples metadata data-frame (phenoData field) dataset MultiDataSet object combine one dataframe.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_samples_metadata_combined.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get combined samples metadata data-frame from MultiDataSet — get_samples_metadata_combined","text":"","code":"get_samples_metadata_combined(mo_data, only_common_cols = TRUE)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_samples_metadata_combined.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get combined samples metadata data-frame from MultiDataSet — get_samples_metadata_combined","text":"mo_data MultiDataSet::MultiDataSet object. only_common_cols Logical, whether retain common columns. TRUE (default value), retain columns present samples metadata datasets. FALSE, retain columns datasets' sample metadata.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_samples_metadata_combined.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get combined samples metadata data-frame from MultiDataSet — get_samples_metadata_combined","text":"data-frame samples metadata.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_samples_score_correlation.html","id":null,"dir":"Reference","previous_headings":"","what":"Get samples score correlation — get_samples_score_correlation","title":"Get samples score correlation — get_samples_score_correlation","text":"Constructs correlation matrix samples score latent dimensions obtained different integration methods.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_samples_score_correlation.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get samples score correlation — get_samples_score_correlation","text":"","code":"get_samples_score_correlation(output_list)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_samples_score_correlation.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get samples score correlation — get_samples_score_correlation","text":"output_list List integration methods output, generated via get_output() function. named, names added beginning latent dimension' label. unnamed, name integration method used instead.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_samples_score_correlation.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get samples score correlation — get_samples_score_correlation","text":"correlation matrix.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_selected_features.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract selected features — get_selected_features","title":"Extract selected features — get_selected_features","text":"Extracts selected features output integration method. features non-null weight least one latent dimension returned. MultiDataSet object supplied, information features features metadata added resulting table.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_selected_features.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract selected features — get_selected_features","text":"","code":"get_selected_features(   method_output,   latent_dimensions = NULL,   datasets = NULL,   mo_data = NULL )"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_selected_features.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract selected features — get_selected_features","text":"method_output Integration method output generated via get_output() function. latent_dimensions Character vector latent dimensions name. Default value NULL (top contributing features returned latent dimensions). datasets Character vector datasets name. Default value NULL (top contributing features returned datasets). mo_data MultiDataSet::MultiDataSet object.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_selected_features.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract selected features — get_selected_features","text":"tibble containing one row per feature latent dimension, giving weight importance score feature corresponding latent dimension. mo_data supplied, information features features metadata added resulting table.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_table_transformations.html","id":null,"dir":"Reference","previous_headings":"","what":"Get table with transformation applied to each dataset — get_table_transformations","title":"Get table with transformation applied to each dataset — get_table_transformations","text":"results transformations datasets, generates table giving dataset transformation applied .","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_table_transformations.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get table with transformation applied to each dataset — get_table_transformations","text":"","code":"get_table_transformations(   transformation_result,   best_normalize_details = FALSE )"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_table_transformations.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get table with transformation applied to each dataset — get_table_transformations","text":"transformation_result list element result transformation applied different dataset, computed transform_dataset function. best_normalize_details Logical, information transformations selected bestNormalize feature displayed? Default value FALSE.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_table_transformations.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get table with transformation applied to each dataset — get_table_transformations","text":"tibble columns 'Dataset' 'Transformation'. best_normalize_details = TRUE, additional column 'Details' lists chsoen transformation applied feature corresponding dataset bestNormalize transformation.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_top_features.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract top features — get_top_features","title":"Extract top features — get_top_features","text":"Extracts features highest contribution latent dimensions constructed integration method. Can retain specific number top contributing features dataset latent dimension, features minimum importance score.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_top_features.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract top features — get_top_features","text":"","code":"get_top_features(   method_output,   n_features = 10,   min_importance = NULL,   latent_dimensions = NULL,   datasets = NULL,   mo_data = NULL )"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_top_features.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract top features — get_top_features","text":"method_output Integration method output generated via get_output() function. n_features Integer, number features extract latent dimension dataset. Ignored min_importance set. Default value 10. include ties. min_importance Numeric value 0 1, minimum importance score used select features. Default value NULL, .e. top n_features features selected instead. latent_dimensions Character vector latent dimensions name. Default value NULL (top contributing features returned latent dimensions). datasets Character vector datasets name. Default value NULL (top contributing features returned datasets). mo_data MultiDataSet::MultiDataSet object.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_top_features.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract top features — get_top_features","text":"tibble containing one row per feature latent dimension, giving weight importance score feature corresponding latent dimension. mo_data supplied, information features features metadata added resulting table.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_transformed_data.html","id":null,"dir":"Reference","previous_headings":"","what":"Get MultiDataSet with transformed data — get_transformed_data","title":"Get MultiDataSet with transformed data — get_transformed_data","text":"Replace original datasets transformed datasets MultiDataSet object results transformations applied datasets.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_transformed_data.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get MultiDataSet with transformed data — get_transformed_data","text":"","code":"get_transformed_data(mo_data, transformation_result)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_transformed_data.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get MultiDataSet with transformed data — get_transformed_data","text":"mo_data MultiDataSet-class object. transformation_result list element result transformation applied different dataset, computed transform_dataset function.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_transformed_data.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get MultiDataSet with transformed data — get_transformed_data","text":"MultiDataSet-class object, assay dataset imputed dataset.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/ggpairs_custom.html","id":null,"dir":"Reference","previous_headings":"","what":"ggpairs plot with custom colours — ggpairs_custom","title":"ggpairs plot with custom colours — ggpairs_custom","text":"Creates ggpairs plot (see GGally::ggpairs()) colours shapes can differ upper triangle, lower triangle diagonal plots.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/ggpairs_custom.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"ggpairs plot with custom colours — ggpairs_custom","text":"","code":"ggpairs_custom(   toplot,   vars,   colour_upper = NULL,   colour_diag = colour_upper,   colour_lower = colour_upper,   shape_upper = NULL,   shape_lower = shape_upper,   scale_colour_upper = NULL,   scale_colour_diag = NULL,   scale_colour_lower = NULL,   scale_shape_upper = NULL,   scale_shape_lower = NULL,   title = NULL,   point_size = 1.5 )"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/ggpairs_custom.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"ggpairs plot with custom colours — ggpairs_custom","text":"toplot Tibble wide format, observations rows variables columns. vars Character vector, names columns toplot correspond variables used plot matrix. colour_upper Character, name column toplot use colouring observations upper triangle plots. Default value NULL. colour_diag Character, name column toplot use colouring observations diagonal plots. default, follow colour_upper. colour_lower Character, name column toplot use colouring observations lower triangle plots. default, follow colour_upper. shape_upper Character, name column toplot use shaping observations upper triangle plots. Default value NULL. shape_lower Character, name column toplot use shaping observations lower triangle plots. default, follow shape_upper. scale_colour_upper ggplot2 colour scale use upper triangle plots. Default value NULL (colour_upper NULL, use ggplot2 default colour scales). scale_colour_diag ggplot2 colour scale use diagonal plots. NULL (default), colour scale used upper triangle plots used colour_diag equal colour_upper; colour scale used lower triangle plots used colour_diag equal colour_lower. scale_colour_lower ggplot2 colour scale use lower triangle plots. NULL (default), colour scale used upper triangle plots used. scale_shape_upper ggplot2 shape scale use upper triangle plots. Default value NULL (shape_upper NULL, use ggplot2 default shape scale). scale_shape_lower ggplot2 shape scale use lower triangle plots. NULL (default), shape scale used upper triangle plots used. title Character, title plot. Default value NULL (title added plot). point_size Numeric, size points (pt) plot. Default value 1.5.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/ggpairs_custom.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"ggpairs plot with custom colours — ggpairs_custom","text":"ggmatrix plot.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/ggpairs_custom.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"ggpairs plot with custom colours — ggpairs_custom","text":"","code":"if (FALSE) { library(palmerpenguins) library(ggplot2) data(\"penguins\")  vars <- c(   \"bill_length_mm\",   \"bill_depth_mm\",   \"flipper_length_mm\" )  toplot <- penguins |>   dplyr::filter(!is.na(bill_length_mm))  # simple scatterplots of the variables ggpairs_custom(toplot, vars)  # colouring points by species, using custom colour palette ggpairs_custom(   toplot,   vars,   colour_upper = \"species\",   scale_colour_upper = scale_colour_brewer(palette = \"Set1\") )  # now adding the sex variable as shape of the points ggpairs_custom(   toplot,   vars,   colour_upper = \"species\",   scale_colour_upper = scale_colour_brewer(palette = \"Set1\"),   shape_upper = \"sex\" )  # using the lower plots to show the island as colour ggpairs_custom(   toplot,   vars,   colour_upper = \"species\",   scale_colour_upper = scale_colour_brewer(palette = \"Set1\"),   shape_upper = \"sex\",   colour_lower = \"island\",   scale_colour_lower = scale_colour_viridis_d() )  # showing species and sex in upper plots, body mass and island # in lower plots ggpairs_custom(   toplot,   vars,   colour_upper = \"species\",   scale_colour_upper = scale_colour_brewer(palette = \"Set1\"),   shape_upper = \"sex\",   shape_lower = \"island\",   colour_lower = \"body_mass_g\",   scale_colour_lower = scale_colour_viridis_c(option = \"plasma\") )  # same as above, but the diagonal plots show density per year ggpairs_custom(   toplot,   vars,   colour_upper = \"species\",   scale_colour_upper = scale_colour_brewer(palette = \"Set1\"),   shape_upper = \"sex\",   shape_lower = \"island\",   colour_lower = \"body_mass_g\",   scale_colour_lower = scale_colour_viridis_c(option = \"plasma\"),   colour_diag = \"year\" )  # common legend if the diagonal follows the colour of the lower plots ggpairs_custom(   toplot,   vars,   colour_upper = \"species\",   scale_colour_upper = scale_colour_brewer(palette = \"Set1\"),   colour_lower = \"island\",   scale_colour_lower = scale_colour_brewer(palette = \"Accent\"),   colour_diag = \"island\" ) }"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/hclust_matrix_rows.html","id":null,"dir":"Reference","previous_headings":"","what":"Hierarchical clustering of matrix rows — hclust_matrix_rows","title":"Hierarchical clustering of matrix rows — hclust_matrix_rows","text":"Performs hierarchical clustering rows matrix. Code inspired ComplexHeatmap package.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/hclust_matrix_rows.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Hierarchical clustering of matrix rows — hclust_matrix_rows","text":"","code":"hclust_matrix_rows(x)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/hclust_matrix_rows.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Hierarchical clustering of matrix rows — hclust_matrix_rows","text":"x Matrix.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/hclust_matrix_rows.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Hierarchical clustering of matrix rows — hclust_matrix_rows","text":"dendrogram.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/import_dataset_csv.html","id":null,"dir":"Reference","previous_headings":"","what":"Import a dataset from a csv file — import_dataset_csv","title":"Import a dataset from a csv file — import_dataset_csv","text":"Reads csv file returns matrix rows corresponds features (e.g. markers, genes, phenotypes...) columns correspond samples/observations.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/import_dataset_csv.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Import a dataset from a csv file — import_dataset_csv","text":"","code":"import_dataset_csv(file, col_id, features_as_rows = TRUE, ...)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/import_dataset_csv.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Import a dataset from a csv file — import_dataset_csv","text":"file Character, path dataset csv file. col_id Character, name column file contains ID rows (.e. feature IDs features_as_rows TRUE, sample IDs features_as_rows FALSE). features_as_rows Logical, rows file correspond features? Default value TRUE, .e. file contains features rows samples columns. ... arguments passed readr::read_csv().","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/import_dataset_csv.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Import a dataset from a csv file — import_dataset_csv","text":"matrix samples columns features rows. Feature IDs used row names sample IDs column names.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/import_dataset_csv.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Import a dataset from a csv file — import_dataset_csv","text":"","code":"if (FALSE) { data_geno <- import_dataset_csv(   \"genotype_dataset.csv\",   col_id = \"Marker\",   features_as_rows = TRUE ) data_pheno <- import_dataset_csv(   \"phenotype_dataset.csv\",   col_id = \"Sample\",   features_as_rows = FALSE ) }"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/import_dataset_csv_factory.html","id":null,"dir":"Reference","previous_headings":"","what":"Target factory for csv datasets import — import_dataset_csv_factory","title":"Target factory for csv datasets import — import_dataset_csv_factory","text":"Creates list targets track file import dataset csv file.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/import_dataset_csv_factory.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Target factory for csv datasets import — import_dataset_csv_factory","text":"","code":"import_dataset_csv_factory(   files,   col_ids,   features_as_rowss,   target_name_suffixes )"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/import_dataset_csv_factory.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Target factory for csv datasets import — import_dataset_csv_factory","text":"files Character vector, vector paths dataset csv files. col_ids Character vector, name column file contains ID rows (.e. feature IDs value features_as_rowss TRUE corresponding dataset, sample IDs value features_as_rowss FALSE). features_as_rowss Logical vector, rows file correspond features? target_name_suffixes Character vector, suffix add name targets created target factory dataset.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/import_dataset_csv_factory.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Target factory for csv datasets import — import_dataset_csv_factory","text":"list target objects. example, two files import target_name_suffixes = c(\"geno\", \"transcripto\"), factory returns following targets: dataset_file_geno dataset_file_transcripto: targets tracking genomics dataset file transcriptomics dataset file, respectively. data_geno data_transcripto: targets import genomics transcriptomics dataset, respectively.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/import_dataset_csv_factory.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Target factory for csv datasets import — import_dataset_csv_factory","text":"","code":"if (FALSE) { ## in the _targets.R library(moiraine)  list(   import_dataset_csv_factory(     c(       \"data/genotype_data.csv\",       \"data/rnaseq_data.csv\"     ),     col_ids = c(\"Marker\", \"Sample\"),     features_as_rows = c(TRUE, FALSE),     target_name_suffixes = c(\"geno\", \"transcripto\")   ) ) }"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/import_fmetadata_csv.html","id":null,"dir":"Reference","previous_headings":"","what":"Import feature metadata from a csv file — import_fmetadata_csv","title":"Import feature metadata from a csv file — import_fmetadata_csv","text":"Reads csv file returns dataframe rows correspond features (e.g. markers, genes, phenotypes...) columns correspond information features. Non-ASCII characters replaced ASCII equivalents (using stringi textclean packages).","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/import_fmetadata_csv.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Import feature metadata from a csv file — import_fmetadata_csv","text":"","code":"import_fmetadata_csv(file, col_id, ...)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/import_fmetadata_csv.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Import feature metadata from a csv file — import_fmetadata_csv","text":"file Character, path dataset csv file. col_id Character, name column file contains feature IDs. ... arguments passed readr::read_csv().","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/import_fmetadata_csv.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Import feature metadata from a csv file — import_fmetadata_csv","text":"data-frame features rows features information columns. Feature IDs used row names.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/import_fmetadata_csv.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Import feature metadata from a csv file — import_fmetadata_csv","text":"","code":"if (FALSE) { geno_info_features <- import_fmetadata_csv(   \"genotype_features_info.csv\",   col_id = \"Marker\" ) }"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/import_fmetadata_csv_factory.html","id":null,"dir":"Reference","previous_headings":"","what":"Target factory for csv features metadata import — import_fmetadata_csv_factory","title":"Target factory for csv features metadata import — import_fmetadata_csv_factory","text":"Creates list targets track file import features metadata csv file.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/import_fmetadata_csv_factory.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Target factory for csv features metadata import — import_fmetadata_csv_factory","text":"","code":"import_fmetadata_csv_factory(files, col_ids, target_name_suffixes)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/import_fmetadata_csv_factory.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Target factory for csv features metadata import — import_fmetadata_csv_factory","text":"files Character vector, vector paths features metadata csv files. col_ids Character vector, name column file contains features ID. target_name_suffixes Character vector, suffix add name targets created target factory dataset.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/import_fmetadata_csv_factory.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Target factory for csv features metadata import — import_fmetadata_csv_factory","text":"list target objects. example, two files import target_name_suffixes = c(\"geno\", \"transcripto\"), factory returns following targets: fmetadata_file_geno fmetadata_file_transcripto: targets tracking genomics transcriptomics features metadata files, respectively. fmetadata_geno fmetadata_transcripto: targets import genomics transcriptomics features metadata dataset.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/import_fmetadata_csv_factory.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Target factory for csv features metadata import — import_fmetadata_csv_factory","text":"","code":"if (FALSE) { ## in the _targets.R library(moiraine)  list(   import_fmetadata_csv_factory(     c(       \"data/genotype_fmetadata.csv\",       \"data/rnaseq_fmetadata.csv\"     ),     col_ids = c(\"Marker\", \"Info\"),     target_name_suffixes = c(\"geno\", \"transcripto\")   ) ) }"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/import_fmetadata_gff.html","id":null,"dir":"Reference","previous_headings":"","what":"Import features metadata from a GFF/GTF file — import_fmetadata_gff","title":"Import features metadata from a GFF/GTF file — import_fmetadata_gff","text":"Reads GFF GTF annotation file returns dataframe rows correspond features (e.g. genes transcripts) columns correspond information features. Non-ASCII characters replaced ASCII equivalents (using stringi textclean packages).","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/import_fmetadata_gff.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Import features metadata from a GFF/GTF file — import_fmetadata_gff","text":"","code":"import_fmetadata_gff(file, feature_type, add_fields = NULL)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/import_fmetadata_gff.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Import features metadata from a GFF/GTF file — import_fmetadata_gff","text":"file Character, path dataset GFF GTF file. feature_type Character, type feature extract annotation file. Currently supports 'genes' 'transcripts'. add_fields Character vector, fields GFF/GTF file extract imported default (use run function realised fields extracted function).","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/import_fmetadata_gff.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Import features metadata from a GFF/GTF file — import_fmetadata_gff","text":"data-frame features rows features information columns. Feature IDs used row names.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/import_fmetadata_gff.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Import features metadata from a GFF/GTF file — import_fmetadata_gff","text":"","code":"if (FALSE) { import_fmetadata_gff(   \"bos_taurus_gene_model.gff3\",   \"genes\",   add_fields = c(\"name\", \"description\") ) }"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/import_fmetadata_gff_factory.html","id":null,"dir":"Reference","previous_headings":"","what":"Target factory for GFF/GTF features metadata import — import_fmetadata_gff_factory","title":"Target factory for GFF/GTF features metadata import — import_fmetadata_gff_factory","text":"Creates list targets track file import features metadata GFF/GTF file.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/import_fmetadata_gff_factory.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Target factory for GFF/GTF features metadata import — import_fmetadata_gff_factory","text":"","code":"import_fmetadata_gff_factory(   files,   feature_types,   add_fieldss,   target_name_suffixes )"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/import_fmetadata_gff_factory.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Target factory for GFF/GTF features metadata import — import_fmetadata_gff_factory","text":"files Character vector, vector paths samples metadata GFF GTF files. feature_types Character vector, type features extract annotation file. Currently supports 'genes' 'transcripts'. add_fieldss List, element character vector  field names GFF/GTF file extract imported default. character vector provided, used files read . target_name_suffixes Character vector, suffix add name targets created target factory dataset.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/import_fmetadata_gff_factory.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Target factory for GFF/GTF features metadata import — import_fmetadata_gff_factory","text":"list target objects. example, two files import target_name_suffixes = c(\"geno\", \"transcripto\"), factory returns following targets: fmetadata_file_geno fmetadata_file_transcripto: targets tracking genomics transcriptomics annotation files, respectively. fmetadata_geno fmetadata_transcripto: targets import genomics transcriptomics features metadata datasets, respectively.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/import_fmetadata_gff_factory.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Target factory for GFF/GTF features metadata import — import_fmetadata_gff_factory","text":"","code":"if (FALSE) { ## in the _targets.R library(moiraine)  list(   import_fmetadata_gff_factory(     c(       \"data/annotation.gff\",       \"data/annotationv2.gtf\"     ),     feature_types = c(\"genes\", \"transcripts\"),     add_fieldss = list(       c(\"gene_name\", \"gene_custom_ID\"),       c(\"transcript_name\")     ),     target_name_suffixes = c(\"geno\", \"transcripto\")   ) ) }"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/import_smetadata_csv.html","id":null,"dir":"Reference","previous_headings":"","what":"Import samples metadata from a csv file — import_smetadata_csv","title":"Import samples metadata from a csv file — import_smetadata_csv","text":"Reads csv file returns dataframe rows correspond features (e.g. markers, genes, phenotypes...) columns correspond information features.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/import_smetadata_csv.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Import samples metadata from a csv file — import_smetadata_csv","text":"","code":"import_smetadata_csv(file, col_id, ...)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/import_smetadata_csv.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Import samples metadata from a csv file — import_smetadata_csv","text":"file Character, path dataset csv file. col_id Character, name column file contains ID rows (.e. sample IDs). ... arguments passed readr::read_csv().","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/import_smetadata_csv.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Import samples metadata from a csv file — import_smetadata_csv","text":"data-frame samples rows samples properties columns. Sample IDs used rownames.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/import_smetadata_csv.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Import samples metadata from a csv file — import_smetadata_csv","text":"","code":"if (FALSE) { samples_information <- import_smetadata_csv(   \"samples_information.csv\",   col_id = \"Sample\" ) }"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/import_smetadata_csv_factory.html","id":null,"dir":"Reference","previous_headings":"","what":"Target factory for csv samples metadata import — import_smetadata_csv_factory","title":"Target factory for csv samples metadata import — import_smetadata_csv_factory","text":"Creates list targets track file import samples metadata csv file.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/import_smetadata_csv_factory.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Target factory for csv samples metadata import — import_smetadata_csv_factory","text":"","code":"import_smetadata_csv_factory(files, col_ids, target_name_suffixes)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/import_smetadata_csv_factory.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Target factory for csv samples metadata import — import_smetadata_csv_factory","text":"files Character vector, vector paths samples metadata csv files. col_ids Character vector, name column file contains ID rows (.e. sample IDs). target_name_suffixes Character vector, suffix add name targets created target factory dataset.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/import_smetadata_csv_factory.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Target factory for csv samples metadata import — import_smetadata_csv_factory","text":"list target objects. example, two files import target_name_suffixes = c(\"geno\", \"transcripto\"), factory returns following targets: smetadata_file_geno smetadata_file_transcripto: targets tracking genomics transcriptomics samples metadata files, respectively. smetadata_geno smetadata_transcripto: targets import genomics transcriptomics samples metadata datasets, respectively.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/import_smetadata_csv_factory.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Target factory for csv samples metadata import — import_smetadata_csv_factory","text":"","code":"if (FALSE) { ## in the _targets.R library(moiraine)  list(   import_smetadata_csv_factory(     c(       \"data/genotype_smetadata.csv\",       \"data/rnaseq_smetadata.csv\"     ),     col_ids = c(\"Sample\", \"SampleIDs\"),     target_name_suffixes = c(\"geno\", \"transcripto\")   ) ) }"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/is_equal_or_null.html","id":null,"dir":"Reference","previous_headings":"","what":"Check null or equality — is_equal_or_null","title":"Check null or equality — is_equal_or_null","text":"Tests whether object NULL equal value.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/is_equal_or_null.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check null or equality — is_equal_or_null","text":"","code":"is_equal_or_null(x, val)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/is_equal_or_null.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check null or equality — is_equal_or_null","text":"x object test. val Value compare ","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/is_equal_or_null.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check null or equality — is_equal_or_null","text":"TRUE FALSE.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/join_features_metadata.html","id":null,"dir":"Reference","previous_headings":"","what":"Join feature metadata to table — join_features_metadata","title":"Join feature metadata to table — join_features_metadata","text":"Adds features metadata information table containing feature IDs.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/join_features_metadata.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Join feature metadata to table — join_features_metadata","text":"","code":"join_features_metadata(df, mo_data)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/join_features_metadata.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Join feature metadata to table — join_features_metadata","text":"df Data-frame tibble column feature_id containing feature IDs. mo_data MultiDataSet::MultiDataSet object.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/join_features_metadata.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Join feature metadata to table — join_features_metadata","text":"df table additional columns containing information features features metadata table.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/join_samples_metadata.html","id":null,"dir":"Reference","previous_headings":"","what":"Join samples metadata to table — join_samples_metadata","title":"Join samples metadata to table — join_samples_metadata","text":"Adds samples metadata information table containing sample IDs.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/join_samples_metadata.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Join samples metadata to table — join_samples_metadata","text":"","code":"join_samples_metadata(df, mo_data, datasets = NULL)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/join_samples_metadata.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Join samples metadata to table — join_samples_metadata","text":"df Data-frame tibble column id containing sample IDs. mo_data MultiDataSet::MultiDataSet object. datasets Character vector, name(s) datasets samples metadata extracted. NULL (default value), information datasets used.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/join_samples_metadata.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Join samples metadata to table — join_samples_metadata","text":"df table additional columns containing information samples samples metadata table.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/make_feature_sets_from_df.html","id":null,"dir":"Reference","previous_headings":"","what":"Makes list of feature sets from data-frame — make_feature_sets_from_df","title":"Makes list of feature sets from data-frame — make_feature_sets_from_df","text":"Creates list feature sets annotation data-frame.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/make_feature_sets_from_df.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Makes list of feature sets from data-frame — make_feature_sets_from_df","text":"","code":"make_feature_sets_from_df(annotation_df, col_id, col_set)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/make_feature_sets_from_df.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Makes list of feature sets from data-frame — make_feature_sets_from_df","text":"annotation_df data-frame feature annotation long format, least column feature ID column giving set feature belongs. feature belongs one set, row sets. col_id Character, name column annotation_df data-frame contains features ID. col_set Character, name column annotation_df data-frame contains sets ID.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/make_feature_sets_from_df.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Makes list of feature sets from data-frame — make_feature_sets_from_df","text":"named list, element corresponds set, contains vector features ID features belonging set.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/make_feature_sets_from_fm.html","id":null,"dir":"Reference","previous_headings":"","what":"Makes list of feature sets from features metadata — make_feature_sets_from_fm","title":"Makes list of feature sets from features metadata — make_feature_sets_from_fm","text":"Creates list feature sets features metadata.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/make_feature_sets_from_fm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Makes list of feature sets from features metadata — make_feature_sets_from_fm","text":"","code":"make_feature_sets_from_fm(mo_data, col_names, combine_omics_sets = FALSE)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/make_feature_sets_from_fm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Makes list of feature sets from features metadata — make_feature_sets_from_fm","text":"mo_data MultiDataSet-class object. col_names Named list character, one element per dataset feature sets generated. name element correspond name dataset, value column name feature metadata corresponding dataset use set ID. combine_omics_sets Logical, can sets contain features different omics datasets? FALSE (default), feature sets created omics separately. two sets different omics ID, made unique.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/make_feature_sets_from_fm.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Makes list of feature sets from features metadata — make_feature_sets_from_fm","text":"named list, element corresponds set, contains vector features ID features belonging set.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/mofa_get_weights.html","id":null,"dir":"Reference","previous_headings":"","what":"Get feature weights from MOFA object — mofa_get_weights","title":"Get feature weights from MOFA object — mofa_get_weights","text":"Extracts feature weights trained MOFA MEFISTO model (MOFA2 package).","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/mofa_get_weights.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get feature weights from MOFA object — mofa_get_weights","text":"","code":"mofa_get_weights(   object,   views = \"all\",   factors = \"all\",   abs = FALSE,   scale = \"none\",   as.data.frame = TRUE )"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/mofa_get_weights.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get feature weights from MOFA object — mofa_get_weights","text":"object trained MOFA object. views Character integer vector, name index views (.e. datasets) feature weights extracted. Default value \"\", .e. datasets considered. factors Character integer vector, name index factors feature weights extracted. Default value \"\", .e. factors considered. abs Logical, absolute value weights returned? Default value FALSE. scale Character, type scaling performed feature weights. Possible values 'none', 'by_view', 'by_factor' 'overall' (see Details). Default value 'none'. .data.frame Logical, whether function return long data-frame instead list matrices. Default value TRUE.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/mofa_get_weights.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get feature weights from MOFA object — mofa_get_weights","text":"default, returns tibble columns view, feature, factor, value. Alternatively, .data.frame = FALSE, returns list matrices, one per view, features rows factors columns.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/mofa_get_weights.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Get feature weights from MOFA object — mofa_get_weights","text":"Scaling options: scale = 'none': scaling performed; scale = 'by_view': weights divided maximum absolute weight corresponding view/dataset; scale = 'by_factor': weights divided maximum absolute weight corresponding factor; scale = 'overall': weights divided maximum absolute weight across views/datasets factors considered.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/mofa_plot_cor_covariates.html","id":null,"dir":"Reference","previous_headings":"","what":"Plots the correlation between factors and covariates for MOFA — mofa_plot_cor_covariates","title":"Plots the correlation between factors and covariates for MOFA — mofa_plot_cor_covariates","text":"Plots Pearson correlation MOFA latent factors covariates (obtained samples metadata). function provides ggplot2 version plot created correlate_factors_with_covariates (plot parameter set 'r').","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/mofa_plot_cor_covariates.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plots the correlation between factors and covariates for MOFA — mofa_plot_cor_covariates","text":"","code":"mofa_plot_cor_covariates(   mofa_output,   covariates = NULL,   show_cor = TRUE,   min_show_cor = 0.2,   round_cor = 2,   factor_as_col = TRUE )"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/mofa_plot_cor_covariates.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plots the correlation between factors and covariates for MOFA — mofa_plot_cor_covariates","text":"mofa_output output run_mofa. covariates Character vector, covariates use plot. NULL, covariates retrieved via colnames(MOFA2::samples_metadata(mofa_output)) (except group, id sample) used. Default value NULL. show_cor Logical, correlation values added plot? Default value TRUE. min_show_cor Numeric, minimum value correlation coefficients values added plot (.e. circle appear values text). Ignored show_cor FALSE. Default value 0.2. round_cor Integer, many decimal places show correlation coefficients. Ignored show_cor FALSE. Default value 2. factor_as_col Logical, factors represented columns? FALSE, represented rows instead. Default value TRUE.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/mofa_plot_cor_covariates.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plots the correlation between factors and covariates for MOFA — mofa_plot_cor_covariates","text":"ggplot.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/n_features.html","id":null,"dir":"Reference","previous_headings":"","what":"Number of features in each dataset of MultiDataSet object — n_features","title":"Number of features in each dataset of MultiDataSet object — n_features","text":"Gives number features dataset MultiDataSet object.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/n_features.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Number of features in each dataset of MultiDataSet object — n_features","text":"","code":"n_features(mo_data)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/n_features.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Number of features in each dataset of MultiDataSet object — n_features","text":"mo_data MultiDataSet::MultiDataSet object.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/n_features.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Number of features in each dataset of MultiDataSet object — n_features","text":"named integer vector, element number features corresponding dataset.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/n_samples.html","id":null,"dir":"Reference","previous_headings":"","what":"Number of samples in each dataset of MultiDataSet object — n_samples","title":"Number of samples in each dataset of MultiDataSet object — n_samples","text":"Gives number samples dataset MultiDataSet object.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/n_samples.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Number of samples in each dataset of MultiDataSet object — n_samples","text":"","code":"n_samples(mo_data)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/n_samples.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Number of samples in each dataset of MultiDataSet object — n_samples","text":"mo_data MultiDataSet::MultiDataSet object.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/n_samples.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Number of samples in each dataset of MultiDataSet object — n_samples","text":"named integer vector, element number sample corresponding dataset.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/options_list_as_tibble.html","id":null,"dir":"Reference","previous_headings":"","what":"Returns options list as a tibble — options_list_as_tibble","title":"Returns options list as a tibble — options_list_as_tibble","text":"Transforms list options (parameters) tibble name options (parameters) one column, value second column. Vector values collapsed span one column.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/options_list_as_tibble.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Returns options list as a tibble — options_list_as_tibble","text":"","code":"options_list_as_tibble(options_list)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/options_list_as_tibble.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Returns options list as a tibble — options_list_as_tibble","text":"options_list named list, element corresponds one option parameter name element corresponds name option/parameter.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/options_list_as_tibble.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Returns options list as a tibble — options_list_as_tibble","text":"tibble, Parameter column giving list options parameters, Value column giving values corresponding option parameter.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/pca_complete_data_factory.html","id":null,"dir":"Reference","previous_headings":"","what":"Target factory for PCA run and missing values imputation on each omics\ndataset — pca_complete_data_factory","title":"Target factory for PCA run and missing values imputation on each omics\ndataset — pca_complete_data_factory","text":"Creates list targets perform PCA run omics dataset MultiDataSet object using dynamic branching, imputes missing values datasets using results PCA runs.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/pca_complete_data_factory.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Target factory for PCA run and missing values imputation on each omics\ndataset — pca_complete_data_factory","text":"","code":"pca_complete_data_factory(   mo_data_target,   dataset_names = NULL,   target_name_prefix = \"\",   complete_data_name = NULL,   ... )"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/pca_complete_data_factory.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Target factory for PCA run and missing values imputation on each omics\ndataset — pca_complete_data_factory","text":"mo_data_target Symbol, name target containing MultiDataSet object. dataset_names Character vector, names datasets PCA run. NULL, PCA run datasets. Default value NULL. target_name_prefix Character, prefix add name targets created factory. Default value \"\". complete_data_name Character, name target containing MultiDataSet missing data imputed created. NULL, selected automatically. Default value NULL. ... arguments passed run_pca_matrix() function.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/pca_complete_data_factory.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Target factory for PCA run and missing values imputation on each omics\ndataset — pca_complete_data_factory","text":"List targets. target_name_prefix = \"\" complete_data_name = NULL, following targets created: dataset_names_pca: target containing character vector gives names datasets PCA run. dataset_mats_pca: dynamic branching target applies get_dataset_matrix() function dataset specified dataset_names. results saved list. Note using dynamic branching, names list meaningful. Rather, use sapply(pca_pca_runs_listruns_list, attr, \"dataset_name\") assess element list corresponds omics dataset. pca_runs_list: dynamic branching target applies run_pca_matrix() function matrix dataset_mats_pca. results saved list. Note using dynamic branching, names list meaningful. Rather, use sapply(pca_runs_list, attr, \"dataset_name\") assess element list corresponds omics dataset. complete_set: target returns MultiDataSet missing values imputed.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/pca_complete_data_factory.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Target factory for PCA run and missing values imputation on each omics\ndataset — pca_complete_data_factory","text":"","code":"if (FALSE) { ## in the _targets.R library(moiraine) list(   # ... code for importing datasets etc    ## mo_set is the target containing the MultiDataSet object   ## Example 1: running a PCA on all datasets   run_pca_factory(mo_set),    ## Example 2: running a PCA on 'rnaseq' and 'metabolome' datasets   run_pca_factory(     mo_set,     c(\"rnaseq\", \"metabolome\"),     complete_data_name = \"mo_data_complete\"   ) ) }"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/perf_splsda.html","id":null,"dir":"Reference","previous_headings":"","what":"Assess optimal number of components for sPLS-DA on omics dataset from MultiDataSet object — perf_splsda","title":"Assess optimal number of components for sPLS-DA on omics dataset from MultiDataSet object — perf_splsda","text":"Performs cross-validation PLS-DA run (implemented mixOmics package) omics dataset MultiDataSet object. allows estimate optimal number latent components construct. intended feature preselection omics dataset (see examples ).","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/perf_splsda.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Assess optimal number of components for sPLS-DA on omics dataset from MultiDataSet object — perf_splsda","text":"","code":"perf_splsda(   splsda_input,   ncomp_max = 5,   validation = \"Mfold\",   folds = 5,   nrepeat = 50,   measure = \"BER\",   distance = \"centroids.dist\",   cpus = 1,   progressBar = TRUE )"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/perf_splsda.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Assess optimal number of components for sPLS-DA on omics dataset from MultiDataSet object — perf_splsda","text":"splsda_input Input sPLS-DA functions mixOmics, created get_input_splsda(). ncomp_max Integer, maximum number latent components test estimating number latent components use. Default value 5. validation Character, cross-validation method use, can one \"Mfold\" \"loo\" (see mixOmics::perf()). Default value \"Mfold\". folds Integer, number folds use M-fold cross-validation (see mixOmics::perf()). Default value 5. nrepeat Integer, number times cross-validation repeated (see mixOmics::perf()). measure Performance measure used select optimal value ncomp, can one \"BER\" \"overall\" (see mixOmics::perf()). Default value \"BER\". distance Distance metric used select optimal value ncomp, can one \"max.dist\", \"centroids.dist\" \"mahalanobis.dist\" (see mixOmics::perf()). Default value \"centroids.dist\". cpus Integer, number cpus use. progressBar Logical, whether display progress bar optimisation ncomp. Default value TRUE.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/perf_splsda.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Assess optimal number of components for sPLS-DA on omics dataset from MultiDataSet object — perf_splsda","text":"list per output mixOmics::perf() function, following additional elements: dataset_name: name dataset analysed; group: column name samples information data-frame used samples group; optim_ncomp: optimal number latent components per measure distance specified; optim_measure: measure used select optimal number latent components; optim_distance: distance metric used select optimal number latent components. addition, name dataset analysed column name samples information data-frame used samples group stored attributes dataset_name group, respectively.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/perf_splsda.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Assess optimal number of components for sPLS-DA on omics dataset from MultiDataSet object — perf_splsda","text":"function uses plsda  perf function mixOmics package.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_correlation_matrix.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot correlation matrix — plot_correlation_matrix","title":"Plot correlation matrix — plot_correlation_matrix","text":"Plots correlation matrix using corrplot package.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_correlation_matrix.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot correlation matrix — plot_correlation_matrix","text":"","code":"plot_correlation_matrix(cormat, ...)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_correlation_matrix.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot correlation matrix — plot_correlation_matrix","text":"cormat correlation matrix. ... arguments passed corrplot::corrplot() function.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_correlation_matrix.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot correlation matrix — plot_correlation_matrix","text":"correlation plot","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_correlation_matrix_full.html","id":null,"dir":"Reference","previous_headings":"","what":"Plots a full correlation matrix (corrplot-style) — plot_correlation_matrix_full","title":"Plots a full correlation matrix (corrplot-style) — plot_correlation_matrix_full","text":"Generates plot correlation matrix style corrplot package.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_correlation_matrix_full.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plots a full correlation matrix (corrplot-style) — plot_correlation_matrix_full","text":"","code":"plot_correlation_matrix_full(   mat,   rows_title = NULL,   cols_title = NULL,   title = NULL,   show_cor = TRUE,   min_show_cor = 0.2,   round_cor = 2 )"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_correlation_matrix_full.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plots a full correlation matrix (corrplot-style) — plot_correlation_matrix_full","text":"mat Correlation matrix plot. rows_title Character, title rows. Default value NULL. cols_title Character, title cols. Default value NULL. title Character, title plot. Default value NULL. show_cor Logical, correlation values added plot? Default value TRUE. min_show_cor Numeric, minimum value correlation coefficients values added plot (.e. circle appear values text). Ignored show_cor FALSE. Default value 0.2. round_cor Integer, many decimal places show correlation coefficients. Ignored show_cor FALSE. Default value 2.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_correlation_matrix_full.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plots a full correlation matrix (corrplot-style) — plot_correlation_matrix_full","text":"ggplot.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_data_covariate.html","id":null,"dir":"Reference","previous_headings":"","what":"Plots omics data vs sample covariate — plot_data_covariate","title":"Plots omics data vs sample covariate — plot_data_covariate","text":"given set features, plots value sample covariate samples metadata. Depending whether covariate continuous discrete, generate either scatterplot violin plot.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_data_covariate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plots omics data vs sample covariate — plot_data_covariate","text":"","code":"plot_data_covariate(   mo_data,   covariate,   features,   samples = NULL,   only_common_samples = FALSE,   colour_by = NULL,   shape_by = NULL,   point_alpha = 1,   add_se = TRUE,   add_boxplot = TRUE,   ncol = NULL,   label_cols = NULL,   truncate = NULL )"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_data_covariate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plots omics data vs sample covariate — plot_data_covariate","text":"mo_data MultiDataSet::MultiDataSet object. covariate Character, name column one samples metadata tables mo_data use x-axis plot. features Character vector, ID features show plot. samples Character vector, ID samples include plot. NULL (default), samples corresponding dataset used. only_common_samples Logical, whether samples present datasets plotted. Default value FALSE. colour_by Character, name column one samples metadata tables mo_data use colour observations plot. Default value NULL. shape_by Character, name column one samples metadata tables mo_data use shape observations plot. point_alpha Numeric 0 1, opacity points plot (1 = fully opaque, 0 = fully transparent). Default value 1. add_se Logical, confidence interval drawn around smoothing curves numerical covariates? Default value TRUE. add_boxplot Logical, boxplot drawn top points categorical covariates? Default value TRUE. ncol Integer, number columns faceted plot. Default value NULL. label_cols Character named list character, giving dataset name column corresponding features metadata use label. one value, used datasets. list, names must correspond names datasets mo_data. dataset missing list value provided, feature IDs used labels. Alternatively, use feature_id get feature IDs labels. truncate Integer, width labels truncated (avoid issues long labels plots). NULL (default value), truncation performed.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_data_covariate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plots omics data vs sample covariate — plot_data_covariate","text":"ggplot.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_data_covariate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plots omics data vs sample covariate — plot_data_covariate","text":"","code":"if (FALSE) { ## Selecting at random 3 features from each dataset random_features <- get_features(mo_set) |>   map(sample, size = 3, replace = FALSE) |>   unlist() |>   unname()  ## Plotting features value against a discrete samples covariate plot_data_covariate(   mo_set,   \"feedlot\",   random_features,   only_common_samples = TRUE,   colour_by = \"status\",   shape_by = \"geno_comp_cluster\" )  ## Plotting features value against a continuous samples covariate plot_data_covariate(   mo_set,   \"day_on_feed\",   random_features,   only_common_samples = TRUE,   colour_by = \"status\",   shape_by = \"geno_comp_cluster\" ) }"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_data_heatmap.html","id":null,"dir":"Reference","previous_headings":"","what":"Plots omics data as heatmap — plot_data_heatmap","title":"Plots omics data as heatmap — plot_data_heatmap","text":"given set features, plots value across samples heatmap.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_data_heatmap.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plots omics data as heatmap — plot_data_heatmap","text":"","code":"plot_data_heatmap(   mo_data,   features,   center = FALSE,   scale = FALSE,   samples = NULL,   only_common_samples = FALSE,   samples_info = NULL,   features_info = NULL,   colours_list = NULL,   label_cols = NULL,   truncate = NULL,   legend_title_size = 10,   legend_text_size = 10,   legend_ncol = 1,   ... )"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_data_heatmap.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plots omics data as heatmap — plot_data_heatmap","text":"mo_data MultiDataSet::MultiDataSet object. features Character vector, ID features show plot. center Logical, whether data centered (feature-wise). Default value FALSE. scale Logical, whether data scaled (feature-wise). Default value FALSE. samples Character vector, ID samples include plot. NULL (default), samples used. only_common_samples Logical, whether samples present datasets plotted. Default value FALSE. samples_info Character vector, column names samples metadata tables datasets represented plot samples annotation. features_info Named list character vectors, element corresponds dataset, gives column names features metadata dataset represented plot features annotation. names list must correspond dataset names mo_data object. colours_list Named list, element gives colour palette use samples features annotation. Names must match values samples_info vector elements features_info list. continuous palettes, must use circlize::colorRamp2() function (see ComplexHeatmap reference book). label_cols Character named list character, giving dataset name column corresponding features metadata use label. one value, used datasets. list, names must correspond names datasets mo_data. dataset missing list value provided, feature IDs used labels. Alternatively, use feature_id get feature IDs labels. truncate Integer, width labels truncated (avoid issues long labels plots). NULL (default value), truncation performed. legend_title_size Integer, size points legend title. legend_text_size Integer, size points legend elements text. legend_ncol Integer, number columns legend. Default value 1. ... Additional arguments passed ComplexHeatmap::Heatmap() function.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_data_heatmap.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plots omics data as heatmap — plot_data_heatmap","text":"ComplexHeatmap::Heatmap object.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_data_heatmap.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plots omics data as heatmap — plot_data_heatmap","text":"","code":"if (FALSE) { ## Selecting at random 3 features from each dataset random_features <- get_features(mo_set) |>   map(sample, size = 3, replace = FALSE) |>   unlist() |>   unname()  plot_data_heatmap(   mo_set,   random_features,   center = TRUE,   scale = TRUE,   show_column_names = FALSE,   only_common_samples = TRUE,   samples_info = c(\"status\", \"day_on_feed\"),   features_info = c(\"chromosome\"),   colours_list = list(     \"status\" = c(\"Control\" = \"gold\", \"BRD\" = \"navyblue\"),     \"day_on_feed\" = colorRamp2(c(5, 65), c(\"white\", \"pink3\"))   ),   label_cols = list(      \"rnaseq\" = \"Name\",     \"metabolome\" = \"name\"   )  ) }"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_density_data.html","id":null,"dir":"Reference","previous_headings":"","what":"Per-dataset density plot for MultiDataSet object — plot_density_data","title":"Per-dataset density plot for MultiDataSet object — plot_density_data","text":"Displays density plot values dataset MultiDataSet object.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_density_data.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Per-dataset density plot for MultiDataSet object — plot_density_data","text":"","code":"plot_density_data(   mo_data,   datasets = names(mo_data),   combined = TRUE,   scales = \"fixed\" )"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_density_data.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Per-dataset density plot for MultiDataSet object — plot_density_data","text":"mo_data MultiDataSet::MultiDataSet object. datasets Character vector, names datasets include plot. default, datasets included. combined Logical, different datasets represented plot? FALSE (default value), dataset represented subplot. Default value TRUE. scales Character, axes plotted combined = FALSE. Can either 'fixed', .e. limits applied axes subplot; 'free', .e. axis limits adapted subplot. Ignored combined = TRUE. Default value 'fixed.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_density_data.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Per-dataset density plot for MultiDataSet object — plot_density_data","text":"ggplot.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_feature_preselection_cov.html","id":null,"dir":"Reference","previous_headings":"","what":"Diagnostics plots for COV-based feature preselection — plot_feature_preselection_cov","title":"Diagnostics plots for COV-based feature preselection — plot_feature_preselection_cov","text":"Displays COV distribution across features original (.e. non-filtered) datasets, vertical red line showing cut-used preselection function.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_feature_preselection_cov.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Diagnostics plots for COV-based feature preselection — plot_feature_preselection_cov","text":"","code":"plot_feature_preselection_cov(cov_list)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_feature_preselection_cov.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Diagnostics plots for COV-based feature preselection — plot_feature_preselection_cov","text":"cov_list list result COV calculation dataset filtered, returned select_features_cov function.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_feature_preselection_cov.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Diagnostics plots for COV-based feature preselection — plot_feature_preselection_cov","text":"ggplot.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_feature_preselection_mad.html","id":null,"dir":"Reference","previous_headings":"","what":"Diagnostics plots for MAD-based feature preselection — plot_feature_preselection_mad","title":"Diagnostics plots for MAD-based feature preselection — plot_feature_preselection_mad","text":"Displays MAD distribution across features original (.e. non-filtered) datasets, vertical red line showing cut-used preselection function.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_feature_preselection_mad.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Diagnostics plots for MAD-based feature preselection — plot_feature_preselection_mad","text":"","code":"plot_feature_preselection_mad(mad_list)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_feature_preselection_mad.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Diagnostics plots for MAD-based feature preselection — plot_feature_preselection_mad","text":"mad_list list result MAD calculation dataset filtered, returned select_features_mad function.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_feature_preselection_mad.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Diagnostics plots for MAD-based feature preselection — plot_feature_preselection_mad","text":"ggplot.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_feature_preselection_splsda.html","id":null,"dir":"Reference","previous_headings":"","what":"Diagnostics plots for sPLS-DA-based feature preselection — plot_feature_preselection_splsda","title":"Diagnostics plots for sPLS-DA-based feature preselection — plot_feature_preselection_splsda","text":"Displays PLS-DA classification performance across different number latent components prefiltered dataset. classification error rates computed different measures (column facets) different distance metrics (colours). vertical grey bar represents dataset number latent components selected feature preselection step. addition, circle highlights measure distance metric used select number latent component.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_feature_preselection_splsda.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Diagnostics plots for sPLS-DA-based feature preselection — plot_feature_preselection_splsda","text":"","code":"plot_feature_preselection_splsda(   perf_splsda_res,   measure = NULL,   distance = NULL )"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_feature_preselection_splsda.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Diagnostics plots for sPLS-DA-based feature preselection — plot_feature_preselection_splsda","text":"perf_splsda_res list result perf_splsda dataset filtered. measure measure(s) displayed? Can one \"BER\" \"overall\". NULL, measures displayed. Default value NULL. distance measure(s) displayed? Can one \"max.dist\", \"centroids.dist\" \"mahalanobis.dist\". NULL, measures displayed. Default value NULL.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_feature_preselection_splsda.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Diagnostics plots for sPLS-DA-based feature preselection — plot_feature_preselection_splsda","text":"ggplot.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_features_weight_covariate.html","id":null,"dir":"Reference","previous_headings":"","what":"Plots features weight against covariate — plot_features_weight_covariate","title":"Plots features weight against covariate — plot_features_weight_covariate","text":"Plots features weight importance result integration method covariate features metadata.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_features_weight_covariate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plots features weight against covariate — plot_features_weight_covariate","text":"","code":"plot_features_weight_covariate(   method_output,   mo_data,   covariate,   features_metric = c(\"signed_importance\", \"weight\", \"importance\"),   remove_null_weight = FALSE,   latent_dimensions = NULL,   colour_by = NULL,   shape_by = NULL,   point_alpha = 0.5,   add_se = TRUE,   add_boxplot = TRUE,   scales = \"free_x\" )"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_features_weight_covariate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plots features weight against covariate — plot_features_weight_covariate","text":"method_output Integration method output generated via get_output() function. mo_data MultiDataSet object (used extract samples information). covariate Character named list character, giving dataset name column corresponding features metadata use x-axis plot. one value, used datasets. list, names must correspond names datasets mo_data. dataset present list, excluded plot. features_metric Character, features metric plotted y-axis. one 'signed_importance' (default value), 'weight' 'importance'. remove_null_weight Logical, features null weight/importance removed plot? Default value FALSE. latent_dimensions Character vector giving latent dimensions display. Default value NULL, .e. latent dimensions shown. colour_by Character named list character, giving dataset name column corresponding feature metadata use colour features plot. one value, used datasets. list, names must correspond names datasets covariate. Default value NULL. shape_by Character named list character, giving dataset name column corresponding feature metadata use shape features plot. one value, used datasets. list, names must correspond names datasets covariate. Default value NULL. point_alpha Numeric 0 1, opacity points plot (1 = fully opaque, 0 = fully transparent). Default value 0.5. add_se Logical, confidence interval drawn around smoothing curves numerical covariates? Default value TRUE. add_boxplot Logical, boxplot drawn top points categorical covariates? Default value TRUE. scales Character, value use scales argument ggplot2::facet_grid(). Default value 'free_x'.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_features_weight_covariate.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Plots features weight against covariate — plot_features_weight_covariate","text":"covariate numeric, function creates scatter plot, loess curve summarise trend covariate features weight. colour_by used, corresponding variable numeric, loess curve take account variable. instead colour_by variable character factor, loess curve fitted separately category. covariate numeric, function creates violin/boxplot. colour_by used, corresponding variable numeric, violins boxplots take account variable. instead colour_by variable character factor, separate violin boxplot drawn category.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_features_weight_distr.html","id":null,"dir":"Reference","previous_headings":"","what":"Plots features weight distribution — plot_features_weight_distr","title":"Plots features weight distribution — plot_features_weight_distr","text":"Plots features weight importance per dataset latent dimension.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_features_weight_distr.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plots features weight distribution — plot_features_weight_distr","text":"","code":"plot_features_weight_distr(   method_output,   latent_dimensions = NULL,   datasets = NULL,   features_metric = c(\"signed_importance\", \"weight\", \"importance\"),   top_n = 0,   mo_data = NULL,   label_cols = NULL,   truncate = NULL,   text_size = 2.5 )"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_features_weight_distr.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plots features weight distribution — plot_features_weight_distr","text":"method_output Integration method output generated via get_output() function. latent_dimensions Character vector giving latent dimensions display. Default value NULL, .e. latent dimensions shown. datasets Character vector giving datasets display. Default value NULL, .e. datasets shown. features_metric Character, attribute plotted: can 'signed_importance' (.e. importance value weight sign), 'importance' 'weight'. Default value 'signed_importance'. top_n Integer, number top features (terms importance) label shown. Default value 0. mo_data MultiDataSet object. used label_cols NULL. label_cols Character named list character, giving dataset name column corresponding features metadata use label. one value, used datasets. list, names must correspond names datasets mo_data. dataset missing list value provided, feature IDs used labels. Alternatively, use feature_id get feature IDs labels. truncate Integer, width labels truncated (avoid issues long labels plots). NULL (default value), truncation performed. text_size Numeric, size feature labels.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_features_weight_distr.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plots features weight distribution — plot_features_weight_distr","text":"patchwork plots.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_features_weight_pair.html","id":null,"dir":"Reference","previous_headings":"","what":"Plots features weight as a scatterplot — plot_features_weight_pair","title":"Plots features weight as a scatterplot — plot_features_weight_pair","text":"Plots features weight pair latent dimensions one two dimension reduction analysis scatterplot.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_features_weight_pair.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plots features weight as a scatterplot — plot_features_weight_pair","text":"","code":"plot_features_weight_pair(   method_output,   latent_dimensions,   datasets = NULL,   features_metric = c(\"signed_importance\", \"weight\", \"importance\"),   include_missing_features = TRUE,   top_n = 5,   metric = \"geometric\",   label_cols = NULL,   mo_data = NULL,   truncate = NULL,   ncol = NULL,   label_size = 3 )"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_features_weight_pair.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plots features weight as a scatterplot — plot_features_weight_pair","text":"method_output single Integration method output generated via get_output() function, list two integration method outputs. latent_dimensions Character vector length 2 (method_output single output object), named list length 2 (method_output list two output objects). first case, gives name two latent dimensions represented. second case, names list correspond names methods, values character giving corresponding method name latent dimension display. datasets Character vector, names datasets features weight plotted. Default value NULL, .e. relevant datasets shown. features_metric Character, features metric plotted y-axis. one 'signed_importance' (default value), 'weight' 'importance'. include_missing_features Logical, whether show features input one method , comparing results two different integration methods. Default value TRUE. top_n Integer, number top features (according consensus importance metric) highlight plot. Default value 5. metric Character, one metrics use compute consensus score. Can one 'min', 'max', 'average', 'product', 'l2' (L2-norm), 'geometric' (geometric mean) 'harmonic' (harmonic mean). Default value 'geometric'. Names must match output_list. label_cols Character named list character, giving dataset name column corresponding features metadata use label. one value, used datasets. list, names must correspond names datasets mo_data. dataset missing list value provided, feature IDs used labels. Alternatively, use feature_id get feature IDs labels. mo_data MultiDataSet object. used label_cols NULL. truncate Integer, width labels truncated (avoid issues long labels plots). NULL (default value), truncation performed. ncol Integer, number columns datasets combined plot. Default value NULL, .e. picked automatically. label_size Integer, size features label. Default value 3.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_features_weight_pair.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plots features weight as a scatterplot — plot_features_weight_pair","text":"ggplot.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_features_weight_set.html","id":null,"dir":"Reference","previous_headings":"","what":"Plots features weight in/not in a set — plot_features_weight_set","title":"Plots features weight in/not in a set — plot_features_weight_set","text":"Plots distribution features weight integration method, depending whether features belong feature set interest.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_features_weight_set.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plots features weight in/not in a set — plot_features_weight_set","text":"","code":"plot_features_weight_set(   method_output,   feature_set,   set_name = \"set\",   features_metric = c(\"signed_importance\", \"weight\", \"importance\"),   add_missing_features = FALSE,   mo_data = NULL,   datasets = NULL,   latent_dimensions = NULL,   point_alpha = 0.5,   add_boxplot = TRUE,   scales = \"free_x\" )"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_features_weight_set.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plots features weight in/not in a set — plot_features_weight_set","text":"method_output Integration method output generated via get_output() function. feature_set Character vector, features ID belonging features set interest. set_name Character, name set. Default value 'set'. features_metric Character, features metric plotted y-axis. one 'signed_importance' (default value), 'weight' 'importance'. add_missing_features Logical, whether features multi-omics dataset (provided mo_data argument) weight integration results (e.g. selected pre-processing step) added results. TRUE (default value), added weight importance 0. mo_data MultiDataSet-class object. add_missing_features true, features multi-omics dataset weight integration method result added weight importance 0. datasets Character vector, name datasets features importance plotted. NULL (default value), datasets considered. latent_dimensions Character vector, latent dimensions represent plot. NULL (default value), latent dimensions represented. point_alpha Numeric 0 1, opacity points plot (1 = fully opaque, 0 = fully transparent). Default value 0.5. add_boxplot Logical, boxplot drawn top points categorical covariates? Default value TRUE. scales Character, value use scales argument ggplot2::facet_grid(). Default value 'free_x'.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_features_weight_set.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plots features weight in/not in a set — plot_features_weight_set","text":"ggplot.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_meansd_data.html","id":null,"dir":"Reference","previous_headings":"","what":"Per-dataset mean-sd trend plots for MultiDataSet object — plot_meansd_data","title":"Per-dataset mean-sd trend plots for MultiDataSet object — plot_meansd_data","text":"Displays dataset MultiDataSet object trend features mean standard deviation across samples.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_meansd_data.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Per-dataset mean-sd trend plots for MultiDataSet object — plot_meansd_data","text":"","code":"plot_meansd_data(   mo_data,   datasets = names(mo_data),   by_rank = FALSE,   colour_log10 = TRUE )"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_meansd_data.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Per-dataset mean-sd trend plots for MultiDataSet object — plot_meansd_data","text":"mo_data MultiDataSet::MultiDataSet object. datasets Character vector, names datasets include plot. default, datasets included. by_rank Logical, x-axis display rank features (ordered mean) rather features mean? Default value FALSE, .e. x axis represents mean features. colour_log10 colour legend log10 scale? Default value TRUE.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_meansd_data.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Per-dataset mean-sd trend plots for MultiDataSet object — plot_meansd_data","text":"ggplot.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_samples_coordinates_pca.html","id":null,"dir":"Reference","previous_headings":"","what":"Samples score plots for single-omics PCA — plot_samples_coordinates_pca","title":"Samples score plots for single-omics PCA — plot_samples_coordinates_pca","text":"Produces pairwise samples score plot PCA run omics dataset, using GGally::ggpairs().","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_samples_coordinates_pca.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Samples score plots for single-omics PCA — plot_samples_coordinates_pca","text":"","code":"plot_samples_coordinates_pca(pca_result, pcs = NULL, datasets = NULL, ...)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_samples_coordinates_pca.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Samples score plots for single-omics PCA — plot_samples_coordinates_pca","text":"pca_result List PCA results datasets, computed run_pca() function. pcs Integer vector named list integer vectors, principal components display dataset. integer vector (e.g. 1:5), used datasets. Alternatively, different set PCs can specified named list (e.g. list('snps' = 1:4, 'rnaseq' = 1:5)). length list must match number datasets displayed, names must match dataset names. Default value NULL, .e. principal components plotted dataset. datasets Optional, character vector datasets plots created. ... arguments passed plot_samples_score().","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_samples_coordinates_pca.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Samples score plots for single-omics PCA — plot_samples_coordinates_pca","text":"list ggmatrix plots (single ggmatrix plot pcs length 1).","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_samples_coordinates_pca.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Samples score plots for single-omics PCA — plot_samples_coordinates_pca","text":"","code":"if (FALSE) { ## Default: plotting all PCs for all datasets plot_samples_coordinates_pca(pca_result)  ## Plotting only the first 3 PCs for each dataset plot_samples_coordinates_pca(   pca_result,   pcs = 1:3 )  ## Plotting the first 3 PCs for the genomics dataset, 4 PCs for the ## transcriptomics dataset, 5 PCs for the metabolomics dataset plot_samples_coordinates_pca(   pca_result,   pcs = list(     \"snps\" = 1:3,     \"rnaseq\" = 1:4,     \"metabolome\" = 1:5   ) )  ## Plotting the first 3 PCs for the genomics and transcriptomics datasets plot_samples_coordinates_pca(   pca_result,   pcs = 1:3,   datasets = c(\"snps\", \"rnaseq\") )  # Plotting the first 3 PCs for the genomics dataset and 4 PCs for the ## transcriptomics dataset (no plot for the metabolomics dataset) plot_samples_coordinates_pca(   pca_result,   pcs = list(     \"snps\" = 1:3,     \"rnaseq\" = 1:4   ),   datasets = c(\"snps\", \"rnaseq\") ) }"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_samples_score.html","id":null,"dir":"Reference","previous_headings":"","what":"Plots sample scores as scatterplot matrix — plot_samples_score","title":"Plots sample scores as scatterplot matrix — plot_samples_score","text":"Plots samples score dimension reduction analysis matrix scatterplots. one latent dimension, plotted boxplot instead.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_samples_score.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plots sample scores as scatterplot matrix — plot_samples_score","text":"","code":"plot_samples_score(   method_output,   latent_dimensions = NULL,   mo_data = NULL,   colour_upper = NULL,   colour_diag = colour_upper,   colour_lower = colour_upper,   shape_upper = NULL,   shape_lower = shape_upper,   scale_colour_upper = NULL,   scale_colour_diag = NULL,   scale_colour_lower = NULL,   scale_shape_upper = NULL,   scale_shape_lower = NULL,   title = NULL,   point_size = 1.5 )"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_samples_score.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plots sample scores as scatterplot matrix — plot_samples_score","text":"method_output Integration method output generated via get_output() function. latent_dimensions Character vector giving latent dimensions display. NULL (default value), latent dimensions shown. mo_data MultiDataSet object (used extract samples information). colour_upper Character, name column one samples metadata tables mo_data use colouring observations upper triangle plots. Default value NULL. colour_diag Character, name column one samples metadata tables mo_data use colouring observations diagonal plots. default, follow colour_upper. colour_lower Character, name column one samples metadata tables mo_data use colouring observations lower triangle plots. default, follow colour_upper. shape_upper Character, name column one samples metadata tables mo_data use shaping observations upper triangle plots. Default value NULL. shape_lower Character, name column one samples metadata tables mo_data use shaping observations lower triangle plots. default, follow shape_upper. scale_colour_upper ggplot2 colour scale use upper triangle plots. Default value NULL (colour_upper NULL, use ggplot2 default colour scales). scale_colour_diag ggplot2 colour scale use diagonal plots. NULL (default), colour scale used upper triangle plots used colour_diag equal colour_upper; colour scale used lower triangle plots used colour_diag equal colour_lower. scale_colour_lower ggplot2 colour scale use lower triangle plots. NULL (default), colour scale used upper triangle plots used. scale_shape_upper ggplot2 shape scale use upper triangle plots. Default value NULL (shape_upper NULL, use ggplot2 default shape scale). scale_shape_lower ggplot2 shape scale use lower triangle plots. NULL (default), shape scale used upper triangle plots used. title Character, title plot. NULL (default value), method name method_output used construct plot title. point_size Numeric, size points (pt) plot. Default value 1.5.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_samples_score.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plots sample scores as scatterplot matrix — plot_samples_score","text":"ggmatrix plot.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_samples_score.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plots sample scores as scatterplot matrix — plot_samples_score","text":"","code":"if (FALSE) { ## Let's say we've already prepared a MultiDataSet mo_data, in which the ## datasets have samples metadata with columns treatment (discrete), ## weeks (continuous), tissue_type (discrete), disease_score (continuous).  library(ggplot2)  pca_res <- run_pca(mo_data, \"metabolome\") output_pca <- get_output_pca(output_pca) pcs <- paste0(\"Principal component \", 1:4)  # Simple matrix of scatterplot to visualised PCs two by two plot_samples_score(   output_pca,   pcs )  # Colouring points according to weeks plot_samples_score(   output_pca,   pcs,   colour_upper = \"weeks\" ) # Adding a custom colour palette plot_samples_score(   output_pca,   pcs,   colour_upper = \"weeks\",   scale_colour_upper = scale_colour_viridis_c() )  # Adding the treatment as shape plot_samples_score(   output_pca,   pcs,   colour_upper = \"weeks\",   shape_upper = \"treatment\" )  # Using the lower triangle of the plots to display disease score # Again can pass custom colour scale through scale_colour_lower plot_samples_score(   output_pca,   pcs,   colour_upper = \"weeks\",   shape_upper = \"treatment\",   colour_lower = \"disease_score\" )  # By default the diagonal plots follow the colour of the upper plots, # but can follow the lower plots instead plot_samples_score(   output_pca,   pcs,   colour_upper = \"weeks\",   shape_upper = \"treatment\",   colour_lower = \"disease_score\",   colour_diag = \"disease_score\" )  # or diagonal can show a different variable plot_samples_score(   output_pca,   pcs,   colour_upper = \"weeks\",   shape_upper = \"treatment\",   colour_lower = \"tissue_type\" )  # also the lower plots can have a different shape than the upper plots plot_samples_score(   output_pca,   pcs,   colour_upper = \"weeks\",   shape_upper = \"treatment\",   colour_lower = \"disease_score\",   shape_lower = \"tissue_type\" ) }"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_samples_score_covariate.html","id":null,"dir":"Reference","previous_headings":"","what":"Plots sample scores against covariate — plot_samples_score_covariate","title":"Plots sample scores against covariate — plot_samples_score_covariate","text":"Plots samples score result integration method covariate samples metadata.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_samples_score_covariate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plots sample scores against covariate — plot_samples_score_covariate","text":"","code":"plot_samples_score_covariate(   method_output,   mo_data,   covariate,   latent_dimensions = NULL,   colour_by = NULL,   shape_by = NULL,   point_alpha = 1,   add_se = TRUE,   add_boxplot = TRUE,   ncol = NULL )"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_samples_score_covariate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plots sample scores against covariate — plot_samples_score_covariate","text":"method_output Integration method output generated via get_output() function. mo_data MultiDataSet object (used extract samples information). covariate Character, name column one samples metadata tables mo_data use x-axis plot. latent_dimensions Character vector giving latent dimensions display. Default value NULL, .e. latent dimensions shown. colour_by Character, name column one samples metadata tables mo_data use colour samples plot. Default value NULL. shape_by Character, name column one samples metadata tables mo_data use shape samples plot. Default value NULL. point_alpha Numeric 0 1, opacity points plot (1 = fully opaque, 0 = fully transparent). Default value 1. add_se Logical, confidence interval drawn around smoothing curves numerical covariates? Default value TRUE. add_boxplot Logical, boxplot drawn top points categorical covariates? Default value TRUE. ncol Integer, number columns faceted plot. Default value NULL.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_samples_score_covariate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plots sample scores against covariate — plot_samples_score_covariate","text":"ggplot.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_samples_score_covariate.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Plots sample scores against covariate — plot_samples_score_covariate","text":"covariate numeric, function creates scatter plot, loess curve summarise trend covariate samples score. colour_by used, corresponding variable numeric, loess curve take account variable. instead colour_by variable character factor, loess curve fitted separately category. covariate numeric, function creates violin/boxplot. colour_by used, corresponding variable numeric, violins boxplots take account variable. instead colour_by variable character factor, separate violin boxplot drawn category.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_samples_score_pair.html","id":null,"dir":"Reference","previous_headings":"","what":"Plots sample scores as a scatterplot — plot_samples_score_pair","title":"Plots sample scores as a scatterplot — plot_samples_score_pair","text":"Plots samples score pair latent dimensions dimension reduction analysis scatterplot.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_samples_score_pair.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plots sample scores as a scatterplot — plot_samples_score_pair","text":"","code":"plot_samples_score_pair(   method_output,   latent_dimensions,   mo_data = NULL,   colour_by = NULL,   shape_by = NULL )"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_samples_score_pair.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plots sample scores as a scatterplot — plot_samples_score_pair","text":"method_output single Integration method output generated via get_output() function, list two integration method outputs. latent_dimensions Character vector length 2 (method_output single output object), named list length 2 (method_output list two output objects). first case, gives name two latent dimensions represented. second case, names list correspond names methods, values character giving corresponding method name latent dimension display. mo_data MultiDataSet object (used extract samples information). colour_by Character, name column one samples metadata tables mo_data use colouring samples plot. Default value NULL. shape_by Character, name column one samples metadata tables mo_data use shape samples plot. Default value NULL.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_samples_score_pair.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plots sample scores as a scatterplot — plot_samples_score_pair","text":"ggplot.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_samples_score_pair.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plots sample scores as a scatterplot — plot_samples_score_pair","text":"","code":"if (FALSE) { ## Let diablo_res be the output from a DIABLO analysis, and mofa_res the ## output from a MOFA analysis. Let mo_set be the corresponding MultiDataSet object. output_diablo <- get_output_diablo(diablo_res) output_mofa <- get_output_mofa2(mofa_res)  ## Scatterplot of the first two DIABLO components plot_samples_score_pair(output_diablo, c(\"Component 1\", \"Component 2\"))  ## Adding samples information to the plot - here 'Time' and 'Treatment' should ## two columns in the samples metadata of one of the datasets in mo_set plot_samples_score_pair(   output_diablo,   c(\"Component 1\", \"Component 2\"),   mo_data <- mo_set,   colour_by = \"Time\",   shape_by = \"Treatment\" )  ## Comparing the first MOFA factor to the first DIABLO component plot_samples_score_pair(   list(output_diablo, output_mofa),   list(\"DIABLO\" = \"Component 1\", \"MOFA\" = \"Factor 1\"),   mo_data <- mo_set,   colour_by = \"Time\",   shape_by = \"Treatment\" )  ## Giving custom names to the methods plot_samples_score_pair(   list(\"DIABLO prefiltered\" = output_diablo, \"MOFA full\" = output_mofa),   list(\"DIABLO prefiltered\" = \"Component 1\", \"MOFA full\" = \"Factor 1\") ) }"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_samples_upset.html","id":null,"dir":"Reference","previous_headings":"","what":"Upset plot of samples — plot_samples_upset","title":"Upset plot of samples — plot_samples_upset","text":"Generates upset plot compare samples present omics dataset.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_samples_upset.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Upset plot of samples — plot_samples_upset","text":"","code":"plot_samples_upset(mo_data)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_samples_upset.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Upset plot of samples — plot_samples_upset","text":"mo_data MultiDataSet::MultiDataSet object.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_samples_upset.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Upset plot of samples — plot_samples_upset","text":"upset plot.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_screeplot_pca.html","id":null,"dir":"Reference","previous_headings":"","what":"Screeplots for single-omics PCA — plot_screeplot_pca","title":"Screeplots for single-omics PCA — plot_screeplot_pca","text":"Produces scree plot (percentage variance explained principal component) PCA run omics dataset, using ggplot2.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_screeplot_pca.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Screeplots for single-omics PCA — plot_screeplot_pca","text":"","code":"plot_screeplot_pca(pca_result, cumulative = FALSE, datasets = NULL)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_screeplot_pca.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Screeplots for single-omics PCA — plot_screeplot_pca","text":"pca_result List PCA runs result datasets, computed run_pca() function. cumulative Logical, cumulative variance plotted? Default FALSE. datasets Optional, character vector names datasets plot. NULL (default value), datasets plotted.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_screeplot_pca.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Screeplots for single-omics PCA — plot_screeplot_pca","text":"ggplot2 plot.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_top_features.html","id":null,"dir":"Reference","previous_headings":"","what":"Plots top features importance — plot_top_features","title":"Plots top features importance — plot_top_features","text":"Plots top features importance per dataset latent dimension.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_top_features.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plots top features importance — plot_top_features","text":"","code":"plot_top_features(   method_output,   latent_dimensions = NULL,   group_latent_dims = TRUE,   datasets = NULL,   n_features = 20,   mo_data = NULL,   label_cols = NULL,   truncate = NULL,   nrow = 1 )"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_top_features.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plots top features importance — plot_top_features","text":"method_output Integration method output generated via get_output() function. latent_dimensions Character vector giving latent dimensions display. Default value NULL, .e. latent dimensions shown. group_latent_dims Logical, integrations methods construct datasets- specific versions latent dimension, grouped? e.g. DIABLO constructs snps- rnaseq version component 1, two grouped \"Component 1\"? Default value TRUE. datasets Character vector giving datasets display. Default value NULL, .e. datasets shown. n_features Integer, number top features display per dataset latent dimension. mo_data MultiDataSet object. used label_cols NULL. label_cols Character named list character, giving dataset name column corresponding features metadata use label. one value, used datasets. list, names must correspond names datasets mo_data. dataset missing list value provided, feature IDs used labels. Alternatively, use feature_id get feature IDs labels. truncate Integer, width labels truncated (avoid issues long labels plots). NULL (default value), truncation performed. nrow Integer, number rows dataset panels plotted latent dimensions.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_top_features.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plots top features importance — plot_top_features","text":"patchwork plots.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_variance_explained.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot of variance explained — plot_variance_explained","title":"Plot of variance explained — plot_variance_explained","text":"Displays percentage variance explained latent dimension output dimension reduction method dataset analysed.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_variance_explained.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot of variance explained — plot_variance_explained","text":"","code":"plot_variance_explained(   method_output,   datasets = NULL,   latent_dimensions = NULL,   ncol = NULL,   free_y_axis = FALSE,   cumulative = FALSE )"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_variance_explained.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot of variance explained — plot_variance_explained","text":"method_output Integration method output generated via get_output() function. datasets Character vector giving datasets display. Default value NULL, .e. datasets shown. latent_dimensions Character vector giving latent dimensions display. Default value NULL, .e. latent dimensions shown. ncol Integer, number columns faceted plot. Default value NULL. free_y_axis Logical, whether y-axis (representing percentage variance) range datasets. Default value FALSE. cumulative Logical, whether cumulative percentage variance explained plotted. Default FALSE.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_variance_explained.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot of variance explained — plot_variance_explained","text":"ggplot.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_x_continuous.html","id":null,"dir":"Reference","previous_headings":"","what":"Scatter plot function — plot_x_continuous","title":"Scatter plot function — plot_x_continuous","text":"Creates scatter plot","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_x_continuous.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Scatter plot function — plot_x_continuous","text":"","code":"plot_x_continuous(toplot, x, y, colour, shape, point_alpha = 1, add_se = TRUE)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_x_continuous.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Scatter plot function — plot_x_continuous","text":"toplot Tibble data plot. x Character, name column toplot use x-axis. y Character, name column toplot use y-axis. colour Character, name column toplot use colour. shape Character, name column toplot use shape. point_alpha Numeric 0 1, opacity points plot (1 = fully opaque, 0 = fully transparent). Default value 1. add_se Logical, confidence interval drawn around smoothing curve scatterplots? Default value TRUE.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_x_continuous.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Scatter plot function — plot_x_continuous","text":"ggplot.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_x_continuous.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Scatter plot function — plot_x_continuous","text":"function adds loess curve summarise trend covariate samples score. colour_by used, corresponding variable numeric, loess curve take account variable. instead colour_by variable character factor, loess curve fitted separately category.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_x_discrete.html","id":null,"dir":"Reference","previous_headings":"","what":"Violin plot function function — plot_x_discrete","title":"Violin plot function function — plot_x_discrete","text":"Creates violin plot","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_x_discrete.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Violin plot function function — plot_x_discrete","text":"","code":"plot_x_discrete(   toplot,   x,   y,   colour,   shape,   point_alpha = 1,   add_boxplot = TRUE )"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_x_discrete.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Violin plot function function — plot_x_discrete","text":"toplot Tibble data plot. x Character, name column toplot use x-axis. y Character, name column toplot use y-axis. colour Character, name column toplot use colour. shape Character, name column toplot use shape. point_alpha Numeric 0 1, opacity points plot (1 = fully opaque, 0 = fully transparent). Default value 1. add_boxplot Logical, boxplot drawn top points violin plots? Default value TRUE.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_x_discrete.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Violin plot function function — plot_x_discrete","text":"ggplot.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_x_discrete.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Violin plot function function — plot_x_discrete","text":"colour_by used, corresponding variable numeric, violins boxplots take account variable. instead colour_by variable character factor, separate violin boxplot drawn category.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_x_wrapper.html","id":null,"dir":"Reference","previous_headings":"","what":"Wrapper to create plot — plot_x_wrapper","title":"Wrapper to create plot — plot_x_wrapper","text":"Wrapper around plot_x_continuous() plot_x_discrete(), choose one use depending whether x-axis variable continuous discrete.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_x_wrapper.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Wrapper to create plot — plot_x_wrapper","text":"","code":"plot_x_wrapper(   toplot,   x,   y,   colour,   shape,   point_alpha = 1,   add_se = TRUE,   add_boxplot = TRUE,   facet_wrap = NULL,   ncol_wrap = NULL,   facet_grid = NULL,   scales_facet = \"free_y\" )"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_x_wrapper.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Wrapper to create plot — plot_x_wrapper","text":"toplot Tibble data plot. x Character, name column toplot use x-axis. y Character, name column toplot use y-axis. colour Character, name column toplot use colour. shape Character, name column toplot use shape. point_alpha Numeric 0 1, opacity points plot (1 = fully opaque, 0 = fully transparent). Default value 1. add_se Logical, confidence interval drawn around smoothing curve scatterplots? Default value TRUE. add_boxplot Logical, boxplot drawn top points violin plots? Default value TRUE. facet_wrap Character, name column toplot use faceting (using ggplot2::facet_wrap()). Default NULL. ncol_wrap Integer, number columns faceted plot using facet_wrap. Default value NULL. facet_grid Character vector length 2, name columns toplot use row (first element) column (second element) faceting (using ggplot2::facet_grid()). ignored facet_wrap NULL. Default NULL. scales_facet Character, value use scales argument ggplot2::facet_wrap() ggplot2::facet_grid(). Default value 'free_y'.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_x_wrapper.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Wrapper to create plot — plot_x_wrapper","text":"ggplot.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/reduce_feature_sets_data.html","id":null,"dir":"Reference","previous_headings":"","what":"Reduce feature sets to match multi-omics dataset — reduce_feature_sets_data","title":"Reduce feature sets to match multi-omics dataset — reduce_feature_sets_data","text":"Removes list feature sets features present multi-omics dataset.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/reduce_feature_sets_data.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Reduce feature sets to match multi-omics dataset — reduce_feature_sets_data","text":"","code":"reduce_feature_sets_data(feature_sets, mo_data, datasets = names(mo_data))"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/reduce_feature_sets_data.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Reduce feature sets to match multi-omics dataset — reduce_feature_sets_data","text":"feature_sets Named list, element corresponds feature set, contains vector features ID features belonging set. mo_data MultiDataSet-class object. datasets Character vector, names datasets features assignment checked. default, datasets mo_data considered.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/reduce_feature_sets_data.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Reduce feature sets to match multi-omics dataset — reduce_feature_sets_data","text":"feature sets list, .e. named list element corresponds feature set, containing ID features belong set present multi-omics dataset.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/replace_dataset.html","id":null,"dir":"Reference","previous_headings":"","what":"Replace matrix dataset within a MultiDataSet object — replace_dataset","title":"Replace matrix dataset within a MultiDataSet object — replace_dataset","text":"Replaces matrix omics dataset new matrix MultiDataSet object.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/replace_dataset.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Replace matrix dataset within a MultiDataSet object — replace_dataset","text":"","code":"replace_dataset(mo_data, dataset_name, new_data)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/replace_dataset.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Replace matrix dataset within a MultiDataSet object — replace_dataset","text":"mo_data MultiDataSet::MultiDataSet object. dataset_name Character, name dataset matrix data changed. new_data Matrix, new data. features rows samples columns. Rownames match corresponding feature IDs, colnames match corresponding sample IDs.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/replace_dataset.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Replace matrix dataset within a MultiDataSet object — replace_dataset","text":"MultiDataSet object.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/round_dataset.html","id":null,"dir":"Reference","previous_headings":"","what":"Round values in omics dataset from MultiDataSet object — round_dataset","title":"Round values in omics dataset from MultiDataSet object — round_dataset","text":"Rounds values given omics dataset within MultiDataSet object. Can also limit range possible values.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/round_dataset.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Round values in omics dataset from MultiDataSet object — round_dataset","text":"","code":"round_dataset(   mo_data,   dataset_name,   ndecimals = 0,   min_val = -Inf,   max_val = Inf )"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/round_dataset.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Round values in omics dataset from MultiDataSet object — round_dataset","text":"mo_data MultiDataSet::MultiDataSet object. dataset_name Character, name dataset matrix data changed. ndecimals Integer, number decimals keep dataset. Default value 0. min_val Numeric, minimum value allowed dataset. Values min_val set min_val. max_val Numeric, maximum value allowed dataset. Values max_val set max_val.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/round_dataset.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Round values in omics dataset from MultiDataSet object — round_dataset","text":"MultiDataSet object.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/round_dataset.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Round values in omics dataset from MultiDataSet object — round_dataset","text":"","code":"if (FALSE) { ## Let's imagine that we imputed missing values in the genomics dataset from ## mo_data using NIPALS-PCA. The imputed values are continuous, but the ## dataset contains dosage values for a diploid organism (i.e. values can ## be 0, 1, 2). We'll round the imputed values and make sure they can't be ## negative or higher than 2. round_dataset(mo_data, \"snps\", min_val = 0, max_val = 2) }"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/run_pairwise_pls.html","id":null,"dir":"Reference","previous_headings":"","what":"Pairwise PLS datasets comparison — run_pairwise_pls","title":"Pairwise PLS datasets comparison — run_pairwise_pls","text":"Runs Projection Latent Structure (PLS) analysis pair omics datasets, per mixOmics package.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/run_pairwise_pls.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Pairwise PLS datasets comparison — run_pairwise_pls","text":"","code":"run_pairwise_pls(mixomics_data, datasets_name, ..., verbose = TRUE)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/run_pairwise_pls.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Pairwise PLS datasets comparison — run_pairwise_pls","text":"mixomics_data mixOmics input object created get_input_mixomics_supervised. datasets_name Character vector length 2, names two omics datasets analyse. ... Additional parameters passed pls function. verbose Logical, details printed execution? Default value TRUE.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/run_pairwise_pls.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Pairwise PLS datasets comparison — run_pairwise_pls","text":"named list; element object class pls, provides result PLS run. name datasets analysed stored character vector datasets_name attribute.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/run_pairwise_pls.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Pairwise PLS datasets comparison — run_pairwise_pls","text":"Note one latent component computed first latent component used assess correlation datasets.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/run_pairwise_pls.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Pairwise PLS datasets comparison — run_pairwise_pls","text":"","code":"if (FALSE) { run_pairwise_pls(mo_set, c(\"rnaseq\", \"metabolome\")) }"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/run_pca.html","id":null,"dir":"Reference","previous_headings":"","what":"Run PCA on MultiDataSet — run_pca","title":"Run PCA on MultiDataSet — run_pca","text":"Runs Principal Component Analysis omics dataset MultiDataSet object. wrapper function around get_dataset_matrix() run_pca_matrix() functions.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/run_pca.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Run PCA on MultiDataSet — run_pca","text":"","code":"run_pca(   mo_data,   dataset_name,   n_pcs = 10,   scale = \"none\",   center = TRUE,   method = NULL )"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/run_pca.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Run PCA on MultiDataSet — run_pca","text":"mo_data MultiDataSet::MultiDataSet object. dataset_name Character, name omics dataset PCA run. n_pcs numeric, number Principal Components compute. Default value 10. scale character, type scaling applied dataset running PCA. one 'none', 'pareto', 'vector', 'uv' (see pcaMethods::pca()). Default value 'none'. center boolean, dataset centred prior running PCA? Default value TRUE. method character, type PCA applied dataset. See pcaMethods::listPcaMethods(). list available methods. Default value 'svd' datasets missing value, 'nipals' datasets missing values.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/run_pca.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Run PCA on MultiDataSet — run_pca","text":"pcaMethods::pcaRes object containing result PCA analysis. attribute dataset_name specifies name dataset analysed.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/run_pca.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Run PCA on MultiDataSet — run_pca","text":"facilitate use dynamic branching targets package, dataset_name attribute resulting object set value dataset_name parameter, can accessed via attr(res_pca, \"dataset_name\").","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/run_pca_matrix.html","id":null,"dir":"Reference","previous_headings":"","what":"Run PCA on matrix — run_pca_matrix","title":"Run PCA on matrix — run_pca_matrix","text":"Runs Principal Component Analysis omics matrix, using pcaMethods::pca() function.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/run_pca_matrix.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Run PCA on matrix — run_pca_matrix","text":"","code":"run_pca_matrix(mat, n_pcs = 10, scale = \"none\", center = TRUE, method = NULL)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/run_pca_matrix.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Run PCA on matrix — run_pca_matrix","text":"mat Matrix omics measurement, features rows samples columns. n_pcs numeric, number Principal Components compute. Default value 10. scale character, type scaling applied dataset running PCA. one 'none', 'pareto', 'vector', 'uv' (see pcaMethods::pca()). Default value 'none'. center boolean, dataset centred prior running PCA? Default value TRUE. method character, type PCA applied dataset. See pcaMethods::listPcaMethods(). list available methods. Default value 'svd' datasets missing value, 'nipals' datasets missing values.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/run_pca_matrix.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Run PCA on matrix — run_pca_matrix","text":"pcaMethods::pcaRes object containing result PCA analysis. attribute dataset_name specifies name dataset analysed.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/run_splsda.html","id":null,"dir":"Reference","previous_headings":"","what":"Performs sPLS-DA on omics dataset from MultiDataSet object — run_splsda","title":"Performs sPLS-DA on omics dataset from MultiDataSet object — run_splsda","text":"Performs sPLS-DA (implemented mixOmics) package omics dataset MultiDataSet object. intended feature preselection omics dataset (see get_filtered_dataset_splsda).","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/run_splsda.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Performs sPLS-DA on omics dataset from MultiDataSet object — run_splsda","text":"","code":"run_splsda(   splsda_input,   perf_res,   to_keep_n = NULL,   to_keep_prop = NULL,   ncomp = NULL )"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/run_splsda.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Performs sPLS-DA on omics dataset from MultiDataSet object — run_splsda","text":"splsda_input Input sPLS-DA functions mixOmics, created get_input_splsda(). perf_res Result perf_splsda function. supplied, sPLS-DA run dataset specified argument dataset_name number latent components specified argument comp. to_keep_n Integer, number features retain dataset. less number features dataset. NULL NA, to_keep_prop used instead. to_keep_prop Numeric, proportion features retain dataset. ignored to_keep_n supplied. Value > 0 < 1. ncomp Integer, number latent components construct. Ignored perf_res supplied. Default value NULL.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/run_splsda.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Performs sPLS-DA on omics dataset from MultiDataSet object — run_splsda","text":"list per output splsda function.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/run_splsda.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Performs sPLS-DA on omics dataset from MultiDataSet object — run_splsda","text":"function uses plsda function mixOmics package. Note sPLS-DA method can select feature several latent components, number features retained dataset might less number specified to_keep_n argument.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/select_features_cov.html","id":null,"dir":"Reference","previous_headings":"","what":"Select features based on Coefficient of Variation from MultiDataSet — select_features_cov","title":"Select features based on Coefficient of Variation from MultiDataSet — select_features_cov","text":"Computes Coefficient Variation (COV) feature omics dataset MultiDataSet object, select features highest COV values. wrapper function around get_dataset_matrix() select_features_cov_matrix() functions.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/select_features_cov.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Select features based on Coefficient of Variation from MultiDataSet — select_features_cov","text":"","code":"select_features_cov(   mo_data,   dataset_name,   to_keep_n = NULL,   to_keep_prop = NULL,   with_ties = TRUE )"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/select_features_cov.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Select features based on Coefficient of Variation from MultiDataSet — select_features_cov","text":"mo_data MultiDataSet-class object. dataset_name Character, name omics dataset apply feature pre-selection. to_keep_n Integer, number features retain dataset. less number features dataset. NULL NA, to_keep_prop used instead. to_keep_prop Numeric, proportion features retain dataset. ignored to_keep_n supplied. Value > 0 < 1. with_ties ties kept together? TRUE, may return features requested. Default value TRUE.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/select_features_cov.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Select features based on Coefficient of Variation from MultiDataSet — select_features_cov","text":"tibble columns feature_id, cov selected (logical, indicates whether feature selected based COV value). addition, name dataset filtered stored return object attribute dataset_name (can accessed via attr(res, \"dataset_name\")).","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/select_features_cov_matrix.html","id":null,"dir":"Reference","previous_headings":"","what":"Select features based on Coefficient of Variation from matrix — select_features_cov_matrix","title":"Select features based on Coefficient of Variation from matrix — select_features_cov_matrix","text":"Computes Coefficient Variation (COV) feature omics dataset MultiDataSet object, select features highest COV values.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/select_features_cov_matrix.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Select features based on Coefficient of Variation from matrix — select_features_cov_matrix","text":"","code":"select_features_cov_matrix(   mat,   to_keep_n = NULL,   to_keep_prop = NULL,   with_ties = TRUE )"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/select_features_cov_matrix.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Select features based on Coefficient of Variation from matrix — select_features_cov_matrix","text":"mat Matrix omics measurement, features rows samples columns. to_keep_n Integer, number features retain dataset. less number features dataset. NULL NA, to_keep_prop used instead. to_keep_prop Numeric, proportion features retain dataset. ignored to_keep_n supplied. Value > 0 < 1. with_ties ties kept together? TRUE, may return features requested. Default value TRUE.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/select_features_cov_matrix.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Select features based on Coefficient of Variation from matrix — select_features_cov_matrix","text":"tibble columns feature_id, cov selected (logical, indicates whether feature selected based COV value). addition, name dataset filtered stored return object attribute dataset_name (can accessed via attr(res, \"dataset_name\")).","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/select_features_mad.html","id":null,"dir":"Reference","previous_headings":"","what":"Select features based on Median Absolute Deviation from MultiDataSet — select_features_mad","title":"Select features based on Median Absolute Deviation from MultiDataSet — select_features_mad","text":"Computes Median Absolute Deviation (MAD) feature omics dataset MultiDataSet object, select features highest MAD values. wrapper function around get_dataset_matrix() select_features_mad_matrix() functions.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/select_features_mad.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Select features based on Median Absolute Deviation from MultiDataSet — select_features_mad","text":"","code":"select_features_mad(   mo_data,   dataset_name,   to_keep_n = NULL,   to_keep_prop = NULL,   with_ties = TRUE )"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/select_features_mad.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Select features based on Median Absolute Deviation from MultiDataSet — select_features_mad","text":"mo_data MultiDataSet-class object. dataset_name Character, name omics dataset apply feature pre-selection. to_keep_n Integer, number features retain dataset. less number features dataset. NULL NA, to_keep_prop used instead. to_keep_prop Numeric, proportion features retain dataset. ignored to_keep_n supplied. Value > 0 < 1. with_ties ties kept together? TRUE, may return features requested. Default value TRUE.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/select_features_mad.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Select features based on Median Absolute Deviation from MultiDataSet — select_features_mad","text":"tibble columns feature_id, mad selected (logical, indicates whether feature selected based MAD value). addition, name dataset filtered stored return object attribute dataset_name (can accessed via attr(res, \"dataset_name\")).","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/select_features_mad_matrix.html","id":null,"dir":"Reference","previous_headings":"","what":"Select features based on Median Absolute Deviation from matrix — select_features_mad_matrix","title":"Select features based on Median Absolute Deviation from matrix — select_features_mad_matrix","text":"Computes Median Absolute Deviation (MAD) feature omics dataset MultiDataSet object, select features highest MAD values.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/select_features_mad_matrix.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Select features based on Median Absolute Deviation from matrix — select_features_mad_matrix","text":"","code":"select_features_mad_matrix(   mat,   to_keep_n = NULL,   to_keep_prop = NULL,   with_ties = TRUE )"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/select_features_mad_matrix.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Select features based on Median Absolute Deviation from matrix — select_features_mad_matrix","text":"mat Matrix omics measurement, features rows samples columns. to_keep_n Integer, number features retain dataset. less number features dataset. NULL NA, to_keep_prop used instead. to_keep_prop Numeric, proportion features retain dataset. ignored to_keep_n supplied. Value > 0 < 1. with_ties ties kept together? TRUE, may return features requested. Default value TRUE.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/select_features_mad_matrix.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Select features based on Median Absolute Deviation from matrix — select_features_mad_matrix","text":"tibble columns feature_id, mad selected (logical, indicates whether feature selected based MAD value). addition, name dataset filtered stored return object attribute dataset_name (can accessed via attr(res, \"dataset_name\")).","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/show_consensus_metrics.html","id":null,"dir":"Reference","previous_headings":"","what":"Illustrates importance consensus metrics — show_consensus_metrics","title":"Illustrates importance consensus metrics — show_consensus_metrics","text":"Plots heatmap illustrate behaviour different importance consensus metrics.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/show_consensus_metrics.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Illustrates importance consensus metrics — show_consensus_metrics","text":"","code":"show_consensus_metrics(   metrics = c(\"min\", \"harmonic\", \"geometric\", \"product\", \"average\", \"l2\", \"max\") )"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/show_consensus_metrics.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Illustrates importance consensus metrics — show_consensus_metrics","text":"metrics Character vector metrics show. valid values metric argument consensus_importance_metric(), .e. \"min\", \"max\", \"average\", \"product\", \"l2\", \"geometric\", \"harmonic\".","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/show_consensus_metrics.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Illustrates importance consensus metrics — show_consensus_metrics","text":"ggplot.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/so2pls_compare_samples_joint_components.html","id":null,"dir":"Reference","previous_headings":"","what":"Compares sO2PLS samples joint component scores between the two datasets — so2pls_compare_samples_joint_components","title":"Compares sO2PLS samples joint component scores between the two datasets — so2pls_compare_samples_joint_components","text":"Plots comparison samples joint component scores obtained two datasets sO2PLS run (OmicsPLS package).","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/so2pls_compare_samples_joint_components.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compares sO2PLS samples joint component scores between the two datasets — so2pls_compare_samples_joint_components","text":"","code":"so2pls_compare_samples_joint_components(so2pls_res, components = NULL, ...)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/so2pls_compare_samples_joint_components.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compares sO2PLS samples joint component scores between the two datasets — so2pls_compare_samples_joint_components","text":"so2pls_res output o2m function. components Optional, integer vector joint components plotted. Default NULL, .e. joint components represented. ... arguments passed plot_samples_score_pair.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/so2pls_compare_samples_joint_components.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compares sO2PLS samples joint component scores between the two datasets — so2pls_compare_samples_joint_components","text":"patchwork ggplots.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/so2pls_crossval_o2m.html","id":null,"dir":"Reference","previous_headings":"","what":"Wrapper for OmicsPLS::crossval_o2m function — so2pls_crossval_o2m","title":"Wrapper for OmicsPLS::crossval_o2m function — so2pls_crossval_o2m","text":"Wrapper function around crossval_o2m function. main purpose wrapper add result names datasets facilitate plotting. result previous call crossval_o2m_adjR2 so2pls_crossval_o2m_adjR2 provided, used set values test , ax ay.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/so2pls_crossval_o2m.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Wrapper for OmicsPLS::crossval_o2m function — so2pls_crossval_o2m","text":"","code":"so2pls_crossval_o2m(   omicspls_input,   cv_adj_res = NULL,   a = 1:5,   ax = seq(0, 10, by = 2),   ay = seq(0, 10, by = 2),   nr_folds = 10,   ... )"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/so2pls_crossval_o2m.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Wrapper for OmicsPLS::crossval_o2m function — so2pls_crossval_o2m","text":"omicspls_input named list length 2, produced get_input_omicspls. cv_adj_res Data-frame returned crossval_o2m_adjR2 so2pls_crossval_o2m_adjR2. Default value NULL. Vector positive integers, number joint components test. Ignored cv_adj_res NULL. ax Vector non-negative integers, number specific components test first dataset. Ignored cv_adj_res NULL. ay Vector non-negative integers, number specific components test second dataset. Ignored cv_adj_res NULL. nr_folds Positive integer, number folds use cross-validation. Default value 10. ... arguments passed crossval_o2m_adjR2 function.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/so2pls_crossval_o2m.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Wrapper for OmicsPLS::crossval_o2m function — so2pls_crossval_o2m","text":"list class \"cvo2m\" original sorted Prediction errors number folds used.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/so2pls_crossval_o2m.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Wrapper for OmicsPLS::crossval_o2m function — so2pls_crossval_o2m","text":"result previous call crossval_o2m_adjR2 so2pls_crossval_o2m_adjR2 provided cv_adj_res parameter, optimal values n, nx ny extracted , values , ax ay set follows: = max(n - 1, 1):(n + 1) ax = max(nx - 1, 0):(nx + 1) ay = max(ny - 1, 0):(ny + 1)","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/so2pls_crossval_o2m_adjR2.html","id":null,"dir":"Reference","previous_headings":"","what":"Wrapper for OmicsPLS::crossval_o2m_adjR2 function — so2pls_crossval_o2m_adjR2","title":"Wrapper for OmicsPLS::crossval_o2m_adjR2 function — so2pls_crossval_o2m_adjR2","text":"Wrapper function around crossval_o2m_adjR2 function. main purpose wrapper add result names datasets facilitate plotting.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/so2pls_crossval_o2m_adjR2.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Wrapper for OmicsPLS::crossval_o2m_adjR2 function — so2pls_crossval_o2m_adjR2","text":"","code":"so2pls_crossval_o2m_adjR2(   omicspls_input,   a = 1:5,   ax = seq(0, 10, by = 2),   ay = seq(0, 10, by = 2),   nr_folds = 10,   ... )"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/so2pls_crossval_o2m_adjR2.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Wrapper for OmicsPLS::crossval_o2m_adjR2 function — so2pls_crossval_o2m_adjR2","text":"omicspls_input named list length 2, produced get_input_omicspls. Vector positive integers, number joint components test. ax Vector non-negative integers, number specific components test first dataset. ay Vector non-negative integers, number specific components test second dataset. nr_folds Positive integer, number folds use cross-validation. Default value 10. ... arguments passed crossval_o2m_adjR2 function.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/so2pls_crossval_o2m_adjR2.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Wrapper for OmicsPLS::crossval_o2m_adjR2 function — so2pls_crossval_o2m_adjR2","text":"data-frame four columns: MSE, n, nx ny. row corresponds element .","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/so2pls_crossval_sparsity.html","id":null,"dir":"Reference","previous_headings":"","what":"Perform cross-validation to find the optimal number of features/groups to keep for each joint component for sO2PLS — so2pls_crossval_sparsity","title":"Perform cross-validation to find the optimal number of features/groups to keep for each joint component for sO2PLS — so2pls_crossval_sparsity","text":"Computes optimal number features/groups keep joint component sO2PLS run. Directly copied crossval_sparsity function, improved output plotting purposes.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/so2pls_crossval_sparsity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Perform cross-validation to find the optimal number of features/groups to keep for each joint component for sO2PLS — so2pls_crossval_sparsity","text":"","code":"so2pls_crossval_sparsity(   omicspls_input,   n,   nx,   ny,   nr_folds = 10,   keepx_seq = NULL,   keepy_seq = NULL,   groupx = NULL,   groupy = NULL,   tol = 1e-10,   max_iterations = 100 )"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/so2pls_crossval_sparsity.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Perform cross-validation to find the optimal number of features/groups to keep for each joint component for sO2PLS — so2pls_crossval_sparsity","text":"omicspls_input named list length 2, produced get_input_omicspls. n Integer, number joint PLS components. Must positive. nx Integer, number orthogonal components X. Negative values interpreted 0. ny Integer, number orthogonal components Y. Negative values interpreted 0. nr_folds integer, number folds cross-validation. Default value 10. keepx_seq Numeric vector, many features/groups keep cross-validation joint components X. Sparsity joint component selected sequentially. keepy_seq Numeric vector, many features/groups keep cross-validation joint components Y. Sparsity joint component selected sequentially. groupx Character vector, group name X-feature. length must equal number features X. order group names must corresponds order features. NULL, groups considered. Default value NULL. groupy Character vector, group name Y-feature. length must equal number features Y. order group names must corresponds order features. NULL, groups considered. Default value NULL. tol Numeric, threshold NIPALS method deemed converged. Must positive. Default value 1e-10. max_iterations Integer, maximum number iterations NIPALS method.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/so2pls_crossval_sparsity.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Perform cross-validation to find the optimal number of features/groups to keep for each joint component for sO2PLS — so2pls_crossval_sparsity","text":"list following elements: Best: vector giving join component number features keep X Y yield highest covariance joint components X Y (elements x1, y1, x2, y2, etc), number features keep X Y yielding highest covariance 1 standard error rule (elements x_1sd1, y_1sd1, x_1sd2, y_1sd2, etc). Covs: list, many elements number joint components (n). element matrix giving average covariance joint components X Y obtained across folds, tested values keepx (columns) keepy (rows). SEcov: list, many elements number joint components (n). element matrix giving standard error covariance joint components X Y obtained across folds, tested values keepx (columns) keepy (rows).","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/so2pls_get_components.html","id":null,"dir":"Reference","previous_headings":"","what":"Get list of latent components from sO2PLS results — so2pls_get_components","title":"Get list of latent components from sO2PLS results — so2pls_get_components","text":"Extracts list joint specific latent components sO2PLS results.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/so2pls_get_components.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get list of latent components from sO2PLS results — so2pls_get_components","text":"","code":"so2pls_get_components(so2pls_res)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/so2pls_get_components.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get list of latent components from sO2PLS results — so2pls_get_components","text":"so2pls_res sO2PLS results generated get_output_so2pls() function.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/so2pls_get_components.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get list of latent components from sO2PLS results — so2pls_get_components","text":"list following elements: joint: character vector name joint latent components. specific: named list length 2. element corresponds dataset (names list datasets name), character vector name specific latent components corresponding dataset.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/so2pls_get_optim_keep.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract optimal number of features to keep from cross-validation results for\nsO2PLS — so2pls_get_optim_keep","title":"Extract optimal number of features to keep from cross-validation results for\nsO2PLS — so2pls_get_optim_keep","text":"Extracts optimal number features retain datasets X Y joint components.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/so2pls_get_optim_keep.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract optimal number of features to keep from cross-validation results for\nsO2PLS — so2pls_get_optim_keep","text":"","code":"so2pls_get_optim_keep(cv_res, use_1sd_rule = TRUE)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/so2pls_get_optim_keep.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract optimal number of features to keep from cross-validation results for\nsO2PLS — so2pls_get_optim_keep","text":"cv_res List, result call so2pls_crossval_sparsity() OmicsPLS::crossval_sparsity(). use_1sd_rule Boolean, 1 standard deviation rule used selecting optimal number features retain? See Details.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/so2pls_get_optim_keep.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract optimal number of features to keep from cross-validation results for\nsO2PLS — so2pls_get_optim_keep","text":"list elements keepx keepy, vector length equal number joint components, ith element giving number features retain dataset X (keepx) Y (keepy) -th joint component.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/so2pls_get_optim_keep.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Extract optimal number of features to keep from cross-validation results for\nsO2PLS — so2pls_get_optim_keep","text":"1-SD rule means retaining smallest number features yielding average covariance within 1SD maximum covariance obtained.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/so2pls_get_optim_ncomp.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract optimal number of components from cross-validation results for sO2PLS — so2pls_get_optim_ncomp","title":"Extract optimal number of components from cross-validation results for sO2PLS — so2pls_get_optim_ncomp","text":"Extracts optimal number components (joint dataset-specific) estimated via cross-validation results sO2PLS.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/so2pls_get_optim_ncomp.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract optimal number of components from cross-validation results for sO2PLS — so2pls_get_optim_ncomp","text":"","code":"so2pls_get_optim_ncomp(cv_res)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/so2pls_get_optim_ncomp.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract optimal number of components from cross-validation results for sO2PLS — so2pls_get_optim_ncomp","text":"cv_res cvo2m object, output crossval_o2m function.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/so2pls_get_optim_ncomp.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract optimal number of components from cross-validation results for sO2PLS — so2pls_get_optim_ncomp","text":"vector three integer values: n: optimal number joint components nx: optimal number specific components dataset X (first dataset) ny: optimal number specific components dataset Y (second dataset)","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/so2pls_get_optim_ncomp_adj.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract optimal number of components from adjusted cross-validation results for sO2PLS — so2pls_get_optim_ncomp_adj","title":"Extract optimal number of components from adjusted cross-validation results for sO2PLS — so2pls_get_optim_ncomp_adj","text":"Extracts optimal number components (joint dataset-specific) estimated via adjusted cross-validation results sO2PLS.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/so2pls_get_optim_ncomp_adj.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract optimal number of components from adjusted cross-validation results for sO2PLS — so2pls_get_optim_ncomp_adj","text":"","code":"so2pls_get_optim_ncomp_adj(cv_res)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/so2pls_get_optim_ncomp_adj.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract optimal number of components from adjusted cross-validation results for sO2PLS — so2pls_get_optim_ncomp_adj","text":"cv_res Data-frame, output crossval_o2m_adjR2 function.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/so2pls_get_optim_ncomp_adj.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract optimal number of components from adjusted cross-validation results for sO2PLS — so2pls_get_optim_ncomp_adj","text":"vector three integer values: n: optimal number joint components nx: optimal number specific components dataset X (first dataset) ny: optimal number specific components dataset Y (second dataset)","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/so2pls_get_variance_explained.html","id":null,"dir":"Reference","previous_headings":"","what":"Percentage of variance explained for sO2PLS — so2pls_get_variance_explained","title":"Percentage of variance explained for sO2PLS — so2pls_get_variance_explained","text":"Generates table giving percentage variance explained component sO2PLS corresponding dataset.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/so2pls_get_variance_explained.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Percentage of variance explained for sO2PLS — so2pls_get_variance_explained","text":"","code":"so2pls_get_variance_explained(so2pls_res)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/so2pls_get_variance_explained.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Percentage of variance explained for sO2PLS — so2pls_get_variance_explained","text":"so2pls_res output o2m function.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/so2pls_get_variance_explained.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Percentage of variance explained for sO2PLS — so2pls_get_variance_explained","text":"tibble columns latent_dimension, dataset prop_var_expl.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/so2pls_get_wa_coord.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes average sample coordinates for sO2PLS joint components — so2pls_get_wa_coord","title":"Computes average sample coordinates for sO2PLS joint components — so2pls_get_wa_coord","text":"Computes average sample coordinates sO2PLS joint components across two datasets.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/so2pls_get_wa_coord.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes average sample coordinates for sO2PLS joint components — so2pls_get_wa_coord","text":"","code":"so2pls_get_wa_coord(so2pls_res)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/so2pls_get_wa_coord.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes average sample coordinates for sO2PLS joint components — so2pls_get_wa_coord","text":"so2pls_res output o2m function.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/so2pls_get_wa_coord.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes average sample coordinates for sO2PLS joint components — so2pls_get_wa_coord","text":"matrix samples coordinates, samples rows joint components columns.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/so2pls_o2m.html","id":null,"dir":"Reference","previous_headings":"","what":"Wrapper for OmicsPLS::o2m function — so2pls_o2m","title":"Wrapper for OmicsPLS::o2m function — so2pls_o2m","text":"Wrapper function around o2m function. main purpose wrapper add result names datasets facilitate plotting.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/so2pls_o2m.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Wrapper for OmicsPLS::o2m function — so2pls_o2m","text":"","code":"so2pls_o2m(   omicspls_input,   cv_res = NULL,   sparsity_res = NULL,   n = NULL,   nx = NULL,   ny = NULL,   sparse = FALSE,   keepx = NULL,   keepy = NULL,   ... )"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/so2pls_o2m.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Wrapper for OmicsPLS::o2m function — so2pls_o2m","text":"omicspls_input named list length 2, produced get_input_omicspls. cv_res Named integer vector length 3, names n, nx, ny. obtained so2pls_get_optim_ncomp_adj so2pls_get_optim_ncomp. sparsity_res Named list length 2, names keepx keepy. obtained so2pls_get_optim_keep. n Positive integer, number joint components compute. Ignored cv_res NULL. nx Positive integer, number specific components compute first dataset. Ignored cv_res NULL. ny Positive integer, number specific components compute second dataset. Ignored cv_res NULL. sparse Logical, feature selection performed? Default value FALSE. sparsity_res NULL, set TRUE. keepx Integer integer vector length n, number features first dataset retain joint component. Ignored sparsity_res NULL. keepy Integer integer vector length n, number features second dataset retain joint component. Ignored sparsity_res NULL. ... arguments passed o2m.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/so2pls_o2m.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Wrapper for OmicsPLS::o2m function — so2pls_o2m","text":"list (see o2m).","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/so2pls_plot_cv.html","id":null,"dir":"Reference","previous_headings":"","what":"Plots cross-validation results for sO2PLS — so2pls_plot_cv","title":"Plots cross-validation results for sO2PLS — so2pls_plot_cv","text":"Plots results cross-validation sO2PLS run (OmicsPLS package)","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/so2pls_plot_cv.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plots cross-validation results for sO2PLS — so2pls_plot_cv","text":"","code":"so2pls_plot_cv(cv_res, nb_col = NULL)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/so2pls_plot_cv.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plots cross-validation results for sO2PLS — so2pls_plot_cv","text":"cv_res cvo2m object, output crossval_o2m function. nb_col Integer, number columns use faceted plot. Default value NULL (number columns chosen automatically).","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/so2pls_plot_cv.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plots cross-validation results for sO2PLS — so2pls_plot_cv","text":"ggplot.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/so2pls_plot_cv_adj.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot adjusted cross-validation results for sO2PLS — so2pls_plot_cv_adj","title":"Plot adjusted cross-validation results for sO2PLS — so2pls_plot_cv_adj","text":"Plots results adjusted cross-validation sO2PLS run (OmicsPLS package)","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/so2pls_plot_cv_adj.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot adjusted cross-validation results for sO2PLS — so2pls_plot_cv_adj","text":"","code":"so2pls_plot_cv_adj(cv_res, with_labels = TRUE)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/so2pls_plot_cv_adj.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot adjusted cross-validation results for sO2PLS — so2pls_plot_cv_adj","text":"cv_res Data-frame, output crossval_o2m_adjR2 function. with_labels Boolean, whether optimal values nx ny value n displayed. Default value TRUE.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/so2pls_plot_cv_adj.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot adjusted cross-validation results for sO2PLS — so2pls_plot_cv_adj","text":"ggplot","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/so2pls_plot_cv_sparsity.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot sparsity cross-validation results for sO2PLS — so2pls_plot_cv_sparsity","title":"Plot sparsity cross-validation results for sO2PLS — so2pls_plot_cv_sparsity","text":"Plots results sparsity cross-validation sO2PLS run (OmicsPLS package).","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/so2pls_plot_cv_sparsity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot sparsity cross-validation results for sO2PLS — so2pls_plot_cv_sparsity","text":"","code":"so2pls_plot_cv_sparsity(cv_res)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/so2pls_plot_cv_sparsity.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot sparsity cross-validation results for sO2PLS — so2pls_plot_cv_sparsity","text":"cv_res List, result call so2pls_crossval_sparsity.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/so2pls_plot_cv_sparsity.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot sparsity cross-validation results for sO2PLS — so2pls_plot_cv_sparsity","text":"ggplot.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/so2pls_plot_cv_sparsity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Plot sparsity cross-validation results for sO2PLS — so2pls_plot_cv_sparsity","text":"produced plot one facet joint component. x-axis corresponds number features retained X dataset construct joint component, y-axis number features retained Y dataset construct joint component. colour point ith facet represents average covariance obtained joint ith components two datasets cross-validation folds. size points' shadow correspond covariance standard error across cross-validation folds. joint component, setting yielding maximum average covariance highlighted orange, one yielding highest average covariance 1-SD rule red.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/so2pls_plot_joint_components_coefficients.html","id":null,"dir":"Reference","previous_headings":"","what":"Plots sO2PLS contributions between datasets joint components — so2pls_plot_joint_components_coefficients","title":"Plots sO2PLS contributions between datasets joint components — so2pls_plot_joint_components_coefficients","text":"Plots regression coefficients link joint components two datasets, SO2PLS run (OmicsPLS package).","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/so2pls_plot_joint_components_coefficients.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plots sO2PLS contributions between datasets joint components — so2pls_plot_joint_components_coefficients","text":"","code":"so2pls_plot_joint_components_coefficients(so2pls_res, datasets = NULL)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/so2pls_plot_joint_components_coefficients.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plots sO2PLS contributions between datasets joint components — so2pls_plot_joint_components_coefficients","text":"so2pls_res output o2m function. datasets Optional, character vector names datasets plotted. Default NULL, .e. datasets considered.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/so2pls_plot_joint_components_coefficients.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plots sO2PLS contributions between datasets joint components — so2pls_plot_joint_components_coefficients","text":"patchwork ggplots.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/so2pls_plot_samples_joint_components.html","id":null,"dir":"Reference","previous_headings":"","what":"Plots sO2PLS joint components samples scores — so2pls_plot_samples_joint_components","title":"Plots sO2PLS joint components samples scores — so2pls_plot_samples_joint_components","text":"Plots samples scores average joint components sO2PLS run (OmicsPLS package).","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/so2pls_plot_samples_joint_components.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plots sO2PLS joint components samples scores — so2pls_plot_samples_joint_components","text":"","code":"so2pls_plot_samples_joint_components(so2pls_res, ...)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/so2pls_plot_samples_joint_components.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plots sO2PLS joint components samples scores — so2pls_plot_samples_joint_components","text":"so2pls_res output o2m function. ... arguments passed plot_samples_score().","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/so2pls_plot_samples_joint_components.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plots sO2PLS joint components samples scores — so2pls_plot_samples_joint_components","text":"ggmatrix plot.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/so2pls_plot_samples_specific_components.html","id":null,"dir":"Reference","previous_headings":"","what":"Plots sO2PLS specific components samples scores — so2pls_plot_samples_specific_components","title":"Plots sO2PLS specific components samples scores — so2pls_plot_samples_specific_components","text":"Plots samples scores datasets specific components sO2PLS run (OmicsPLS package).","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/so2pls_plot_samples_specific_components.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plots sO2PLS specific components samples scores — so2pls_plot_samples_specific_components","text":"","code":"so2pls_plot_samples_specific_components(so2pls_res, dataset = NULL, ...)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/so2pls_plot_samples_specific_components.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plots sO2PLS specific components samples scores — so2pls_plot_samples_specific_components","text":"so2pls_res output o2m function. dataset Character, name dataset specific components plotted. Default NULL, .e. specific components datasets plotted. ... arguments passed plot_samples_score().","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/so2pls_plot_samples_specific_components.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plots sO2PLS specific components samples scores — so2pls_plot_samples_specific_components","text":"list ggmatrix plots (one per dataset), one plot dataset used specify dataset.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/so2pls_plot_summary.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot summary of sO2PLS run — so2pls_plot_summary","title":"Plot summary of sO2PLS run — so2pls_plot_summary","text":"Plots summary variation sO2PLS run (OmicsPLS package).","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/so2pls_plot_summary.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot summary of sO2PLS run — so2pls_plot_summary","text":"","code":"so2pls_plot_summary(so2pls_res, datasets = NULL)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/so2pls_plot_summary.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot summary of sO2PLS run — so2pls_plot_summary","text":"so2pls_res output o2m function. datasets Optional, character vector names datasets selected features extracted. Default NULL, .e. datasets considered.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/so2pls_plot_summary.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot summary of sO2PLS run — so2pls_plot_summary","text":"patchwork ggplots.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/so2pls_print_cv_adj.html","id":null,"dir":"Reference","previous_headings":"","what":"Print adjusted cross-validation results for sO2PLS — so2pls_print_cv_adj","title":"Print adjusted cross-validation results for sO2PLS — so2pls_print_cv_adj","text":"Prints results adjusted cross-validation sO2PLS run (OmicsPLS package)","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/so2pls_print_cv_adj.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Print adjusted cross-validation results for sO2PLS — so2pls_print_cv_adj","text":"","code":"so2pls_print_cv_adj(cv_res)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/so2pls_print_cv_adj.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Print adjusted cross-validation results for sO2PLS — so2pls_print_cv_adj","text":"cv_res Data-frame, output crossval_o2m_adjR2 function.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/so2pls_print_cv_adj.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Print adjusted cross-validation results for sO2PLS — so2pls_print_cv_adj","text":"tibble.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/so2pls_print_cv_sparsity.html","id":null,"dir":"Reference","previous_headings":"","what":"Print sparsity cross-validation results for sO2PLS — so2pls_print_cv_sparsity","title":"Print sparsity cross-validation results for sO2PLS — so2pls_print_cv_sparsity","text":"Prints results sparsity cross-validation sO2PLS run (OmicsPLS package)","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/so2pls_print_cv_sparsity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Print sparsity cross-validation results for sO2PLS — so2pls_print_cv_sparsity","text":"","code":"so2pls_print_cv_sparsity(cv_res_optim)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/so2pls_print_cv_sparsity.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Print sparsity cross-validation results for sO2PLS — so2pls_print_cv_sparsity","text":"cv_res_optim Named list, output so2pls_get_optim_keep function.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/so2pls_print_cv_sparsity.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Print sparsity cross-validation results for sO2PLS — so2pls_print_cv_sparsity","text":"tibble, giving dataset (dataset column) joint component (columns) optimal number features retain, well total number features per dataset retain.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/so2pls_screeplot.html","id":null,"dir":"Reference","previous_headings":"","what":"Screeplot sO2PLS run — so2pls_screeplot","title":"Screeplot sO2PLS run — so2pls_screeplot","text":"Plots percentage variation explained latent component sO2PLS (OmicsPLS package).","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/so2pls_screeplot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Screeplot sO2PLS run — so2pls_screeplot","text":"","code":"so2pls_screeplot(so2pls_res, datasets = NULL)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/so2pls_screeplot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Screeplot sO2PLS run — so2pls_screeplot","text":"so2pls_res output o2m function. datasets Optional, character vector names datasets selected features extracted. Default NULL, .e. datasets considered.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/so2pls_screeplot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Screeplot sO2PLS run — so2pls_screeplot","text":"patchwork ggplots.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/so2pls_screeplot.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Screeplot sO2PLS run — so2pls_screeplot","text":"Note plots set possible add custom colour palette get different colours dataset (see example).","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/so2pls_screeplot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Screeplot sO2PLS run — so2pls_screeplot","text":"","code":"if (FALSE) { ## by default, same colour used for both datasets (cannot find a way to fix that cleanly) so2pls_screeplot(so2pls_final_res)  ## Add a colour palette to get different colour for each dataset so2pls_screeplot(so2pls_final_res) &  scale_fill_brewer(palette = \"Set1\", drop = F) }"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/spls_get_optim_ncomp.html","id":null,"dir":"Reference","previous_headings":"","what":"Select the optimal ncomp from sPLS cross-validation results — spls_get_optim_ncomp","title":"Select the optimal ncomp from sPLS cross-validation results — spls_get_optim_ncomp","text":"Select optimal number components compute sPLS run cross-validation results obtained mixOmics::perf() PLS sPLS result, using mean Q2.total values. Note function experimental, corresponding diagnostic plots considered selecting optimal number components use.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/spls_get_optim_ncomp.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Select the optimal ncomp from sPLS cross-validation results — spls_get_optim_ncomp","text":"","code":"spls_get_optim_ncomp(spls_perf, thr = 0.0975, min_ncomp = 1)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/spls_get_optim_ncomp.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Select the optimal ncomp from sPLS cross-validation results — spls_get_optim_ncomp","text":"spls_perf List, result mixOmics::perf(). thr Numeric, threshold used Q2 values. Default value 0.0975. min_ncomp Integer, minimum ncomp value returned. Default value 1, .e. argument play role selecting comp value. Can useful want least 2 latent components final plots.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/spls_get_optim_ncomp.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Select the optimal ncomp from sPLS cross-validation results — spls_get_optim_ncomp","text":"integer, optimal number components use sPLS run.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/spls_get_optim_ncomp.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Select the optimal ncomp from sPLS cross-validation results — spls_get_optim_ncomp","text":"selection made follows: Q2 values threshold specified thr, number components yielding highest Q2 value selected. Q2 values threshold, number components yielding lowest Q2 value selected. Q2 values increasing, number components n selected n+1 smallest number components Q2 value threshold. Q2 values decreasing, number components n selected n+1 smallest number components Q2 value threshold.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/spls_get_params.html","id":null,"dir":"Reference","previous_headings":"","what":"Get parameters from sPLS run — spls_get_params","title":"Get parameters from sPLS run — spls_get_params","text":"Extracts ncomp, keepX keepY parameters sPLS run format table.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/spls_get_params.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get parameters from sPLS run — spls_get_params","text":"","code":"spls_get_params(spls_res)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/spls_get_params.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get parameters from sPLS run — spls_get_params","text":"spls_res output spls spls_run.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/spls_get_params.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get parameters from sPLS run — spls_get_params","text":"tibble.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/spls_get_wa_coord.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes average sample coordinates for sPLS components — spls_get_wa_coord","title":"Computes average sample coordinates for sPLS components — spls_get_wa_coord","text":"Computes average sample coordinates sPLS components across two datasets.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/spls_get_wa_coord.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes average sample coordinates for sPLS components — spls_get_wa_coord","text":"","code":"spls_get_wa_coord(spls_res)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/spls_get_wa_coord.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes average sample coordinates for sPLS components — spls_get_wa_coord","text":"spls_res output spls_run() mixOmics::spls() function.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/spls_get_wa_coord.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes average sample coordinates for sPLS components — spls_get_wa_coord","text":"matrix samples coordinates, samples rows joint components columns.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/spls_plot_tune.html","id":null,"dir":"Reference","previous_headings":"","what":"Displays results of sPLS tuning — spls_plot_tune","title":"Displays results of sPLS tuning — spls_plot_tune","text":"Displays results cross-validation tune number components retain dataset sPLS run. Similar mixOmics::plot.tune.spls().","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/spls_plot_tune.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Displays results of sPLS tuning — spls_plot_tune","text":"","code":"spls_plot_tune(spls_tune_res)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/spls_plot_tune.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Displays results of sPLS tuning — spls_plot_tune","text":"spls_tune_res result spls_tune() mixOmics::tune.spls().","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/spls_plot_tune.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Displays results of sPLS tuning — spls_plot_tune","text":"ggplot.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/spls_plot_tune.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Displays results of sPLS tuning — spls_plot_tune","text":"plot displays correlation RSS latent components obtained corresponding values keepX (x-axis) keepY (y-axis) latent components full model (.e. retains features). colour points shows mean correlation/RSS across cross-validation folds, size points' shadow (gray) represents coefficient variation (COV) correlation/RSS, .e. standard error divided mean. point corresponding optimal value keepX keepY indicating red border.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/spls_plot_var.html","id":null,"dir":"Reference","previous_headings":"","what":"Plots sPLS features correlation circle — spls_plot_var","title":"Plots sPLS features correlation circle — spls_plot_var","text":"Displays sPLS correlation circle plot, uses available feature metadata display feature names.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/spls_plot_var.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plots sPLS features correlation circle — spls_plot_var","text":"","code":"spls_plot_var(   spls_res,   mo_data,   label_cols = \"feature_id\",   truncate = NULL,   ... )"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/spls_plot_var.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plots sPLS features correlation circle — spls_plot_var","text":"spls_res output mixOmics::spls() spls_run(). mo_data MultiDataSet::MultiDataSet object. label_cols Character named list character, giving dataset name column corresponding features metadata use label. one value, used datasets. list, names must correspond names datasets mo_data. Default value feature_id, .e. default ID features used label. truncate Integer, width labels truncated (avoid issues long labels plots). NULL (default value), truncation performed. ... Additional arguments passed mixOmics::plotVar().","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/spls_plot_var.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plots sPLS features correlation circle — spls_plot_var","text":"plot (see mixOmics::plotVar()).","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/spls_plot_var.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plots sPLS features correlation circle — spls_plot_var","text":"","code":"if (FALSE) { # Use the default features ID for the plot spls_plot_var(   spls_final_run,   mo_data,   \"feature_id\",   overlap = FALSE,   cex = c(3, 3),   comp = 1:2 )  # Using a different column from the feature metadata of each omics dataset spls_plot_var(   spls_final_run,   mo_presel_supervised,   c(     \"rnaseq\" = \"Name\",     \"metabolome\" = \"name\"   ),   overlap = FALSE,   cex = c(3, 3),   comp = 1:2 ) }"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/spls_run.html","id":null,"dir":"Reference","previous_headings":"","what":"Run sPLS algorithm — spls_run","title":"Run sPLS algorithm — spls_run","text":"Runs sPLS algorithm (mixOmics::spls()) mixOmics package.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/spls_run.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Run sPLS algorithm — spls_run","text":"","code":"spls_run(spls_input, ...)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/spls_run.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Run sPLS algorithm — spls_run","text":"spls_input mixOmics input object created get_input_spls(). ... Arguments passed mixOmics::spls().","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/spls_run.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Run sPLS algorithm — spls_run","text":"object class mixo.spls (keepX /keepY arguments provided) mix.pls (), see mixOmics::spls() mixOmics::pls().","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/spls_tune.html","id":null,"dir":"Reference","previous_headings":"","what":"Performs cross-validation for mixomics sPLS to select optimal keepX and keepY — spls_tune","title":"Performs cross-validation for mixomics sPLS to select optimal keepX and keepY — spls_tune","text":"Peforms cross-validation assess optimal number features retain dataset sPLS run (implemented mixOmics package).","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/spls_tune.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Performs cross-validation for mixomics sPLS to select optimal keepX and keepY — spls_tune","text":"","code":"spls_tune(spls_input, keepX = NULL, keepY = NULL, cpus = NULL, ...)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/spls_tune.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Performs cross-validation for mixomics sPLS to select optimal keepX and keepY — spls_tune","text":"spls_input mixOmics input object created get_input_mixomics_unsupervised(). keepX Numeric vector, values number features retain dataset X test. Default value NULL (default sequence values used, see details). keepY Numeric vector, values number features retain dataset Y test. Default value NULL (default sequence values used, see details). cpus Integer, number CPUs use running code parallel. advanced users, see BPPARAM argument mixOmics::tune.spls(). ... arguments passed mixOmics::tune.spls().","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/spls_tune.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Performs cross-validation for mixomics sPLS to select optimal keepX and keepY — spls_tune","text":"list (see mixOmics::tune.spls()).","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/spls_tune.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Performs cross-validation for mixomics sPLS to select optimal keepX and keepY — spls_tune","text":"value provided keepX keepY, sequence seq(5, 30, 5) used, truncated retain values inferior equal number features X Y dataset.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/subset_features.html","id":null,"dir":"Reference","previous_headings":"","what":"Subset a MultiDataSet object by feature — subset_features","title":"Subset a MultiDataSet object by feature — subset_features","text":"Subsets MultiDataSet object based list feature IDs provided.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/subset_features.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Subset a MultiDataSet object by feature — subset_features","text":"","code":"subset_features(mo_data, features_id)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/subset_features.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Subset a MultiDataSet object by feature — subset_features","text":"mo_data MultiDataSet::MultiDataSet object. features_id Character vector, vector feature IDs (across datasets) select. Also accepts lists (e.g. list vector feature IDs per dataset).","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/subset_features.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Subset a MultiDataSet object by feature — subset_features","text":"MultiDataSet::MultiDataSet object features specified.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/subset_features.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Subset a MultiDataSet object by feature — subset_features","text":"","code":"if (FALSE) { ## works with a vector of feature IDs: subset_features(mo_data, c(\"featureA\", \"featureB\", \"featureC\"))  ## or with a list of feature IDs (typically one per dataset, but doesn't ## have to be): subset_features(   mo_data,   list(     c(\"omics1_featureA\", \"omics1_featureB\", \"omics1_featureC\"),     c(\"omics2_featureA\", \"omics2_featureB\"),     c(\"omics3_featureA\", \"omics3_featureB\", \"omics3_featureC\"),   ) ) }"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/transform_bestNormalise_auto.html","id":null,"dir":"Reference","previous_headings":"","what":"Applies the bestNormalize function to rows of a matrix — transform_bestNormalise_auto","title":"Applies the bestNormalize function to rows of a matrix — transform_bestNormalise_auto","text":"Applies appropriate normalisation method feature (row) matrix, via bestNormalize function bestNormalize package.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/transform_bestNormalise_auto.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Applies the bestNormalize function to rows of a matrix — transform_bestNormalise_auto","text":"","code":"transform_bestNormalise_auto(mat, return_matrix_only = FALSE, ...)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/transform_bestNormalise_auto.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Applies the bestNormalize function to rows of a matrix — transform_bestNormalise_auto","text":"mat Numeric matrix. return_matrix_only Logical, transformed matrix returned? TRUE, function return matrix. FALSE, function instead returns list transformed data potentially information relevant transformation. Default value FALSE. ... arguments passed bestNormalize function.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/transform_bestNormalise_auto.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Applies the bestNormalize function to rows of a matrix — transform_bestNormalise_auto","text":"Depending return_matrix_only, either matrix transformed data, list following elements: transformed_data: matrix transformed data; info_transformation: named list one element per feature (row), giving details transformation applied feature (see output bestNormalize).","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/transform_bestNormalise_manual.html","id":null,"dir":"Reference","previous_headings":"","what":"Applies a normalisation method from bestNormalize to rows of a matrix — transform_bestNormalise_manual","title":"Applies a normalisation method from bestNormalize to rows of a matrix — transform_bestNormalise_manual","text":"Applies chosen normalisation method feature (row) matrix, via bestNormalize package.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/transform_bestNormalise_manual.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Applies a normalisation method from bestNormalize to rows of a matrix — transform_bestNormalise_manual","text":"","code":"transform_bestNormalise_manual(mat, method, return_matrix_only = FALSE, ...)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/transform_bestNormalise_manual.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Applies a normalisation method from bestNormalize to rows of a matrix — transform_bestNormalise_manual","text":"mat Numeric matrix. method Character, name normalisation method apply. Possible values \"arcsinh_x\", \"boxcox\", \"center_scale\", \"exp_x\", \"log_x\", \"orderNorm\", \"sqrt_x\", \"yeojohnson\". See Details. return_matrix_only Logical, transformed matrix returned? TRUE, function return matrix. FALSE, function instead returns list transformed data potentially information relevant transformation. Default value FALSE. ... arguments passed method function bestNormalize package.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/transform_bestNormalise_manual.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Applies a normalisation method from bestNormalize to rows of a matrix — transform_bestNormalise_manual","text":"Depending return_matrix_only, either matrix transformed data, list following elements: transformed_data: matrix transformed data; info_transformation: named list one element per feature (row), giving details transformation applied feature (see output bestNormalize function corresponding method).","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/transform_bestNormalise_manual.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Applies a normalisation method from bestNormalize to rows of a matrix — transform_bestNormalise_manual","text":"Applies normalisation method implemented bestNormalize package. method argument corresponds function bestNormalize package applied rows matrix. See vignette bestNormalize package information transformations.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/transform_dataset.html","id":null,"dir":"Reference","previous_headings":"","what":"Applies a transformation to a dataset from a MultiDataSet object — transform_dataset","title":"Applies a transformation to a dataset from a MultiDataSet object — transform_dataset","text":"Applies transformation dataset MultiDataSet object. Implemented transformations : Variance Stabilising Normalisation (vsn package), Variance Stabilising Transformation (DESeq2 package - count data), appropriate feature-wise normalisation BestNormalise package.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/transform_dataset.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Applies a transformation to a dataset from a MultiDataSet object — transform_dataset","text":"","code":"transform_dataset(   mo_data,   dataset,   transformation,   return_multidataset = FALSE,   return_matrix_only = FALSE,   verbose = TRUE,   method,   ... )"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/transform_dataset.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Applies a transformation to a dataset from a MultiDataSet object — transform_dataset","text":"mo_data MultiDataSet-class object. dataset Character, name dataset transform. transformation Character, transformation applied. Possible values : vsn, vst-deseq2, best-normalize-auto best-normalize-manual. See Details. return_multidataset Logical, MultiDataSet object original data replaced transformed data returned? FALSE, output function depends return_matrix_only. Default value FALSE. return_matrix_only Logical, transformed matrix returned? TRUE, function return matrix. FALSE, function instead returns list transformed data well information relevant transformation. Ignored return_multidataset TRUE. Default value FALSE. verbose Logical, information transformation printed? Default value TRUE. method Character, transformation = 'best-normalize-manual', normalisation method applied. See possible values transform_bestNormalise_manual(). Ignored transformations. ... arguments passed bestNormalize::bestNormalize() function method function bestNormalize package.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/transform_dataset.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Applies a transformation to a dataset from a MultiDataSet object — transform_dataset","text":"return_multidataset = TRUE: MultiDataSet::MultiDataSet object, original data transformed dataset replaced. return_multidataset = FALSE return_matrix_only = TRUE: matrix transformed data. return_multidataset = FALSE return_matrix_only = FALSE: list two elements, transformed_data containing matrix transformed data, info_transformation containing information transformation (depends transformation applied).","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/transform_dataset.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Applies a transformation to a dataset from a MultiDataSet object — transform_dataset","text":"Currently implemented transformations recommendations based dataset type: vsn: Variance Stabilising normalisation, implemented vsn::justvsn() function vsn package. method originally developed microarray intensities. transformation recommended microarray, metabolome, chemical intensity-based datasets. practice, applies transform_vsn() function. vst-deseq2: Variance Stabilising Transformation, implemented DESeq2::varianceStabilizingTransformation() function DESeq2 package. method applicable count data . transformation recommended RNAseq similar count-based datasets. practice, applies transform_vst() function. best-normalize-auto: appropriate normalisation method automatically selected number options, implemented bestNormalize::bestNormalize() function bestNormalize package. transformation recommended phenotypes measured different scales (since transformation method selected potentially different across features), preferably reasonable number features (less 100) avoid large computation times. practice, applies transform_bestNormalise_auto() function. best-normalize-manual: performs transformation (specified method argument) feature dataset. transformation recommended phenotypes data different phenotypes measured scale. different normalisation methods : \"arcsinh_x\": data transformed log(x + sqrt(x^2 + 1)); \"boxcox\": Box Cox transformation; \"center_scale\": data centered scaled; \"exp_x\": data transformed exp(x); \"log_x\": data transformed log_b(x+) (b either selected automatically passed arguments); \"orderNorm\": Ordered Quantile technique; \"sqrt_x\": data transformed sqrt(x + ) (selected automatically passed argument), \"yeojohnson\": Yeo-Johnson transformation.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/transform_vsn.html","id":null,"dir":"Reference","previous_headings":"","what":"Applies Variance Stabilising Normalisation (vsn) to matrix — transform_vsn","title":"Applies Variance Stabilising Normalisation (vsn) to matrix — transform_vsn","text":"Applies Variance Stabilising Normalisation performed vsn package via justvsn function.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/transform_vsn.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Applies Variance Stabilising Normalisation (vsn) to matrix — transform_vsn","text":"","code":"transform_vsn(mat, return_matrix_only = FALSE, ...)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/transform_vsn.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Applies Variance Stabilising Normalisation (vsn) to matrix — transform_vsn","text":"mat Numeric matrix. return_matrix_only Logical, transformed matrix returned? TRUE, function return matrix. FALSE, function instead returns list transformed data potentially information relevant transformation. Default value FALSE. ... arguments passed vsn2.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/transform_vsn.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Applies Variance Stabilising Normalisation (vsn) to matrix — transform_vsn","text":"Depending return_matrix_only, either matrix transformed data, list following elements: transformed_data: matrix transformed data; info_transformation: NULL.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/transform_vst.html","id":null,"dir":"Reference","previous_headings":"","what":"Applies Variance Stabilising Transformation (DESeq2) to matrix — transform_vst","title":"Applies Variance Stabilising Transformation (DESeq2) to matrix — transform_vst","text":"Applies Variance Stabilising Transformation (VST) performed DESeq2 package via varianceStabilizingTransformation function. Includes size factor normalisation prior VST. applies matrix count.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/transform_vst.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Applies Variance Stabilising Transformation (DESeq2) to matrix — transform_vst","text":"","code":"transform_vst(mat, return_matrix_only = FALSE)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/transform_vst.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Applies Variance Stabilising Transformation (DESeq2) to matrix — transform_vst","text":"mat Numeric matrix, must contain integers . return_matrix_only Logical, transformed matrix returned? TRUE, function return matrix. FALSE, function instead returns list transformed data potentially information relevant transformation. Default value FALSE.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/transform_vst.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Applies Variance Stabilising Transformation (DESeq2) to matrix — transform_vst","text":"Depending return_matrix_only, either matrix transformed data, list following elements: transformed_data: matrix transformed data; info_transformation: DESeqTransform object, details transformation.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/transformation_datasets_factory.html","id":null,"dir":"Reference","previous_headings":"","what":"Target factory for datasets transformation — transformation_datasets_factory","title":"Target factory for datasets transformation — transformation_datasets_factory","text":"Create list targets apply transformation methods one datasets MultiDataSet object.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/transformation_datasets_factory.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Target factory for datasets transformation — transformation_datasets_factory","text":"","code":"transformation_datasets_factory(   mo_data_target,   transformations,   return_matrix_only = FALSE,   target_name_prefix = \"\",   transformed_data_name = NULL,   methods,   ... )"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/transformation_datasets_factory.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Target factory for datasets transformation — transformation_datasets_factory","text":"mo_data_target Symbol, name target containing MultiDataSet object. transformations Named character vector, name element name dataset transform, corresponding element gives type transformation apply dataset (e.g. c(rnaseq = 'vst-deseq2', phenotypes = 'best-normalize-auto')). See Details list available transformations. 'best-normalize-auto' selected, need provide methods argument well. return_matrix_only Logical, transformed matrix returned transformation? TRUE, transformed matrices stored. FALSE, instead transformation, list transformed data potentially information relevant transformation saved. Default value FALSE. target_name_prefix Character, prefix add name targets created target factory. Default value \"\". transformed_data_name Character, name target containing MultiDataSet transformed data created. NULL, selected automatically. Default value NULL. methods Named character vector, gives dataset 'best-normalize-manual' transformation selected normalisation method applied. See possible values Details. ... arguments passed transform_dataset function method function bestNormalize package. relevant 'best-normalize-XX' transformations.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/transformation_datasets_factory.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Target factory for datasets transformation — transformation_datasets_factory","text":"list target objects. target_name_prefix = \"\" transformed_data_name = NULL, following targets created: transformations_spec: generates grouped tibble row corresponds one dataset tranformed, columns specifying dataset name transformation apply. transformations_runs_list: dynamic branching target runs transform_dataset function dataset. Returns list. transformed_set: target returns MultiDataSet object original data replaced transformed data.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/transformation_datasets_factory.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Target factory for datasets transformation — transformation_datasets_factory","text":"#' Currently implemented transformations recommendations based dataset type: vsn: Variance Stabilising normalisation, implemented justvsn function vsn package. method originally developed microarray intensities. practice, applies transform_vsn function. transformation recommended microarray, metabolome, chemical intensity-based datasets. vst-deseq2: Variance Stabilising Transformation, implemented varianceStabilizingTransformation function DESeq2 package. method applicable count data . practice, applies transform_vst function. transformation recommended RNAseq similar count-based datasets. best-normalize-auto: appropriate normalisation method automatically selected number options, implemented bestNormalize function bestNormalize package. practice, applies transform_bestNormalise_auto function. transformation recommended phenotypes measured different scales (since transformation method selected potentially different across phenotypes), preferably reasonable number features (less 100) avoid large computation times. best-normalize-manual: performs transformation (specified method argument) feature dataset. transformation recommended phenotypes data different phenotypes measured scale. different normalisation methods : \"arcsinh_x\": data transformed log(x + sqrt(x^2 + 1)); \"boxcox\": Box Cox transformation; \"center_scale\": data centered scaled; \"exp_x\": data transformed exp(x); \"log_x\": data transformed log_b(x+) (b either selected automatically passed arguments); \"orderNorm\": Ordered Quantile technique; \"sqrt_x\": data transformed sqrt(x + ) (selected automatically passed argument), \"yeojohnson\": Yeo-Johnson transformation.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/transformation_datasets_factory.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Target factory for datasets transformation — transformation_datasets_factory","text":"","code":"if (FALSE) { ## in the _targets.R library(moiraine)  list(   ## add code here to load the different datasets    ## the following target creates a MultiDataSet object from previously   ## created omics sets (geno_set, trans_set, etc)   tar_target(     mo_set,     create_multiomics_set(geno_set, trans_set, metabo_set, pheno_set)   ),    ## Example 1   transformation_datasets_factory(mo_set,     c(       rnaseq = \"vst-deseq2\",       metabolome = \"vsn\",       phenotypes = \"best-normalize-auto\"     ),     return_matrix_only = FALSE,     transformed_data_name = \"mo_set_transformed\"   ),    ## Example 2 - with a log2 transformation for the metabolome dataset   transformation_datasets_factory(     mo_set_complete,     c(       \"rnaseq\" = \"vst-deseq2\",       \"metabolome\" = \"best-normalize-manual\"     ),     methods = c(\"metabolome\" = \"log_x\"),     b = 2   ) ) }"}]
+[{"path":"https://bookish-disco-p832pyq.pages.github.io/LICENSE.html","id":null,"dir":"","previous_headings":"","what":"MIT License","title":"MIT License","text":"Copyright (c) 2024 moiraine authors Permission hereby granted, free charge, person obtaining copy software associated documentation files (“Software”), deal Software without restriction, including without limitation rights use, copy, modify, merge, publish, distribute, sublicense, /sell copies Software, permit persons Software furnished , subject following conditions: copyright notice permission notice shall included copies substantial portions Software. SOFTWARE PROVIDED “”, WITHOUT WARRANTY KIND, EXPRESS IMPLIED, INCLUDING LIMITED WARRANTIES MERCHANTABILITY, FITNESS PARTICULAR PURPOSE NONINFRINGEMENT. EVENT SHALL AUTHORS COPYRIGHT HOLDERS LIABLE CLAIM, DAMAGES LIABILITY, WHETHER ACTION CONTRACT, TORT OTHERWISE, ARISING , CONNECTION SOFTWARE USE DEALINGS SOFTWARE.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Olivia Angelin-Bonnet. Author, maintainer.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Angelin-Bonnet O (2024). moiraine: Construction Reproducible Pipelines Testing Comparing Multi-omics Integration Tools. R package version 0.0.0.9000, https://bookish-disco-p832pyq.pages.github.io/, https://github.com/PlantandFoodResearch/moiraine.","code":"@Manual{,   title = {moiraine: Construction of Reproducible Pipelines for Testing and Comparing Multi-omics Integration Tools},   author = {Olivia Angelin-Bonnet},   year = {2024},   note = {R package version 0.0.0.9000, https://bookish-disco-p832pyq.pages.github.io/},   url = {https://github.com/PlantandFoodResearch/moiraine}, }"},{"path":"https://bookish-disco-p832pyq.pages.github.io/index.html","id":"moiraine","dir":"","previous_headings":"","what":"Construction of Reproducible Pipelines for Testing and Comparing Multi-omics Integration Tools","title":"Construction of Reproducible Pipelines for Testing and Comparing Multi-omics Integration Tools","text":"moiraine package facilitating construction reproducible analysis pipeline multi-omics data integration. provides functions automate data import, pre-processing, transformation, integration several tools. relies targets package generate reproducible workflows. moiraine currently supports multi-omics data integration : sPLS DIABLO mixOmics package; sO2PLS omicsPLS package; MOFA MEFISTO MOFA2 package.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"Construction of Reproducible Pipelines for Testing and Comparing Multi-omics Integration Tools","text":"can install development version moiraine GitHub :","code":"# install.packages(\"devtools\") devtools::install_github(\"PlantandFoodResearch/moiraine\")"},{"path":"https://bookish-disco-p832pyq.pages.github.io/index.html","id":"example","dir":"","previous_headings":"","what":"Example","title":"Construction of Reproducible Pipelines for Testing and Comparing Multi-omics Integration Tools","text":"get started, create new analysis pipeline associated report working directory : using moiraine, encourage get familiar targets package; manual great place start.","code":"library(moiraine)  create_targets_pipeline() create_report(\"integration_analysis_report.Rmd\")"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/MetabolomeSet-class.html","id":null,"dir":"Reference","previous_headings":"","what":"Class to contain objects describing high-throughput metabolomics assays. — MetabolomeSet","title":"Class to contain objects describing high-throughput metabolomics assays. — MetabolomeSet","text":"Container high-throughput metabolomics assays experimental metadata. MetabolomeSet class derived Biobase::eSet().","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/PhenotypeSet-class.html","id":null,"dir":"Reference","previous_headings":"","what":"Class to contain objects describing phenotypic assays. — PhenotypeSet","title":"Class to contain objects describing phenotypic assays. — PhenotypeSet","text":"Container phenotypic assays experimental metadata. PhenotypeSet class derived Biobase::eSet().","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/add_features_metadata.html","id":null,"dir":"Reference","previous_headings":"","what":"Adding data-frame to features metadata — add_features_metadata","title":"Adding data-frame to features metadata — add_features_metadata","text":"Adds information data-frame features metadata MultiDataSet object.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/add_features_metadata.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Adding data-frame to features metadata — add_features_metadata","text":"","code":"add_features_metadata(mo_data, df)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/add_features_metadata.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Adding data-frame to features metadata — add_features_metadata","text":"mo_data MultiDataSet::MultiDataSet object. df tibble data-frame features information, least columns feature_id (giving feature IDs) dataset (giving name dataset features belong), one row per feature ID. Can contain info features different datasets.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/add_features_metadata.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Adding data-frame to features metadata — add_features_metadata","text":"MultiDataSet object, info df adding corresponding features metadata.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/add_metabo-MultiDataSet-MetabolomeSet-method.html","id":null,"dir":"Reference","previous_headings":"","what":"Adds a MetabolomeSet to a MultiDataSet object. — add_metabo,MultiDataSet,MetabolomeSet-method","title":"Adds a MetabolomeSet to a MultiDataSet object. — add_metabo,MultiDataSet,MetabolomeSet-method","text":"Adds MetabolomeSet MultiDataSet object.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/add_metabo-MultiDataSet-MetabolomeSet-method.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Adds a MetabolomeSet to a MultiDataSet object. — add_metabo,MultiDataSet,MetabolomeSet-method","text":"","code":"# S4 method for MultiDataSet,MetabolomeSet add_metabo(object, met_set, warnings = TRUE, ...)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/add_metabo-MultiDataSet-MetabolomeSet-method.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Adds a MetabolomeSet to a MultiDataSet object. — add_metabo,MultiDataSet,MetabolomeSet-method","text":"object MultiDataSet::MultiDataSet object. met_set MetabolomeSet object. warnings Logical, warnings displayed? Default TRUE. ... arguments passed MultiDataSet::add_eset() function.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/add_metabo-MultiDataSet-MetabolomeSet-method.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Adds a MetabolomeSet to a MultiDataSet object. — add_metabo,MultiDataSet,MetabolomeSet-method","text":"new MultiDataSet::MultiDataSet object slot filled.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/add_metabo-methods.html","id":null,"dir":"Reference","previous_headings":"","what":"Method to add a MetabolomeSet to a MultiDataSet object. — add_metabo","title":"Method to add a MetabolomeSet to a MultiDataSet object. — add_metabo","text":"Method add MetabolomeSet MultiDataSet object.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/add_metabo-methods.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Method to add a MetabolomeSet to a MultiDataSet object. — add_metabo","text":"","code":"add_metabo(object, met_set, warnings = TRUE, ...)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/add_metabo-methods.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Method to add a MetabolomeSet to a MultiDataSet object. — add_metabo","text":"object MultiDataSet::MultiDataSet object. met_set MetabolomeSet object. warnings Logical, warnings displayed? Default TRUE. ... arguments passed MultiDataSet::add_eset() function.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/add_metabo-methods.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Method to add a MetabolomeSet to a MultiDataSet object. — add_metabo","text":"new MultiDataSet::MultiDataSet object slot filled.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/add_omics_set.html","id":null,"dir":"Reference","previous_headings":"","what":"Adds an omics set to a MultiDataSet object — add_omics_set","title":"Adds an omics set to a MultiDataSet object — add_omics_set","text":"Adds omics set existing MultiDataSet object.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/add_omics_set.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Adds an omics set to a MultiDataSet object — add_omics_set","text":"","code":"add_omics_set(mo_data, omics_set, ds_name, ...)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/add_omics_set.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Adds an omics set to a MultiDataSet object — add_omics_set","text":"mo_data MultiDataSet::MultiDataSet object. omics_set Biobase::eSet object, created via create_omics_set(). Currently accepted objects: Biobase::SnpSet, Biobase::ExpressionSet, MetabolomeSet, PhenotypeSet. ds_name Character, name dataset (used suffix name dataset resulting MultiDataSet object). ... arguments passed [MultiDataSet::add_snps()], [MultiDataSet::add_rnaseq()], [add_metabo()] [add_pheno()] (depending omics_set` class).","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/add_omics_set.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Adds an omics set to a MultiDataSet object — add_omics_set","text":"MultiDataSet::MultiDataSet object, mo_data omics_set additional dataset.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/add_omics_set.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Adds an omics set to a MultiDataSet object — add_omics_set","text":"","code":"if (FALSE) { add_omics_set(mo_data, omics_set, \"exp1\") }"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/add_pheno-MultiDataSet-PhenotypeSet-method.html","id":null,"dir":"Reference","previous_headings":"","what":"Adds a PhenotypeSet to a MultiDataSet object. — add_pheno,MultiDataSet,PhenotypeSet-method","title":"Adds a PhenotypeSet to a MultiDataSet object. — add_pheno,MultiDataSet,PhenotypeSet-method","text":"Adds PhenotypeSet MultiDataSet object.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/add_pheno-MultiDataSet-PhenotypeSet-method.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Adds a PhenotypeSet to a MultiDataSet object. — add_pheno,MultiDataSet,PhenotypeSet-method","text":"","code":"# S4 method for MultiDataSet,PhenotypeSet add_pheno(object, pheno_set, warnings = TRUE, ...)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/add_pheno-MultiDataSet-PhenotypeSet-method.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Adds a PhenotypeSet to a MultiDataSet object. — add_pheno,MultiDataSet,PhenotypeSet-method","text":"object MultiDataSet::MultiDataSet object. pheno_set PhenotypeSet object. warnings Logical, warnings displayed? Default TRUE. ... arguments passed MultiDataSet::add_eset() function.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/add_pheno-MultiDataSet-PhenotypeSet-method.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Adds a PhenotypeSet to a MultiDataSet object. — add_pheno,MultiDataSet,PhenotypeSet-method","text":"new MultiDataSet::MultiDataSet object slot filled.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/add_pheno-methods.html","id":null,"dir":"Reference","previous_headings":"","what":"Method to add a PhenotypeSet to a MultiDataSet object. — add_pheno","title":"Method to add a PhenotypeSet to a MultiDataSet object. — add_pheno","text":"Method add PhenotypeSet MultiDataSet object.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/add_pheno-methods.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Method to add a PhenotypeSet to a MultiDataSet object. — add_pheno","text":"","code":"add_pheno(object, pheno_set, warnings = TRUE, ...)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/add_pheno-methods.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Method to add a PhenotypeSet to a MultiDataSet object. — add_pheno","text":"object MultiDataSet::MultiDataSet object. pheno_set PhenotypeSet object. warnings Logical, warnings displayed? Default TRUE. ... arguments passed MultiDataSet::add_eset() function.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/add_pheno-methods.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Method to add a PhenotypeSet to a MultiDataSet object. — add_pheno","text":"new MultiDataSet::MultiDataSet object slot filled.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/add_samples_metadata.html","id":null,"dir":"Reference","previous_headings":"","what":"Adding data-frame to samples metadata — add_samples_metadata","title":"Adding data-frame to samples metadata — add_samples_metadata","text":"Adds information data-frame samples metadata MultiDataSet object.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/add_samples_metadata.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Adding data-frame to samples metadata — add_samples_metadata","text":"","code":"add_samples_metadata(mo_data, df, datasets = NULL)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/add_samples_metadata.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Adding data-frame to samples metadata — add_samples_metadata","text":"mo_data MultiDataSet::MultiDataSet object. df tibble data-frame samples information, least column id (giving sample IDs), one row per sample ID. datasets Character vector, name datasets samples information added. NULL (default value), information added samples metadata datasets.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/add_samples_metadata.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Adding data-frame to samples metadata — add_samples_metadata","text":"MultiDataSet object, info df added corresponding samples metadata.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/check_feature_sets.html","id":null,"dir":"Reference","previous_headings":"","what":"Checks features assignment to sets — check_feature_sets","title":"Checks features assignment to sets — check_feature_sets","text":"Checks proportion features multi-omics dataset assigned feature sets.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/check_feature_sets.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Checks features assignment to sets — check_feature_sets","text":"","code":"check_feature_sets(feature_sets, mo_data, datasets = names(mo_data))"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/check_feature_sets.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Checks features assignment to sets — check_feature_sets","text":"feature_sets Named list, element corresponds feature set, contains vector features ID features belonging set. mo_data MultiDataSet-class object. datasets Character vector, names datasets features assignment checked. default, datasets mo_data considered.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/check_feature_sets.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Checks features assignment to sets — check_feature_sets","text":"tibble giving dataset number fraction features assigned least one feature set. message column meant facilitate reporting.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/check_input_multidataset.html","id":null,"dir":"Reference","previous_headings":"","what":"Check a MultiDataSet input — check_input_multidataset","title":"Check a MultiDataSet input — check_input_multidataset","text":"Checks MultiDataSet object provided input. particular, checks input object MultiDataSet object, 2) datasets stored match datasets named provided (). restrict MultiDataSet object necessary datasets.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/check_input_multidataset.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check a MultiDataSet input — check_input_multidataset","text":"","code":"check_input_multidataset(x, datasets = NULL)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/check_input_multidataset.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check a MultiDataSet input — check_input_multidataset","text":"x input object hopefully  MultiDataSet object. datasets Character vector dataset names x. NULL (default value), dataset names checked","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/check_input_multidataset.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check a MultiDataSet input — check_input_multidataset","text":"MultiDataSet object restricted datasets required (datasets NULL.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/check_is_multidataset.html","id":null,"dir":"Reference","previous_headings":"","what":"Checks whether object is MultiDataSet — check_is_multidataset","title":"Checks whether object is MultiDataSet — check_is_multidataset","text":"Checks whether input object MultiDataSet::MultiDataSet object.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/check_is_multidataset.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Checks whether object is MultiDataSet — check_is_multidataset","text":"","code":"check_is_multidataset(x)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/check_is_multidataset.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Checks whether object is MultiDataSet — check_is_multidataset","text":"x object.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/check_is_multidataset.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Checks whether object is MultiDataSet — check_is_multidataset","text":"nothing.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/check_missing_values.html","id":null,"dir":"Reference","previous_headings":"","what":"Check for missing values in MultiDataSet — check_missing_values","title":"Check for missing values in MultiDataSet — check_missing_values","text":"Checks missing values omics dataset MultiDataSet object.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/check_missing_values.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check for missing values in MultiDataSet — check_missing_values","text":"","code":"check_missing_values(mo_data)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/check_missing_values.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check for missing values in MultiDataSet — check_missing_values","text":"mo_data MultiDataSet::MultiDataSet object.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/check_missing_values.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check for missing values in MultiDataSet — check_missing_values","text":"Invisible logical vector indicating whether missing values present dataset.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/comparison_heatmap_corr.html","id":null,"dir":"Reference","previous_headings":"","what":"Heatmap of correlation between latent dimensions — comparison_heatmap_corr","title":"Heatmap of correlation between latent dimensions — comparison_heatmap_corr","text":"Constructs heatmap displaying correlation latent dimensions constructed several integration methods. lower triangle heatmap displays correlation features weight, upper triangle shows correlation samples score. triangle matrix reordered separately show highly correlated dimensions next .","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/comparison_heatmap_corr.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Heatmap of correlation between latent dimensions — comparison_heatmap_corr","text":"","code":"comparison_heatmap_corr(   output_list,   latent_dimensions = NULL,   include_missing_features = FALSE,   legend_ncol = 1 )"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/comparison_heatmap_corr.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Heatmap of correlation between latent dimensions — comparison_heatmap_corr","text":"output_list List integration methods output generated via get_output() function. named, names used annotate plot. See details. latent_dimensions Named list, element character vector giving latent dimensions retain corresponding element output_list. Names must match output_list. Can used filter latent dimensions certain elements output_list (see examples). NULL (default value), latent dimensions used. include_missing_features Logical, see get_features_weight_correlation() details. Default value FALSE. legend_ncol Integer, number columns legend. Default value 1.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/comparison_heatmap_corr.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Heatmap of correlation between latent dimensions — comparison_heatmap_corr","text":"ComplexHeatmap::Heatmap.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/comparison_heatmap_corr.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Heatmap of correlation between latent dimensions — comparison_heatmap_corr","text":"output_list unnamed, different elements list differentiated name method used produce (e.g. DIABLO, sO2PLS, etc). order compare different results integration method (e.g. DIABLO applied full vs pre-filtered data), possible assign names elements output_list (see examples). names used place method name plot identify latent dimensions come .","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/comparison_heatmap_corr.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Heatmap of correlation between latent dimensions — comparison_heatmap_corr","text":"","code":"if (FALSE) { ## Comparing the output from DIABLO, sO2PLS and MOFA  res <- list(   get_output_diablo(diablo_res), ## diablo_res: output from diablo_run()   get_output_so2pls(so2pls_res), ## so2pls_res: output from so2pls_o2m()   get_output_mofa2(mofa_res) ## mofa_res: output from run_mofa )  comparison_heatmap_corr(res)  ## Selecting only some factors from a MOFA run for the comparison ## (for the other methods, all latent dimensions will be retained) comparison_heatmap_corr(   res,   latent_dimensions = list(     \"MOFA\" = paste0(\"Factor \", 1:3)   ) )  ## Comparing two different results from a same integration method - ## diablo_run_full and diablo_run_prefiltered would both be output ## from the diablo_run() function.  res <- list(   \"DIABLO full\" = get_output_diablo(diablo_run_full),   \"DIABLO prefiltered\" = get_output_diablo(diablo_run_prefiltered) )  comparison_heatmap_corr(res) }"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/comparison_plot_correlation.html","id":null,"dir":"Reference","previous_headings":"","what":"Correlation plot between latent components — comparison_plot_correlation","title":"Correlation plot between latent components — comparison_plot_correlation","text":"Plots correlation either samples score features weight latent components obtained two different integration methods.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/comparison_plot_correlation.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Correlation plot between latent components — comparison_plot_correlation","text":"","code":"comparison_plot_correlation(   output_list,   by = \"both\",   latent_dimensions = NULL,   include_missing_features = FALSE,   show_cor = TRUE,   min_show_cor = 0.2,   round_cor = 2 )"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/comparison_plot_correlation.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Correlation plot between latent components — comparison_plot_correlation","text":"output_list List length 2 integration methods output, generated via get_output() function. named, names used annotate plot. See details. Character, correlation calculated based samples score ( = 'samples') features weight (= 'features'), (= '', .e. two matrices plotted). Default value ''. latent_dimensions Named list, element character vector giving latent dimensions retain corresponding element output_list. Names must match output_list. Can used filter latent dimensions certain elements output_list (see examples). NULL (default value), latent dimensions used. include_missing_features Logical, see get_features_weight_correlation details. Default value FALSE. show_cor Logical, correlation values added plot? Default value TRUE. min_show_cor Numeric, minimum value correlation coefficients values added plot (.e. circle appear values text). Ignored show_cor FALSE. Default value 0.2. round_cor Integer, many decimal places show correlation coefficients. Ignored show_cor FALSE. Default value 2.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/comparison_plot_correlation.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Correlation plot between latent components — comparison_plot_correlation","text":"ggplot.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/comparison_plot_correlation.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Correlation plot between latent components — comparison_plot_correlation","text":"output_list unnamed, different elements list differentiated name method used produce (e.g. DIABLO, sO2PLS, etc). order compare different results integration method (e.g. DIABLO applied full vs pre-filtered data), possible assign names elements output_list. names used place method name plot identify latent dimensions come .","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/compute_consensus_importance.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes consensus feature importance — compute_consensus_importance","title":"Computes consensus feature importance — compute_consensus_importance","text":"Computes consensus feature importance features weight obtained different integration methods (considering features importance one latent component per integration method), different latent dimensions constructed integration method.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/compute_consensus_importance.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes consensus feature importance — compute_consensus_importance","text":"","code":"compute_consensus_importance(   output_list,   latent_dimensions,   metric = \"geometric\",   include_missing_features = FALSE )"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/compute_consensus_importance.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes consensus feature importance — compute_consensus_importance","text":"output_list List integration methods output, generated via get_output() function, single integration method output (get_output()). latent_dimensions Named list (output_list list), element character giving latent dimension retain corresponding element output_list (1 value). output_list single output object, needs instead character vector giving latent dimensions retain. metric Character, one metrics use compute consensus score. Can one 'min', 'max', 'average', 'product', 'l2' (L2-norm), 'geometric' (geometric mean) 'harmonic' (harmonic mean). Default value 'geometric'. Names must match output_list. include_missing_features Logical, whether features missing output included calculation (see Details). Default value FALSE.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/compute_consensus_importance.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes consensus feature importance — compute_consensus_importance","text":"tibble giving consensus importance feature.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/compute_consensus_importance.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Computes consensus feature importance — compute_consensus_importance","text":"include_missing_features FALSE (default behaviour), features present output one integration method (e.g. different pre-filtering applied input data two methods), features ignored. mean features selected one method discarded; case feature assigned weight 0 method select . recommended behaviour, changed specific scenarios (e.g. check whether using features dataset vs variance-based preselection affect features deemed important). include_missing_features TRUE, missing features assigned weight 0. Note geometric harmonic means work strictly positive values. Therefore, importance scores 0 replaced offset computing metrics. offset calculated per dataset, corresponds minimum non-null importance score observed across features dataset (across latent dimensions), divided 2. calculation offset done removing missing features (include_missing_features = FALSE) results consistent two options include_missing_features.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/compute_samples_silhouette.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes samples silhouette score from method output — compute_samples_silhouette","title":"Computes samples silhouette score from method output — compute_samples_silhouette","text":"Calculates samples silhouette width results dimension reduction method, according samples grouping samples metadata MultiDataSet object.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/compute_samples_silhouette.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes samples silhouette score from method output — compute_samples_silhouette","text":"","code":"compute_samples_silhouette(   method_output,   mo_data,   group_col,   latent_dimensions = NULL,   distance_metric = c(\"euclidean\", \"maximum\", \"manhattan\", \"canberra\", \"binary\",     \"minkowski\") )"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/compute_samples_silhouette.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes samples silhouette score from method output — compute_samples_silhouette","text":"method_output method_output Integration method output generated via get_output() function. mo_data MultiDataSet-class object. group_col Character, name column one samples metadata table mo_data containing samples grouping used. latent_dimensions Character vector, latent dimensions use computing distance samples. NULL (default value), latent dimensions used. distance_metric Character, name metric use computing distance samples coordinates latent dimensions. passed stats::dist() function. Options include: \"euclidean\" (default value), \"maximum\", \"manhattan\", \"canberra\", \"binary\" \"minkowski\".","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/compute_samples_silhouette.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes samples silhouette score from method output — compute_samples_silhouette","text":"list two elements: samples_silhouette: tibble giving sample (sample_id column) group belongs (group column), closest () group space spanned latent dimensions (neighbour_group), silhouette width (silhouette_width column). groups_average_silhouette: tibble giving samples group (group column) average silhouette width (group_average_width column).","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/compute_samples_silhouette.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Computes samples silhouette score from method output — compute_samples_silhouette","text":"samples silhouette width groups average width calculated using cluster::silhouette() function.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/consensus_importance_metric.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate features importance score — consensus_importance_metric","title":"Calculate features importance score — consensus_importance_metric","text":"Given vector features importance (1 0), returns consensus importance score.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/consensus_importance_metric.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate features importance score — consensus_importance_metric","text":"","code":"consensus_importance_metric(x, metric, offset = 0)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/consensus_importance_metric.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate features importance score — consensus_importance_metric","text":"x Numeric vector importance values (0 1). metric Character, one metrics use compute consensus score. Can one 'min', 'max', 'average', 'product', 'l2' (L2-norm), 'geometric' (geometric mean) 'harmonic' (harmonic mean). offset Numeric (strictly positive), used replace zero values compute geometric harmonic mean. 0 (default value), zero values ignored calculating geometric harmonic mean. Accepting vector values facilitate use whithin dplyr::summarise().","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/consensus_importance_metric.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate features importance score — consensus_importance_metric","text":"numeric value, importance consensus score.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/create_multiomics_set.html","id":null,"dir":"Reference","previous_headings":"","what":"Create a MultiDataSet object to store multi-omics data — create_multiomics_set","title":"Create a MultiDataSet object to store multi-omics data — create_multiomics_set","text":"Creates MultiDataSet object list Biobase Set objects store different omics sets.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/create_multiomics_set.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create a MultiDataSet object to store multi-omics data — create_multiomics_set","text":"","code":"create_multiomics_set(sets_list, datasets_names = NULL, show_warnings = TRUE)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/create_multiomics_set.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create a MultiDataSet object to store multi-omics data — create_multiomics_set","text":"sets_list List Biobase::eSet objects, created via create_omics_set(). Currently accepted objects: Biobase::SnpSet, Biobase::ExpressionSet, MetabolomeSet, PhenotypeSet. datasets_names Optional, vector character, name Set object. appended data type resulting object. sets_list list contains several objects data type (e.g. several SnpSets), names must unique. \"\" provided, name appended data type corresponding dataset. show_warnings Logical, warnings displayed adding set MultiDataSet object? Default value TRUE.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/create_multiomics_set.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create a MultiDataSet object to store multi-omics data — create_multiomics_set","text":"MultiDataSet::MultiDataSet object.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/create_multiomics_set.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create a MultiDataSet object to store multi-omics data — create_multiomics_set","text":"","code":"if (FALSE) { ## set_geno, set_transcripto and set_metabo are all Set objects ## Generating a MultiDataSet object with standard name create_multiomics_set(   list(set_geno, set_transcripto, set_metabo) )  ## Adding custom names for genomics and metabolomics datasets ## but not for the transcriptomics dataset create_multiomics_set(   list(set_geno, set_transcripto, set_metabo),   datasets_names = c(\"genome1\", \"\", \"lcms\") ) }"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/create_omics_set.html","id":null,"dir":"Reference","previous_headings":"","what":"Create a Biobase set object to store omics data — create_omics_set","title":"Create a Biobase set object to store omics data — create_omics_set","text":"Creates Biobase object store omics dataset associated samples features metadata.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/create_omics_set.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create a Biobase set object to store omics data — create_omics_set","text":"","code":"create_omics_set(   dataset,   omics_type = c(\"phenomics\", \"genomics\", \"transcriptomics\", \"metabolomics\"),   features_metadata = NULL,   samples_metadata = NULL )"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/create_omics_set.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create a Biobase set object to store omics data — create_omics_set","text":"dataset Matrix, omics dataset matrix form features rows samples columns. omics_type Character, type omics data stored? Possible values 'genomics', 'transcriptomics', 'metabolomics' 'phenomics'. Use 'phenomics' omics. features_metadata Data.frame, feature annotation data-frame features rows information features columns. number rows row names must match dataset matrix. samples_metadata Data.frame, samples information data-frame samples rows information samples columns. number rows row names must match number columns column names dataset matrix.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/create_omics_set.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create a Biobase set object to store omics data — create_omics_set","text":"object derived Biobase::eSet: omics_type = 'genomics': Biobase::SnpSet object; omics_type = 'transcriptomics': Biobase::ExpressionSet object. omics_type = 'metabolomics': MetabolomeSet object. omics_type = 'phenomics' PhenotypeSet object.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/create_omics_set.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create a Biobase set object to store omics data — create_omics_set","text":"","code":"if (FALSE) { data_geno <- import_dataset_csv( \"genotype_dataset.csv\",   col_id = \"Marker\",   features_as_rows = TRUE ) geno_info_features <- import_fmetadata_csv( \"genotype_features_info.csv\",   col_id = \"Marker\" ) samples_information <- import_smetadata_csv( \"samples_information.csv\",   col_id = \"Sample\" ) create_omics_set(   dataset = data_geno,   omics_type = \"genomics\",   features_metadata = geno_info_features,   samples_metadata = samples_information ) }"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/create_omics_set_factory.html","id":null,"dir":"Reference","previous_headings":"","what":"Target factory for omics sets creation — create_omics_set_factory","title":"Target factory for omics sets creation — create_omics_set_factory","text":"Creates list targets generate omics sets targets containing datasets, features samples metadata.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/create_omics_set_factory.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Target factory for omics sets creation — create_omics_set_factory","text":"","code":"create_omics_set_factory(   datasets,   omics_types,   features_metadatas = NULL,   samples_metadatas = NULL,   target_name_suffixes = NULL )"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/create_omics_set_factory.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Target factory for omics sets creation — create_omics_set_factory","text":"datasets Vector symbols, names targets containing omics datasets. omics_types Character vector, type omics data stored dataset? Possible values 'genomics', 'transcriptomics', 'metabolomics' 'phenomics'.  Use 'phenomics' omics. Use 'phenomics' omics. features_metadatas Vector symbols, names targets containing features metadata data-frame associated omics dataset. Use NULL feature metadata exists dataset. samples_metadatas Vector symbols, names targets containing samples metadata data-frame associated omics dataset. Use NULL samples metadata exists dataset. target_name_suffixes Character vector, suffix add name targets created target factory dataset. none provided, suffixes extracted datasets argument. Default value NULL.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/create_omics_set_factory.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Target factory for omics sets creation — create_omics_set_factory","text":"list target objects, three datasets provided, target_name_suffixes = c(\"geno\", \"transcripto\", \"metabo\"), following targets returned: set_geno, set_transcripto set_metabo.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/create_omics_set_factory.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Target factory for omics sets creation — create_omics_set_factory","text":"","code":"if (FALSE) { ## in the _targets.R library(moiraine) library(targets)  list(   ## targets to import the different datasets    ## Example where genomics dataset has no features metadata information   ## Will generate the following targets: set_geno, set_transcripto   create_omics_set_factory(     datasets = c(data_geno, data_transcripto),     omics_types = c(\"genomics\", \"transcriptomics\"),     features_metadata = c(NULL, fmeta_transcripto),     samples_metadata = c(smeta_geno, smeta_transcripto)   ) ) }"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/create_report.html","id":null,"dir":"Reference","previous_headings":"","what":"Creates Rmd report from template — create_report","title":"Creates Rmd report from template — create_report","text":"Creates Rmarkdown report present results integration analysis. function creates .Rmd file knit document.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/create_report.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Creates Rmd report from template — create_report","text":"","code":"create_report(   file,   add_sections = c(\"spls\", \"so2pls\", \"mofa\", \"diablo\", \"comparison\"),   overwrite = FALSE,   target_project = Sys.getenv(\"TAR_PROJECT\", \"main\"),   use_quarto = FALSE )"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/create_report.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Creates Rmd report from template — create_report","text":"file Name (path) file created. end .Rmd use_quarto FALSE, .qmd use_quarto TRUE. add_sections Character vector, names sections include report. Possible values 'spls', 'so2pls', 'mofa', 'diablo' 'comparison'. default, sections included. overwrite Logical, existing file overwritten? target_project Character, name current targets project (.e. value used TAR_PROJECT environment variable). none provided, read TAR_PROJECT environment variable set \"main\" former set. use_quarto Boolean, whether use Quarto report. Default value FALSE, .e. uses Rmarkdown report.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/create_report.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Creates Rmd report from template — create_report","text":"Invisible character, path name generated .Rmd (.qmd) file.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/create_targets_pipeline.html","id":null,"dir":"Reference","previous_headings":"","what":"Creates a target script file from template — create_targets_pipeline","title":"Creates a target script file from template — create_targets_pipeline","text":"Creates target script file form template multi-omics integration pipeline. function creates script file execute .","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/create_targets_pipeline.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Creates a target script file from template — create_targets_pipeline","text":"","code":"create_targets_pipeline(file = \"_targets.R\", overwrite = FALSE)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/create_targets_pipeline.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Creates a target script file from template — create_targets_pipeline","text":"file Name (path) file created. end .R. Default value (recommended) \"_targets.R\" (current directory). overwrite Logical, existing file overwritten?","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/create_targets_pipeline.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Creates a target script file from template — create_targets_pipeline","text":"file name (invisibly).","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/diablo_generate_design_matrix.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate DIABLO design matrix — diablo_generate_design_matrix","title":"Generate DIABLO design matrix — diablo_generate_design_matrix","text":"Generates design matrix DIABLO algorithm, based correlation datasets inferred pairwise PLS runs.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/diablo_generate_design_matrix.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate DIABLO design matrix — diablo_generate_design_matrix","text":"","code":"diablo_generate_design_matrix(   cormat,   threshold = 0.8,   low_val = 0.1,   high_val = 1,   y_val = 1 )"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/diablo_generate_design_matrix.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate DIABLO design matrix — diablo_generate_design_matrix","text":"cormat correlation matrix datasets, obtained diablo_get_pairwise_pls_corr. threshold Numeric, correlation value datasets considered highly correlated (see Details). Default value 0.8. low_val Numeric, value design matrix datasets highly correlated. Default value 0.1. high_val Numeric, value design matrix datasets highly correlated. Default value 1. y_val Numeric, value design matrix datasets outcome (Y). Default value 1.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/diablo_generate_design_matrix.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generate DIABLO design matrix — diablo_generate_design_matrix","text":"numeric matrix, used design matrix running block.plsda, one row per dataset one column per dataset.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/diablo_generate_design_matrix.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Generate DIABLO design matrix — diablo_generate_design_matrix","text":"Use threshold detect pairs datasets highly correlated. pairs datasets, corresponding cell design matrix set high_val. pairs datasets correlation threshold, corresponding cell design matrix set low_val.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/diablo_get_optim_ncomp.html","id":null,"dir":"Reference","previous_headings":"","what":"Get the optimal ncomp value — diablo_get_optim_ncomp","title":"Get the optimal ncomp value — diablo_get_optim_ncomp","text":"Selects optimal comp value (number components compute) DIABLO cross-validation run, given error measure distance metric.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/diablo_get_optim_ncomp.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get the optimal ncomp value — diablo_get_optim_ncomp","text":"","code":"diablo_get_optim_ncomp(   perf_res,   measure = \"Overall.BER\",   distance = \"centroids.dist\",   min_ncomp = 1 )"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/diablo_get_optim_ncomp.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get the optimal ncomp value — diablo_get_optim_ncomp","text":"perf_res cross-validation results, computed perf. measure error measure obtain optimal value; possible values 'Overall.ER' 'Overall.BER'. Default value 'Overall.BER'. distance distance metric obtain optimal value; possible values 'max.dist', 'centroids.dist' 'mahalanobis.dist'. Default value 'centroids.dist'. min_ncomp Integer, minimum ncomp value returned. Default value 1, .e. argument play role selecting comp value. Can useful want least 2 latent components final plots.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/diablo_get_optim_ncomp.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get the optimal ncomp value — diablo_get_optim_ncomp","text":"integer, optimal value ncomp use DIABLO run.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/diablo_get_pairwise_pls_corr.html","id":null,"dir":"Reference","previous_headings":"","what":"Get pairwise correlations from PLS run — diablo_get_pairwise_pls_corr","title":"Get pairwise correlations from PLS run — diablo_get_pairwise_pls_corr","text":"Computes correlation matrix datasets based first component PLS run.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/diablo_get_pairwise_pls_corr.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get pairwise correlations from PLS run — diablo_get_pairwise_pls_corr","text":"","code":"diablo_get_pairwise_pls_corr(pairwise_pls_result)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/diablo_get_pairwise_pls_corr.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get pairwise correlations from PLS run — diablo_get_pairwise_pls_corr","text":"pairwise_pls_result List containing results pairwise PLS runs, computed run_pairwise_pls function.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/diablo_get_pairwise_pls_corr.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get pairwise correlations from PLS run — diablo_get_pairwise_pls_corr","text":"matrix correlation coefficients datasets.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/diablo_get_pairwise_pls_corr.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Get pairwise correlations from PLS run — diablo_get_pairwise_pls_corr","text":"correlation coefficient two datasets computed correlation coefficient first component dataset obtained Projection Latent Structure (PLS) run, mixOmics package.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/diablo_get_pairwise_pls_corr.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Get pairwise correlations from PLS run — diablo_get_pairwise_pls_corr","text":"","code":"if (FALSE) { ## get mixomics input from MultiDataSet object mixomics_data <- get_input_mixomics_supervised(mo_set, \"outcome_group\")  ## Get the list of dataset names datasets <- setdiff(names(mixomics_data), \"Y\")  ## Get all possible pairwise combinations of dataset names ds_pairs <- utils::combn(datasets, 2)  ## run PLS for each pair of datasets pls_res_list <- lapply(1:ncol(ds_pairs), function(i) {   run_pairwise_pls(mo_set, ds_pairs[, i]) })  ## extract the pairwise correlation matrix diablo_get_pairwise_pls_corr(pls_res_list) }"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/diablo_get_params.html","id":null,"dir":"Reference","previous_headings":"","what":"Get parameters from DIABLO run — diablo_get_params","title":"Get parameters from DIABLO run — diablo_get_params","text":"Extracts ncomp keepX parameters DIABLO run format table.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/diablo_get_params.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get parameters from DIABLO run — diablo_get_params","text":"","code":"diablo_get_params(diablo_res)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/diablo_get_params.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get parameters from DIABLO run — diablo_get_params","text":"diablo_res output block.splsda diablo_run.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/diablo_get_params.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get parameters from DIABLO run — diablo_get_params","text":"tibble.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/diablo_get_wa_coord.html","id":null,"dir":"Reference","previous_headings":"","what":"Get weighted average coordinates — diablo_get_wa_coord","title":"Get weighted average coordinates — diablo_get_wa_coord","text":"Computes samples coordinates weighted average latent components space DIABLO result object.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/diablo_get_wa_coord.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get weighted average coordinates — diablo_get_wa_coord","text":"","code":"diablo_get_wa_coord(diablo_res)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/diablo_get_wa_coord.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get weighted average coordinates — diablo_get_wa_coord","text":"diablo_res output block.splsda diablo_run.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/diablo_get_wa_coord.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get weighted average coordinates — diablo_get_wa_coord","text":"matrix one row per sample one column per latent component.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/diablo_pairwise_pls_factory.html","id":null,"dir":"Reference","previous_headings":"","what":"Target factory for pairwise PLS and design matrix estimation for DIABLO run — diablo_pairwise_pls_factory","title":"Target factory for pairwise PLS and design matrix estimation for DIABLO run — diablo_pairwise_pls_factory","text":"Creates list targets perform PLS run pair datasets, uses results assess correlation datasets create design matrix DIABLO algorithm.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/diablo_pairwise_pls_factory.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Target factory for pairwise PLS and design matrix estimation for DIABLO run — diablo_pairwise_pls_factory","text":"","code":"diablo_pairwise_pls_factory(   mixomics_data,   ...,   threshold = 0.8,   low_val = 0.1,   high_val = 1,   y_val = 1,   target_name_prefix = \"\" )"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/diablo_pairwise_pls_factory.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Target factory for pairwise PLS and design matrix estimation for DIABLO run — diablo_pairwise_pls_factory","text":"mixomics_data mixOmics input object created get_input_mixomics_supervised. ... Additional parameters passed run_pairwise_pls function. threshold Numeric, correlation value datasets considered highly correlated (see Details). Default value 0.8. low_val Numeric, value design matrix datasets highly correlated. Default value 0.1. high_val Numeric, value design matrix datasets highly correlated. Default value 1. y_val Numeric, value design matrix datasets outcome (Y). Default value 1. target_name_prefix Character, prefix add name targets created factory. Default value \"\".","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/diablo_pairwise_pls_factory.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Target factory for pairwise PLS and design matrix estimation for DIABLO run — diablo_pairwise_pls_factory","text":"list targets. example, target_name_prefix = \"\", following targets created: diablo_pairs_datasets: target generates list possible pairs dataset names. diablo_pls_runs_list: dynamic branching target runs PLS algorithm possible pair datasets. target returns list PLS results pair datasets. diablo_pls_correlation_matrix: target computes PLS results list correlation matrix datasets. diablo_design_matrix: target constructs datasets correlation matrix design matrix use DIABLO algorithm.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/diablo_pairwise_pls_factory.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Target factory for pairwise PLS and design matrix estimation for DIABLO run — diablo_pairwise_pls_factory","text":"","code":"if (FALSE) { ## in the _targets.R file library(moiraine)  list(   ## code to import the datasets, etc   ## mo_set is the target containing the MultiDataSet object   tar_target(     mixomics_input,     get_input_mixomics_supervised(mo_set, \"outcome_group\")   ),   diablo_pairwise_pls_factory(mixomics_input) ) }"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/diablo_plot.html","id":null,"dir":"Reference","previous_headings":"","what":"Plots DIABLO output — diablo_plot","title":"Plots DIABLO output — diablo_plot","text":"Displays samples coordinates given latent component across datasets. copy plotDiablo function, difference margins increased accomodate title.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/diablo_plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plots DIABLO output — diablo_plot","text":"","code":"diablo_plot(   diablo_res,   ncomp = 1,   legend = TRUE,   legend.ncol,   col.per.group = NULL )"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/diablo_plot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plots DIABLO output — diablo_plot","text":"diablo_res output block.splsda diablo_run. ncomp Integer, latent component plot. legend Logical, legend added plot? Default value TRUE. legend.ncol Integer, number columns legend. none specified, calculated min(5, nlevels(diablo_res$Y)). col.per.group Named character vector, provides colours use phenotypic group. Names must match levels diablo_res$Y.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/diablo_plot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plots DIABLO output — diablo_plot","text":"None.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/diablo_plot_circos.html","id":null,"dir":"Reference","previous_headings":"","what":"Plots DIABLO circos plot — diablo_plot_circos","title":"Plots DIABLO circos plot — diablo_plot_circos","text":"Displays DIABLO circos plot, uses available feature metadata display feature names.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/diablo_plot_circos.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plots DIABLO circos plot — diablo_plot_circos","text":"","code":"diablo_plot_circos(diablo_res, mo_data, label_cols, truncate = NULL, ...)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/diablo_plot_circos.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plots DIABLO circos plot — diablo_plot_circos","text":"diablo_res output block.splsda diablo_run. mo_data MultiDataSet::MultiDataSet object. label_cols Character named list character, giving dataset name column corresponding features metadata use label. one value, used datasets. list, names must correspond names datasets mo_data. Default value feature_id, .e. default ID features used label. truncate Integer, width labels truncated (avoid issues long labels plots). NULL (default value), truncation performed. ... Additional arguments passed circosPlot.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/diablo_plot_perf.html","id":null,"dir":"Reference","previous_headings":"","what":"Plots DIABLO perf results — diablo_plot_perf","title":"Plots DIABLO perf results — diablo_plot_perf","text":"Displays error rate DIABLO run cross-validation estimate optimal number components (ncomp)","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/diablo_plot_perf.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plots DIABLO perf results — diablo_plot_perf","text":"","code":"diablo_plot_perf(perf_res)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/diablo_plot_perf.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plots DIABLO perf results — diablo_plot_perf","text":"perf_res cross-validation results, computed perf.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/diablo_plot_perf.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plots DIABLO perf results — diablo_plot_perf","text":"ggplot2 object.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/diablo_plot_tune.html","id":null,"dir":"Reference","previous_headings":"","what":"Plots DIABLO tune results — diablo_plot_tune","title":"Plots DIABLO tune results — diablo_plot_tune","text":"Displays error rate DIABLO run cross-validation estimate optimal number features retain dataset (keepX).","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/diablo_plot_tune.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plots DIABLO tune results — diablo_plot_tune","text":"","code":"diablo_plot_tune(tune_res)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/diablo_plot_tune.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plots DIABLO tune results — diablo_plot_tune","text":"tune_res cross-validation results, computed diablo_tune.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/diablo_plot_tune.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plots DIABLO tune results — diablo_plot_tune","text":"ggplot2 object.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/diablo_plot_var.html","id":null,"dir":"Reference","previous_headings":"","what":"Plots DIABLO features correlation circle — diablo_plot_var","title":"Plots DIABLO features correlation circle — diablo_plot_var","text":"Displays DIABLO correlation circle plot, uses available feature metadata display feature names.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/diablo_plot_var.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plots DIABLO features correlation circle — diablo_plot_var","text":"","code":"diablo_plot_var(   diablo_res,   mo_data,   label_cols = \"feature_id\",   truncate = NULL,   ... )"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/diablo_plot_var.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plots DIABLO features correlation circle — diablo_plot_var","text":"diablo_res output block.splsda diablo_run. mo_data MultiDataSet::MultiDataSet object. label_cols Character named list character, giving dataset name column corresponding features metadata use label. one value, used datasets. list, names must correspond names datasets mo_data. Default value feature_id, .e. default ID features used label. truncate Integer, width labels truncated (avoid issues long labels plots). NULL (default value), truncation performed. ... Additional arguments passed plotVar.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/diablo_plot_var.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plots DIABLO features correlation circle — diablo_plot_var","text":"plot (see plotVar).","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/diablo_plot_var.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plots DIABLO features correlation circle — diablo_plot_var","text":"","code":"if (FALSE) { # Use the default features ID for the plot diablo_plot_var(   diablo_final_run,   mo_data,   \"feature_id\",   overlap = FALSE,   cex = rep(2, 3),   comp = 1:2 )  # Using a different column from the feature metadata of each omics dataset diablo_plot_var(   diablo_final_run,   mo_presel_supervised,   c(     \"snps\" = \"feature_id\",     \"rnaseq\" = \"gene_name\",     \"metabolome\" = \"comp_name\"   ),   overlap = FALSE,   cex = rep(2, 3),   comp = 1:2 ) }"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/diablo_predefined_design_matrix.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate a design matrix for DIABLO — diablo_predefined_design_matrix","title":"Generate a design matrix for DIABLO — diablo_predefined_design_matrix","text":"Generates design matrix DIABLO, following predesigned pattern recommended mixOmics authors.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/diablo_predefined_design_matrix.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate a design matrix for DIABLO — diablo_predefined_design_matrix","text":"","code":"diablo_predefined_design_matrix(   datasets_name,   design_matrix = c(\"null\", \"weighted_full\", \"full\") )"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/diablo_predefined_design_matrix.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate a design matrix for DIABLO — diablo_predefined_design_matrix","text":"datasets_name Character vector, names datasets integrate. include value \"Y\" represent samples outcome groups. design_matrix Character, type design matrix generate. one \"null\", \"weighted_full\" \"full\".","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/diablo_predefined_design_matrix.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generate a design matrix for DIABLO — diablo_predefined_design_matrix","text":"matrix many rows columns length datasets_name, filled either 0 (design_matrix = \"null\"), 0.1 (design_matrix = \"weighted_full\") 1 (design_matrix = \"full\"). Values diagonal set 0, values \"Y\" row columns set 1.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/diablo_run.html","id":null,"dir":"Reference","previous_headings":"","what":"Runs DIABLO algorithm — diablo_run","title":"Runs DIABLO algorithm — diablo_run","text":"Runs DIABLO algorithm (block.splsda) mixOmics package.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/diablo_run.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Runs DIABLO algorithm — diablo_run","text":"","code":"diablo_run(mixomics_data, design_matrix, ...)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/diablo_run.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Runs DIABLO algorithm — diablo_run","text":"mixomics_data mixOmics input object created get_input_mixomics_supervised. design_matrix Either numeric matrix created diablo_generate_design_matrix, character (accepted values 'null', 'weighted_full', 'full'). See Details. ... Arguments passed block.splsda function.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/diablo_run.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Runs DIABLO algorithm — diablo_run","text":"object class block.splsda (keepX argument provided) block.splsda (), see mixOmics::block.splsda() mixOmics::block.plsda().","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/diablo_run.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Runs DIABLO algorithm — diablo_run","text":"design_matrix argument can either custom design matrix (example constructed via diablo_generate_design_matrix function); character indicating type design matrix generate. Possible values include: 'null': -diagonal elements design matrix set 0; 'weighted_full': -diagonal elements design matrix set 0.1; 'full': -diagonal elements design matrix set 1.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/diablo_table_optim_keepX.html","id":null,"dir":"Reference","previous_headings":"","what":"Formatted table with optimal keepX values — diablo_table_optim_keepX","title":"Formatted table with optimal keepX values — diablo_table_optim_keepX","text":"Produces nicely formatted table optimal number features select dataset DIABLO run according results cross-validation analysis. Used mostly producing nice report.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/diablo_table_optim_keepX.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Formatted table with optimal keepX values — diablo_table_optim_keepX","text":"","code":"diablo_table_optim_keepX(tune_res)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/diablo_table_optim_keepX.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Formatted table with optimal keepX values — diablo_table_optim_keepX","text":"tune_res cross-validation results, computed diablo_tune.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/diablo_table_optim_keepX.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Formatted table with optimal keepX values — diablo_table_optim_keepX","text":"tibble Dataset column, one column latent component Total column giving number features retain corresponding dataset across latent components.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/diablo_tune.html","id":null,"dir":"Reference","previous_headings":"","what":"Tunes keepX arg for DIABLO — diablo_tune","title":"Tunes keepX arg for DIABLO — diablo_tune","text":"Performs cross-validation estimate optimal number features retain dataset DIABLO run.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/diablo_tune.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tunes keepX arg for DIABLO — diablo_tune","text":"","code":"diablo_tune(mixomics_data, design_matrix, keepX_list = NULL, cpus = NULL, ...)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/diablo_tune.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Tunes keepX arg for DIABLO — diablo_tune","text":"mixomics_data mixOmics input object created get_input_mixomics_supervised. design_matrix Either numeric matrix created diablo_generate_design_matrix, character (accepted values 'null', 'weighted_full', 'full'). See Details. keepX_list Named list, gives omics dataset mixOmics input (.e. excluding response Y) vector values test (.e. number features return dataset). NULL (default), standard grid applied dataset latent component, testing values: seq(5, 30, 5). cpus Integer, number CPUs use running code parallel. advanced users, see BPPARAM argument tune.block.splsda. ... Arguments passed tune.block.splsda function.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/diablo_tune.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Tunes keepX arg for DIABLO — diablo_tune","text":"list, see tune.block.splsda.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/diablo_tune.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Tunes keepX arg for DIABLO — diablo_tune","text":"design_matrix argument can either custom design matrix (example constructed via diablo_generate_design_matrix function); character indicating type design matrix generate. Possible values include: 'null': -diagonal elements design matrix set 0; 'weighted_full': -diagonal elements design matrix set 0.1; 'full': -diagonal elements design matrix set 1.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/dot-add_features_labels_toplot.html","id":null,"dir":"Reference","previous_headings":"","what":"Adds features label to data-frame — .add_features_labels_toplot","title":"Adds features label to data-frame — .add_features_labels_toplot","text":"Adds features label data-frame plotting. Can extracted features metadata MultiDataSet object; otherwise use feature IDs label. labels missing, feature IDs used instead.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/dot-add_features_labels_toplot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Adds features label to data-frame — .add_features_labels_toplot","text":"","code":".add_features_labels_toplot(toplot, label_cols, mo_data, truncate = NULL)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/dot-add_features_labels_toplot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Adds features label to data-frame — .add_features_labels_toplot","text":"toplot data-frame labels added. label_cols Character named list character, giving dataset name column corresponding features metadata use label. one value, used datasets. list, names must correspond names datasets mo_data. dataset missing list value provided, feature IDs used labels. Alternatively, use feature_id get feature IDs labels. mo_data MultiDataSet object. used label_cols NULL. truncate Integer, width labels truncated (avoid issues long labels plots). NULL (default value), truncation performed.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/dot-add_features_labels_toplot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Adds features label to data-frame — .add_features_labels_toplot","text":"toplot data-frame additional column label.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/dot-check_input_var_smetadata.html","id":null,"dir":"Reference","previous_headings":"","what":"Check that variable names corresponds to columns in samples metadata — .check_input_var_smetadata","title":"Check that variable names corresponds to columns in samples metadata — .check_input_var_smetadata","text":"Checks whether variable name corresponds column samples metadata corresponding dataset. one value provided, used datasets.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/dot-check_input_var_smetadata.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check that variable names corresponds to columns in samples metadata — .check_input_var_smetadata","text":"","code":".check_input_var_smetadata(x, mo_data)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/dot-check_input_var_smetadata.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check that variable names corresponds to columns in samples metadata — .check_input_var_smetadata","text":"x Named character list, one element per dataset, element giving name column samples metadata corresponding dataset. names correspond dataset names mo_data. checked .make_var_list(). mo_data MultiDataSet object containing samples information datasets. checked check_input_multidataset().","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/dot-check_input_var_smetadata.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check that variable names corresponds to columns in samples metadata — .check_input_var_smetadata","text":"Nothing. throw error need .","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/dot-check_input_var_smetadata_common.html","id":null,"dir":"Reference","previous_headings":"","what":"Check that variable names corresponds to columns in samples metadata — .check_input_var_smetadata_common","title":"Check that variable names corresponds to columns in samples metadata — .check_input_var_smetadata_common","text":"Checks whether variable name corresponds column samples metadata corresponding dataset. one value provided, used datasets.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/dot-check_input_var_smetadata_common.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check that variable names corresponds to columns in samples metadata — .check_input_var_smetadata_common","text":"","code":".check_input_var_smetadata_common(x, mo_data)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/dot-check_input_var_smetadata_common.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check that variable names corresponds to columns in samples metadata — .check_input_var_smetadata_common","text":"x Character, name column samples metadata. mo_data MultiDataSet object containing samples information datasets. checked check_input_multidataset().","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/dot-check_input_var_smetadata_common.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check that variable names corresponds to columns in samples metadata — .check_input_var_smetadata_common","text":"Nothing. throw error need .","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/dot-check_names_output_list.html","id":null,"dir":"Reference","previous_headings":"","what":"Check names of output list — .check_names_output_list","title":"Check names of output list — .check_names_output_list","text":"Checks names list outputs several integration methods unique. named, name method used name.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/dot-check_names_output_list.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check names of output list — .check_names_output_list","text":"","code":".check_names_output_list(output_list)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/dot-check_names_output_list.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check names of output list — .check_names_output_list","text":"output_list List integration methods output, generated via one get_output_*() function.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/dot-check_names_output_list.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check names of output list — .check_names_output_list","text":"output_list (named ).","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/dot-filter_output_datasets.html","id":null,"dir":"Reference","previous_headings":"","what":"Filter datasets — .filter_output_datasets","title":"Filter datasets — .filter_output_datasets","text":"Filters datasets name output integration method.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/dot-filter_output_datasets.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Filter datasets — .filter_output_datasets","text":"","code":".filter_output_datasets(   method_output,   datasets,   fixed_length = NULL,   method_name = attr(method_output, \"method\") )"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/dot-filter_output_datasets.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Filter datasets — .filter_output_datasets","text":"method_output Integration method output generated via get_output() function. datasets Character vector giving datasets retain features weight table method's output. fixed_length Integer, expected length datasets. NULL (default value), length datasets checked. method_name Character, name method use error message.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/dot-filter_output_datasets.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Filter datasets — .filter_output_datasets","text":"Similar method_output, features weight table filtered.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/dot-filter_output_dimensions.html","id":null,"dir":"Reference","previous_headings":"","what":"Filter latent dimensions — .filter_output_dimensions","title":"Filter latent dimensions — .filter_output_dimensions","text":"Filters latent dimensions name output integration method.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/dot-filter_output_dimensions.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Filter latent dimensions — .filter_output_dimensions","text":"","code":".filter_output_dimensions(   method_output,   latent_dimensions,   fixed_length = NULL,   method_name = attr(method_output, \"method\") )"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/dot-filter_output_dimensions.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Filter latent dimensions — .filter_output_dimensions","text":"method_output Integration method output generated via get_output() function. latent_dimensions Character vector giving latent dimensions retain method's output. fixed_length Integer, expected length latent_dimensions. NULL (default value), length latent_dimensions checked. method_name Character, name method use error message.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/dot-filter_output_dimensions.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Filter latent dimensions — .filter_output_dimensions","text":"Similar method_output, samples score table features weight table filtered.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/dot-filter_output_dimensions_list.html","id":null,"dir":"Reference","previous_headings":"","what":"Filter latent dimensions in list — .filter_output_dimensions_list","title":"Filter latent dimensions in list — .filter_output_dimensions_list","text":"Filters latent dimensions name list outputs integration methods.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/dot-filter_output_dimensions_list.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Filter latent dimensions in list — .filter_output_dimensions_list","text":"","code":".filter_output_dimensions_list(   output_list,   latent_dimensions,   all_present = FALSE,   fixed_length = NULL )"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/dot-filter_output_dimensions_list.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Filter latent dimensions in list — .filter_output_dimensions_list","text":"output_list List integration method outputs generated via one get_output() function. latent_dimensions Named list, element character vector giving latent dimensions retain corresponding element output_list. Names must match output_list. all_present Logical, whether one element latent_dimensions element output_list. TRUE, error returned length names output_list latent_dimensions match. Default value FALSE. fixed_length Integer, expected length element latent_dimensions. NULL (default value), length elements latent_dimensions can vary.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/dot-filter_output_dimensions_list.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Filter latent dimensions in list — .filter_output_dimensions_list","text":"list output similar output_list, samples score table features weight table filtered.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/dot-get_initials.html","id":null,"dir":"Reference","previous_headings":"","what":"Get initials from sentence — .get_initials","title":"Get initials from sentence — .get_initials","text":"Extracts initials sentence well number.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/dot-get_initials.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get initials from sentence — .get_initials","text":"","code":".get_initials(x)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/dot-get_initials.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get initials from sentence — .get_initials","text":"x Character vector.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/dot-get_initials.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get initials from sentence — .get_initials","text":"character vector, element containing initials words x upper case plus number, pasted.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/dot-heatmap_features_weight_corr.html","id":null,"dir":"Reference","previous_headings":"","what":"Heatmap of features weight correlation — .heatmap_features_weight_corr","title":"Heatmap of features weight correlation — .heatmap_features_weight_corr","text":"Constructs lower triangle heatmap features weight correlation latent dimensions constructed several integration methods.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/dot-heatmap_features_weight_corr.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Heatmap of features weight correlation — .heatmap_features_weight_corr","text":"","code":".heatmap_features_weight_corr(   output_list,   include_missing_features = FALSE,   legend_ncol = 1 )"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/dot-heatmap_features_weight_corr.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Heatmap of features weight correlation — .heatmap_features_weight_corr","text":"output_list List integration methods output generated via get_output() function. include_missing_features Logical, see get_features_weight_correlation() details. Default value FALSE. legend_ncol Integer, number columns legend. Default value 1.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/dot-heatmap_features_weight_corr.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Heatmap of features weight correlation — .heatmap_features_weight_corr","text":"ComplexHeatmap::Heatmap (lower triangle ).","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/dot-heatmap_samples_score_corr.html","id":null,"dir":"Reference","previous_headings":"","what":"Heatmap of samples score correlation — .heatmap_samples_score_corr","title":"Heatmap of samples score correlation — .heatmap_samples_score_corr","text":"Constructs upper triangle heatmap samples score correlation latent dimensions constructed several integration methods.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/dot-heatmap_samples_score_corr.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Heatmap of samples score correlation — .heatmap_samples_score_corr","text":"","code":".heatmap_samples_score_corr(output_list, hclust_fw)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/dot-heatmap_samples_score_corr.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Heatmap of samples score correlation — .heatmap_samples_score_corr","text":"output_list List integration methods output generated via get_output() function. hclust_fw Dendrogram latent dimensions according features weight correlation (obtained .heatmap_features_weight_corr).","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/dot-heatmap_samples_score_corr.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Heatmap of samples score correlation — .heatmap_samples_score_corr","text":"ComplexHeatmap::Heatmap (upper triangle ).","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/evaluate_feature_selection_table.html","id":null,"dir":"Reference","previous_headings":"","what":"Evaluate feature selection against features label — evaluate_feature_selection_table","title":"Evaluate feature selection against features label — evaluate_feature_selection_table","text":"Compares selection features different feature labels (e.g. result DE analysis) latent dimension.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/evaluate_feature_selection_table.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Evaluate feature selection against features label — evaluate_feature_selection_table","text":"","code":"evaluate_feature_selection_table(   method_output,   mo_data,   col_names,   latent_dimensions = NULL )"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/evaluate_feature_selection_table.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Evaluate feature selection against features label — evaluate_feature_selection_table","text":"method_output Integration method output generated via get_output() function. mo_data MultiDataSet-class object. col_names Named character vector, giving dataset name column features metadata table contains features label. dataset present vector, excluded resulting table. latent_dimensions Character vector, latent dimensions include resulting table. NULL (default value), latent dimensions represented.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/evaluate_feature_selection_table.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Evaluate feature selection against features label — evaluate_feature_selection_table","text":"tibble, dataset latent dimension number selected non-selected features per feature label.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/evaluate_method_enrichment.html","id":null,"dir":"Reference","previous_headings":"","what":"Enrichment analysis for integration results — evaluate_method_enrichment","title":"Enrichment analysis for integration results — evaluate_method_enrichment","text":"Performs enrichment analysis latent dimension integration result, based user-defined feature sets. enrichment analysis done gage::gage() function gage package, using features' signed importance score features metric.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/evaluate_method_enrichment.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Enrichment analysis for integration results — evaluate_method_enrichment","text":"","code":"evaluate_method_enrichment(   method_output,   feature_sets,   datasets = NULL,   latent_dimensions = NULL,   use_abs = TRUE,   rank_test = FALSE,   min_set_size = 5,   add_missing_features = FALSE,   mo_data = NULL,   sets_info_df = NULL,   col_set = NULL )"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/evaluate_method_enrichment.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Enrichment analysis for integration results — evaluate_method_enrichment","text":"method_output Integration method output generated via get_output() function. feature_sets Named list, element corresponds feature set, contains vector features ID features belonging set. datasets Character vector, names datasets consider enrichment analysis. NULL (default value), features datasets included analysis. latent_dimensions Character vector, latent dimensions enrichment analysis performed. NULL (default value), latent dimensions analysed. use_abs Logical, whether use absolute value features metric perform enrichment. TRUE (default value), allows highlight feature sets features high weight/importance score, positive negative. FALSE, instead highlight feature sets weights sign (coordinated change). rank_test Logical, whether non-parametric Wilcoxon Mann-Whitney test used instead default two-sample t-test (.e. based features rank rather metric). Default value FALSE. min_set_size Integer, minimum number features set required order compute enrichment score set. Default value 5. add_missing_features Logical, whether features multi-omics dataset (provided mo_data argument) weight integration results (e.g. selected pre-processing step) added results. TRUE (default value), added importance score 0. mo_data MultiDataSet-class object. add_missing_features true, features multi-omics dataset weight integration method result added importance score 0. sets_info_df Data-frame, information feature sets added enrichment results. NULL (default value), information added results. col_set Character, name column sets_info_df containing set IDs. match names feature_sets list.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/evaluate_method_enrichment.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Enrichment analysis for integration results — evaluate_method_enrichment","text":"tibble enrichment results.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/evaluate_method_enrichment.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Enrichment analysis for integration results — evaluate_method_enrichment","text":"add_missing_features TRUE (default behaviour) MultiDataSet object passed mo_data argument, features present multi-omics dataset absent integration method's results added method's result weight 0. make sure , set 30 features, 25 features removed feature pre-selection stage, enrichment considers 25 features given high weights method. Otherwise, add_missing_features FALSE, 25 features ignored, enrichment analysis may find one latent dimension enriched particular set, even though 5 features 30 set contribute latent dimension. Also note multiple-testing correction applied latent dimension level, correction across latent dimensions. setting use_abs FALSE, latent dimension, enrichment features test tested twice: enrichment features positive weight/importance, features negative weight/importance score. indicated direction column resulting tibble. Note built function using gage vignette RNA-Seq Data Pathway Gene-set Analysis Workflow, section 7.1.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/feature_preselection_cov_factory.html","id":null,"dir":"Reference","previous_headings":"","what":"Target factory for feature preselection based on Coefficient of Variation — feature_preselection_cov_factory","title":"Target factory for feature preselection based on Coefficient of Variation — feature_preselection_cov_factory","text":"Creates list targets perform feature preselection datasets MultiDataSet object retaining features highest Coefficient Variation (COV).","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/feature_preselection_cov_factory.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Target factory for feature preselection based on Coefficient of Variation — feature_preselection_cov_factory","text":"","code":"feature_preselection_cov_factory(   mo_data_target,   to_keep_ns,   to_keep_props = NULL,   with_ties = TRUE,   target_name_prefix = \"\",   filtered_set_target_name = NULL )"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/feature_preselection_cov_factory.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Target factory for feature preselection based on Coefficient of Variation — feature_preselection_cov_factory","text":"mo_data_target Symbol, name target containing MultiDataSet object. to_keep_ns Named integer vector, number feature retain dataset prefiltered (names correspond dataset name). Value less number features corresponding dataset. Set NULL order use to_keep_props instead. to_keep_props Named numeric vector, proportion features retain dataset prefiltered (names correspond dataset name). Value > 0 < 1. ignored to_keep_ns NULL. with_ties ties kept together? TRUE, may return features requested. Default value TRUE. target_name_prefix Character, prefix add name targets created target factory. Default value \"\". filtered_set_target_name Character, name final target containing filtered MultiDataSet object. NULL, name automatically supplied. Default value NULL.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/feature_preselection_cov_factory.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Target factory for feature preselection based on Coefficient of Variation — feature_preselection_cov_factory","text":"list target objects. target_name_prefix = \"\" filtered_set_target_name = NULL, following targets created: cov_spec: target generates grouped tibble row corresponds one dataset filtered, columns specifying dataset name, associated values to_keep_ns, to_keep_props with_ties. cov_mat: dynamic branching target run get_dataset_matrix() function dataset. individual_cov_values: dynamic branching target runs select_features_cov_matrix() function dataset. filtered_set_cov: target retain original MultiDataSet object features selected based COV values.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/feature_preselection_cov_factory.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Target factory for feature preselection based on Coefficient of Variation — feature_preselection_cov_factory","text":"","code":"if (FALSE) { ## in the _targets.R library(moiraine)  list(   ## add code here to load the different datasets    ## the following target creates a MultiDataSet object from previously   ## created omics sets (geno_set, trans_set, etc)   tar_target(     mo_set,     create_multiomics_set(geno_set, trans_set, metabo_set, pheno_set)   ),   feature_preselection_cov_factory(     mo_set,     to_keep_ns = c(\"rnaseq\" = 1000, \"metabolome\" = 500),     filtered_set_target_name = \"mo_set_filtered\"   ),    ## Another example using to_keep_props   feature_preselection_cov_factory(     mo_set,     to_keep_ns = NULL,     to_keep_props = c(\"rnaseq\" = 0.3, \"metabolome\" = 0.5),     filtered_set_target_name = \"mo_set_filtered\"   ) ) }"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/feature_preselection_mad_factory.html","id":null,"dir":"Reference","previous_headings":"","what":"Target factory for feature preselection based on Median Absolute Deviation — feature_preselection_mad_factory","title":"Target factory for feature preselection based on Median Absolute Deviation — feature_preselection_mad_factory","text":"Creates list targets perform feature preselection datasets MultiDataSet object retaining features highest Median Absolute Deviation (MAD).","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/feature_preselection_mad_factory.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Target factory for feature preselection based on Median Absolute Deviation — feature_preselection_mad_factory","text":"","code":"feature_preselection_mad_factory(   mo_data_target,   to_keep_ns,   to_keep_props = NULL,   with_ties = TRUE,   target_name_prefix = \"\",   filtered_set_target_name = NULL )"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/feature_preselection_mad_factory.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Target factory for feature preselection based on Median Absolute Deviation — feature_preselection_mad_factory","text":"mo_data_target Symbol, name target containing MultiDataSet object. to_keep_ns Named integer vector, number feature retain dataset prefiltered (names correspond dataset name). Value less number features corresponding dataset. Set NULL order use to_keep_props instead. to_keep_props Named numeric vector, proportion features retain dataset prefiltered (names correspond dataset name). Value > 0 < 1. ignored to_keep_ns NULL. with_ties ties kept together? TRUE, may return features requested. Default value TRUE. target_name_prefix Character, prefix add name targets created target factory. Default value \"\". filtered_set_target_name Character, name final target containing filtered MultiDataSet object. NULL, name automatically supplied. Default value NULL.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/feature_preselection_mad_factory.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Target factory for feature preselection based on Median Absolute Deviation — feature_preselection_mad_factory","text":"list target objects. target_name_prefix = \"\" filtered_set_target_name = NULL, following targets created: mad_spec: target generates grouped tibble row corresponds one dataset filtered, columns specifying dataset name, associated values to_keep_ns, to_keep_props with_ties. mad_mat: dynamic branching target run get_dataset_matrix() function dataset. individual_mad_values: dynamic branching target runs select_features_mad_matrix() function dataset. filtered_set_mad: target retain original MultiDataSet object features selected based MAD values.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/feature_preselection_mad_factory.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Target factory for feature preselection based on Median Absolute Deviation — feature_preselection_mad_factory","text":"","code":"if (FALSE) { ## in the _targets.R library(moiraine)  list(   ## add code here to load the different datasets    ## the following target creates a MultiDataSet object from previously   ## created omics sets (geno_set, trans_set, etc)   tar_target(     mo_set,     create_multiomics_set(geno_set, trans_set, metabo_set, pheno_set)   ),   feature_preselection_mad_factory(     mo_set,     to_keep_ns = c(\"rnaseq\" = 1000, \"metabolome\" = 500),     filtered_set_target_name = \"mo_set_filtered\"   ),    ## Another example using to_keep_props   feature_preselection_mad_factory(     mo_set,     to_keep_ns = NULL,     to_keep_props = c(\"rnaseq\" = 0.3, \"metabolome\" = 0.5),     filtered_set_target_name = \"mo_set_filtered\"   ) ) }"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/feature_preselection_splsda_factory.html","id":null,"dir":"Reference","previous_headings":"","what":"Target factory for feature preselection based on sPLS-DA — feature_preselection_splsda_factory","title":"Target factory for feature preselection based on sPLS-DA — feature_preselection_splsda_factory","text":"Creates list targets perform feature preselection datasets MultiDataSet object sPLS-DA (mixOmics package).","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/feature_preselection_splsda_factory.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Target factory for feature preselection based on sPLS-DA — feature_preselection_splsda_factory","text":"","code":"feature_preselection_splsda_factory(   mo_data_target,   group,   to_keep_ns,   to_keep_props = NULL,   target_name_prefix = \"\",   filtered_set_target_name = NULL,   multilevel = NULL,   ... )"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/feature_preselection_splsda_factory.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Target factory for feature preselection based on sPLS-DA — feature_preselection_splsda_factory","text":"mo_data_target Symbol, name target containing MultiDataSet object. group Character, column name samples information data-frame use samples group. to_keep_ns Named integer vector, number feature retain dataset prefiltered (names correspond dataset name). Value less number features corresponding dataset. Set NULL order use to_keep_props instead. to_keep_props Named numeric vector, proportion features retain dataset prefiltered (names correspond dataset name). Value > 0 < 1. ignored to_keep_ns NULL. target_name_prefix Character, prefix add name targets created target factory. Default value \"\". filtered_set_target_name Character, name final target containing filtered MultiDataSet object. NULL, name automatically supplied. Default value NULL. multilevel Character vector length 1 3 used information repeated measurements. See get_input_splsda() details. Default value NULL (repeated measurements). ... arguments passed perf_splsda function.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/feature_preselection_splsda_factory.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Target factory for feature preselection based on sPLS-DA — feature_preselection_splsda_factory","text":"list target objects. target_name_prefix = \"\" filtered_set_target_name = NULL, following targets created: splsda_spec: generates grouped tibble row corresponds one dataset filtered, columns specifying dataset name, associated values to_keep_ns to_keep_props. individual_splsda_input: dynamic branching target runs get_input_splsda() function dataset. individual_splsda_perf: dynamic branching target runs perf_splsda() function dataset. individual_splsda_run: dynamic branching target runs run_splsda() function dataset, using results individual_splsda_perf guide number latent components construct. filtered_set_slpsda: target retain original MultiDataSet object features selected sPLS-DA run.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/feature_preselection_splsda_factory.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Target factory for feature preselection based on sPLS-DA — feature_preselection_splsda_factory","text":"","code":"if (FALSE) { ## in the _targets.R library(moiraine)  list(   ## add code here to load the different datasets    ## the following target creates a MultiDataSet object from previously   ## created omics sets (geno_set, trans_set, etc)   tar_target(     mo_set,     create_multiomics_set(geno_set, trans_set, metabo_set, pheno_set)   ),   feature_preselection_splsda_factory(     mo_set,     group = \"outcome_group\",     to_keep_ns = c(\"rnaseq\" = 1000, \"metabolome\" = 500),     filtered_set_target_name = \"mo_set_filtered\",     folds = 10 ## example of an argument passed to perf_splsda   ),    ## Another example using to_keep_props   feature_preselection_splsda_factory(     mo_set,     group = \"outcome_group\",     to_keep_ns = NULL,     to_keep_props = c(\"rnaseq\" = 0.3, \"metabolome\" = 0.5),     filtered_set_target_name = \"mo_set_filtered\",     folds = 10 ## example of an argument passed to perf_splsda   ) ) }"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_complete_data.html","id":null,"dir":"Reference","previous_headings":"","what":"Get MultiDataSet object with imputed values — get_complete_data","title":"Get MultiDataSet object with imputed values — get_complete_data","text":"Replace missing values imputed values dataset MultiDataSet object, based results Principal Component Analysis applied corresponding dataset.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_complete_data.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get MultiDataSet object with imputed values — get_complete_data","text":"","code":"get_complete_data(mo_data, pca_result)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_complete_data.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get MultiDataSet object with imputed values — get_complete_data","text":"mo_data MultiDataSet::MultiDataSet object. pca_result list element result PCA run different dataset, computed run_pca() function.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_complete_data.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get MultiDataSet object with imputed values — get_complete_data","text":"MultiDataSet::MultiDataSet object, assay dataset imputed dataset.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_complete_data.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Get MultiDataSet object with imputed values — get_complete_data","text":"Uses pcaMethods::completeObs() function impute missing values.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_dataset_matrix.html","id":null,"dir":"Reference","previous_headings":"","what":"Get multi-omics dataset as matrix — get_dataset_matrix","title":"Get multi-omics dataset as matrix — get_dataset_matrix","text":"Extracts omics dataset matrix measurements MultiDataSet object.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_dataset_matrix.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get multi-omics dataset as matrix — get_dataset_matrix","text":"","code":"get_dataset_matrix(mo_data, dataset_name, keep_dataset_name = FALSE)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_dataset_matrix.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get multi-omics dataset as matrix — get_dataset_matrix","text":"mo_data MultiDataSet::MultiDataSet object. dataset_name Character, name omics dataset extract. keep_dataset_name Logical, dataset name stored 'dataset_name' attribute resulting matrix? Default value FALSE.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_dataset_matrix.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get multi-omics dataset as matrix — get_dataset_matrix","text":"matrix measurements features rows samples columns. name dataset stored 'dataset_name' attribute keep_dataset_name TRUE.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_dataset_matrix.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Get multi-omics dataset as matrix — get_dataset_matrix","text":"","code":"if (FALSE) { ## mo_data is a MultiDataSet object with a dataset called \"rnaseq\" mat <- get_dataset_matrix(mo_data, \"rnaseq\", keep_dataset_name = TRUE) ## with keep_dataset_name = TRUE, can recover dataset name as follows: attr(mat, \"dataset_name\") }"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_datasets.html","id":null,"dir":"Reference","previous_headings":"","what":"Get multi-omics measurement datasets — get_datasets","title":"Get multi-omics measurement datasets — get_datasets","text":"Returns multi-omics datasets list matrices MultiDataSet object.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_datasets.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get multi-omics measurement datasets — get_datasets","text":"","code":"get_datasets(mo_data)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_datasets.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get multi-omics measurement datasets — get_datasets","text":"mo_data MultiDataSet::MultiDataSet object.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_datasets.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get multi-omics measurement datasets — get_datasets","text":"named list matrices, features rows samples columns.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_features.html","id":null,"dir":"Reference","previous_headings":"","what":"Get feature IDs from MultiDataSet — get_features","title":"Get feature IDs from MultiDataSet — get_features","text":"Extract list feature IDs dataset MultiDataSet object.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_features.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get feature IDs from MultiDataSet — get_features","text":"","code":"get_features(mo_data)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_features.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get feature IDs from MultiDataSet — get_features","text":"mo_data MultiDataSet::MultiDataSet object.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_features.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get feature IDs from MultiDataSet — get_features","text":"named list, one element per dataset, element character vector feature IDs.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_features_labels.html","id":null,"dir":"Reference","previous_headings":"","what":"Get feature labels — get_features_labels","title":"Get feature labels — get_features_labels","text":"Extracts feature labels MultiDataSet object given name column feature metadata dataset containing feature labels dataset.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_features_labels.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get feature labels — get_features_labels","text":"","code":"get_features_labels(mo_data, label_cols = \"feature_id\", truncate = NULL)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_features_labels.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get feature labels — get_features_labels","text":"mo_data MultiDataSet::MultiDataSet object. label_cols Character named list character, giving dataset name column corresponding features metadata use label. one value, used datasets. list, names must correspond names datasets mo_data. Default value feature_id, .e. default ID features used label. truncate Integer, width labels truncated (avoid issues long labels plots). NULL (default value), truncation performed.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_features_labels.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get feature labels — get_features_labels","text":"tibble columns dataset, feature_id label.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_features_labels.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Get feature labels — get_features_labels","text":"","code":"if (FALSE) { ## This works if each dataset in mo_data has in their features metadata table ## a column called `name` that contains the feature labels. get_features_labels(mo_data, label_cols = \"name\")  ## If instead we want to use a different column for each dataset: get_features_labels(   mo_data,   label_cols = list(     \"snps\" = \"feature_id\",     \"rnaseq\" = \"gene_name\",     \"metabolome\" = \"comp_formula\"   ) )  ## If we want to use the feature IDs as labels for the genomics dataset, ## we can simply remove it from the list (this is equivalent to the example ## above): get_features_labels(   mo_data,   label_cols = list(     \"rnaseq\" = \"gene_name\",     \"metabolome\" = \"comp_formula\"   ) ) }"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_features_metadata.html","id":null,"dir":"Reference","previous_headings":"","what":"Get features metadata dataframes from MultiDataSet — get_features_metadata","title":"Get features metadata dataframes from MultiDataSet — get_features_metadata","text":"Extracts features metadata dataframe (featureData field) dataset MultiDataSet object.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_features_metadata.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get features metadata dataframes from MultiDataSet — get_features_metadata","text":"","code":"get_features_metadata(mo_data)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_features_metadata.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get features metadata dataframes from MultiDataSet — get_features_metadata","text":"mo_data MultiDataSet::MultiDataSet object.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_features_metadata.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get features metadata dataframes from MultiDataSet — get_features_metadata","text":"named list data-frames, one per dataset mo_data object.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_features_weight_correlation.html","id":null,"dir":"Reference","previous_headings":"","what":"Get features weight correlation — get_features_weight_correlation","title":"Get features weight correlation — get_features_weight_correlation","text":"Constructs correlation matrix features weight latent dimensions obtained different integration methods.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_features_weight_correlation.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get features weight correlation — get_features_weight_correlation","text":"","code":"get_features_weight_correlation(output_list, include_missing_features = FALSE)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_features_weight_correlation.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get features weight correlation — get_features_weight_correlation","text":"output_list List integration methods output, generated via get_output() function. named, names added beginning latent dimension' label. unnamed, name integration method used instead. include_missing_features Logical, whether features missing output included calculation (see Details). Default value FALSE.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_features_weight_correlation.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get features weight correlation — get_features_weight_correlation","text":"correlation matrix.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_features_weight_correlation.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Get features weight correlation — get_features_weight_correlation","text":"include_missing_features FALSE (default behaviour), features present output one integration method (e.g. different pre-filtering applied input data two methods), features ignored. mean features selected one method discarded; case feature assigned weight 0 method select . recommended behaviour, changed specific scenarios (e.g. check whether using features dataset vs variance-based preselection affect features deemed important). include_missing_features TRUE, missing features assigned weight 0.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_filtered_dataset_splsda.html","id":null,"dir":"Reference","previous_headings":"","what":"Get filtered MultiDataSet object based on sPLS-DA runs — get_filtered_dataset_splsda","title":"Get filtered MultiDataSet object based on sPLS-DA runs — get_filtered_dataset_splsda","text":"Selects features associated phenotype interest omics datasets based results sPLS-DA applied corresponding omics datasets.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_filtered_dataset_splsda.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get filtered MultiDataSet object based on sPLS-DA runs — get_filtered_dataset_splsda","text":"","code":"get_filtered_dataset_splsda(mo_data, splsda_res_list)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_filtered_dataset_splsda.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get filtered MultiDataSet object based on sPLS-DA runs — get_filtered_dataset_splsda","text":"mo_data MultiDataSet-class object. splsda_res_list list result sPLS-DA run dataset filtered, returned run_splsda function.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_filtered_dataset_splsda.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get filtered MultiDataSet object based on sPLS-DA runs — get_filtered_dataset_splsda","text":"MultiDataSet-class object.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_filtered_dataset_splsda.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Get filtered MultiDataSet object based on sPLS-DA runs — get_filtered_dataset_splsda","text":"Note sPLS-DA method can select feature several latent components, number features retained dataset might less number specified to_keep argument.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_filtered_dataset_splsda.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Get filtered MultiDataSet object based on sPLS-DA runs — get_filtered_dataset_splsda","text":"","code":"if (FALSE) { # Goal: keep 20% of features in dataset1, and 50% of features in dataset2 # outcome_group is the outcome of interest in the samples metadata to_keep_prop <- c(\"dataset1\" = 0.2, \"dataset_2\" = 0.5)  # 1) assess optimal number of latent components for dataset1 and dataset2 splsda_perf_runs <- lapply(names(to_keep_prop), function(i) {   perf_splsda(mo_data, i, \"outcome_group\") })  # 2) run sPLS-DA with optimal number of latent components for dataset1 and dataset2 splsda_runs <- lapply(splsda_perf_runs, function(x) {   run_splsda(mo_data, x, to_keep_prop = to_keep_prop[attr(x, \"dataset_name\")]) })  # 3) Get the filtered dataset mo_data_filtered <- get_filtered_dataset_splsda(mo_data, splsda_runs) }"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_filtered_dataset_variability.html","id":null,"dir":"Reference","previous_headings":"","what":"Get filtered MultiDataSet object based on variability measure — get_filtered_dataset_variability","title":"Get filtered MultiDataSet object based on variability measure — get_filtered_dataset_variability","text":"Selects highly variable features omics datasets based features' variability (e.g. MAD COV).","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_filtered_dataset_variability.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get filtered MultiDataSet object based on variability measure — get_filtered_dataset_variability","text":"","code":"get_filtered_dataset_variability(mo_data, var_list)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_filtered_dataset_variability.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get filtered MultiDataSet object based on variability measure — get_filtered_dataset_variability","text":"mo_data MultiDataSet-class object. var_list list result MAD COV calculation dataset filtered, returned select_features_mad select_features_cov function.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_filtered_dataset_variability.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get filtered MultiDataSet object based on variability measure — get_filtered_dataset_variability","text":"MultiDataSet-class object.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_filtered_dataset_variability.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Get filtered MultiDataSet object based on variability measure — get_filtered_dataset_variability","text":"","code":"if (FALSE) { # Goal: keep 20% of features in dataset1, and 50% of features in dataset2 to_keep_prop <- c(\"dataset1\" = 0.2, \"dataset_2\" = 0.5)  # 1) compute MAD values and select features for dataset1 and dataset2 mad_list <- lapply(names(to_keep_prop), function(i) {   select_features_mad(mo_data, i, to_keep_prop[i]) })  # 2) Get the filtered dataset mo_data_filtered <- get_filtered_dataset_variability(mo_data, mad_list) }"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_input_mefisto.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate MEFISTO input data — get_input_mefisto","title":"Generate MEFISTO input data — get_input_mefisto","text":"Creates object can used input MEFISTO analysis implemented MOFA2 package. contains omics datasets well features samples metadata.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_input_mefisto.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate MEFISTO input data — get_input_mefisto","text":"","code":"get_input_mefisto(   mo_data,   covariates,   datasets = names(mo_data),   groups = NULL,   options_list = NULL,   only_common_samples = FALSE )"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_input_mefisto.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate MEFISTO input data — get_input_mefisto","text":"mo_data MultiDataSet-class object. covariates Character character vector length 2, column name(s) samples metadata data-frames use continuous covariates. datasets Character vector, names datasets mo_data include analysis. groups Character, column name samples metadata data-frames use groups (use get_samples_metadata view samples metadata data-frame dataset). options_list named list. contain 4 elements, named 'data_options', 'model_options', 'training_options' 'mefisto_options'. Provide respectively data, model, training mefisto options apply MEFISTO run. See get_default_data_options, get_default_model_options, get_default_training_options get_default_mefisto_options. only_common_samples Logical, whether samples present datasets returned. Default value FALSE.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_input_mefisto.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generate MEFISTO input data — get_input_mefisto","text":"MOFA object.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_input_mixomics_supervised.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate mixomics input data for supervised methods — get_input_mixomics_supervised","title":"Generate mixomics input data for supervised methods — get_input_mixomics_supervised","text":"Creates object can used input MixOmics package. contains omics datasets restricted common samples (missing group information) outcome group sample.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_input_mixomics_supervised.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate mixomics input data for supervised methods — get_input_mixomics_supervised","text":"","code":"get_input_mixomics_supervised(mo_data, group, datasets = names(mo_data))"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_input_mixomics_supervised.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate mixomics input data for supervised methods — get_input_mixomics_supervised","text":"mo_data MultiDataSet-class object. group Character, column name samples metadata data-frames use samples group (use get_samples_metadata view samples information data-frame dataset). column either type factor, character integer. datasets Character vector, names datasets mo_data include analysis.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_input_mixomics_supervised.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generate mixomics input data for supervised methods — get_input_mixomics_supervised","text":"list, element corresponds one omics dataset, samples rows features columns. Y element factor vector outcome group sample.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_input_mixomics_supervised.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Generate mixomics input data for supervised methods — get_input_mixomics_supervised","text":"Samples missing values group column sample metadata removed dataset.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_input_mixomics_unsupervised.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate mixomics input data for unsupervised methods — get_input_mixomics_unsupervised","title":"Generate mixomics input data for unsupervised methods — get_input_mixomics_unsupervised","text":"Creates object can used input MixOmics package. contains omics datasets restricted common samples.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_input_mixomics_unsupervised.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate mixomics input data for unsupervised methods — get_input_mixomics_unsupervised","text":"","code":"get_input_mixomics_unsupervised(mo_data, datasets = names(mo_data))"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_input_mixomics_unsupervised.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate mixomics input data for unsupervised methods — get_input_mixomics_unsupervised","text":"mo_data MultiDataSet-class object. datasets Character vector, names datasets mo_data include analysis.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_input_mixomics_unsupervised.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generate mixomics input data for unsupervised methods — get_input_mixomics_unsupervised","text":"list, element corresponds one omics dataset, samples rows features columns.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_input_mofa.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate MOFA input data — get_input_mofa","title":"Generate MOFA input data — get_input_mofa","text":"Creates object can used input MOFA analysis implemented MOFA2 package. contains omics datasets well features samples metadata.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_input_mofa.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate MOFA input data — get_input_mofa","text":"","code":"get_input_mofa(   mo_data,   datasets = names(mo_data),   groups = NULL,   options_list = NULL,   only_common_samples = FALSE )"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_input_mofa.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate MOFA input data — get_input_mofa","text":"mo_data MultiDataSet-class object. datasets Character vector, names datasets mo_data include analysis. groups Character, column name samples metadata data-frames use groups (use get_samples_metadata view samples metadata data-frame dataset). WARNING: use familiar MOFA use groups. See https://biofam.github.io/MOFA2/faq.html, section \"FAQ multi-group functionality\". options_list named list. contain 3 elements, named 'data_options', 'model_options' 'training_options'. Provide respectively data, model training options apply MOFA run. See get_default_data_options, get_default_model_options get_default_training_options. only_common_samples Logical, whether samples present datasets returned. Default value FALSE.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_input_mofa.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generate MOFA input data — get_input_mofa","text":"MOFA object.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_input_mofa2.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate input data for MOFA2 package — get_input_mofa2","title":"Generate input data for MOFA2 package — get_input_mofa2","text":"Creates object can used input MOFA MEFISTO analysis implemented MOFA2 package. contains omics datasets well features samples metadata. called directly; instead use get_input_mofa get_input_mefisto.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_input_mofa2.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate input data for MOFA2 package — get_input_mofa2","text":"","code":"get_input_mofa2(   mo_data,   datasets,   covariates,   groups,   options_list,   only_common_samples )"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_input_mofa2.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate input data for MOFA2 package — get_input_mofa2","text":"mo_data MultiDataSet-class object. datasets Character vector, names datasets mo_data include analysis. covariates Character character vector length 2, column name(s) samples metadata data-frames use continuous covariates. NULL, creates input object MOFA. null, creates input object MEFISTO. groups Character, column name samples metadata data-frames use group. options_list named list. contain 3 4 elements (depending whether input MOFA MEFISTO), named 'data_options', 'model_options', 'training_options' 'mefisto_options' (latter MEFISTO input). only_common_samples Logical, whether samples present datasets returned. Default value FALSE.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_input_mofa2.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generate input data for MOFA2 package — get_input_mofa2","text":"MOFA object.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_input_omicspls.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate omicsPLS input data — get_input_omicspls","title":"Generate omicsPLS input data — get_input_omicspls","text":"Creates object can used input omicsPLS package. contains omics datasets restricted common samples. dataset feature-centred.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_input_omicspls.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate omicsPLS input data — get_input_omicspls","text":"","code":"get_input_omicspls(mo_data, datasets = names(mo_data), scale_data = FALSE)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_input_omicspls.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate omicsPLS input data — get_input_omicspls","text":"mo_data MultiDataSet-class object. datasets Character vector length 2, names datasets mo_data include analysis. scale_data Boolean, datasets scaled? Default value FALSE.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_input_omicspls.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generate omicsPLS input data — get_input_omicspls","text":"list, element corresponds one omics dataset, samples rows features columns.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_input_spls.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate sPLS input data (for mixomics) — get_input_spls","title":"Generate sPLS input data (for mixomics) — get_input_spls","text":"Creates object can used input (s)PLS functions mixOmics package. contains two omics datasets selected, restricted common samples.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_input_spls.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate sPLS input data (for mixomics) — get_input_spls","text":"","code":"get_input_spls(mo_data, mode, datasets = names(mo_data), multilevel = NULL)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_input_spls.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate sPLS input data (for mixomics) — get_input_spls","text":"mo_data MultiDataSet::MultiDataSet object. mode Character, mode PLS use analysis (see sPLS documentation). one 'regression', 'canonical', 'invariant' 'classic'. datasets Character vector length 2, names datasets mo_data include analysis. multilevel Character vector length 1 3 used information repeated measurements. See Details. Default value NULL, .e. multilevel option used.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_input_spls.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generate sPLS input data (for mixomics) — get_input_spls","text":"list, element corresponds one omics dataset, samples rows features columns. mode use analysis stored mode attribute returned object.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_input_spls.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Generate sPLS input data (for mixomics) — get_input_spls","text":"multilevel argument: enables multilevel option (see mixOmics site) deal repeated measurements. mixOmics::spls() enables one- two-factor decomposition. one-factor decomposition, multilevel argument name column samples metadata gives ID observation units (e.g. ID subjects measured several times). resulting design matrix (stored multilevel argument returned object) data-frame one column gives ID (integer) observation units corresponding sample omics datasets. two-factor decomposition, multilevel length 3. first value, similarly one-factor decomposition, name column samples metadata gives ID observation units (e.g. ID subjects measured several times). second third values name columns samples metadata give two factors considered. resulting design matrix (stored multilevel argument returned object) data-frame three columns: first column gives ID (integer) observation units corresponding sample omics datasets; second third columns give levels two factors.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_input_splsda.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate sPLS-DA input data (for mixomics) — get_input_splsda","title":"Generate sPLS-DA input data (for mixomics) — get_input_splsda","text":"Creates object can used input (s)PLS-DA functions mixOmics package. contains omics dataset well samples group membership list.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_input_splsda.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate sPLS-DA input data (for mixomics) — get_input_splsda","text":"","code":"get_input_splsda(mo_data, dataset_name, group, multilevel = NULL)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_input_splsda.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate sPLS-DA input data (for mixomics) — get_input_splsda","text":"mo_data MultiDataSet-class object. dataset_name Character, name dataset mo_data analyse. group Character, column name samples information data-frame use samples group (use get_samples_metadata view samples information data-frame omics dataset). multilevel Character vector length 1 3 used information repeated measurements. See Details. Default value NULL (repeated measurements).","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_input_splsda.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generate sPLS-DA input data (for mixomics) — get_input_splsda","text":"list, first element corresponds omics dataset, samples rows features columns, second element (named 'Y') named factor vector, giving sample group. name dataset analysed stored dataset_name attribute returned object.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_input_splsda.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Generate sPLS-DA input data (for mixomics) — get_input_splsda","text":"multilevel argument: enables multilevel option (see mixOmics site) deal repeated measurements. mixOmics::splsda() enables one- two-factor decomposition. one-factor decomposition, multilevel argument name column samples metadata gives ID observation units (e.g. ID subjects measured several times). resulting design matrix (stored multilevel argument returned object) data-frame one column gives ID (integer) observation units corresponding sample omics datasets. two-factor decomposition, multilevel length 3. first value, similarly one-factor decomposition, name column samples metadata gives ID observation units (e.g. ID subjects measured several times). second third values name columns samples metadata give two factors considered. resulting design matrix (stored multilevel argument returned object) data-frame three columns: first column gives ID (integer) observation units corresponding sample omics datasets; second third columns give levels two factors.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_latent_dimensions.html","id":null,"dir":"Reference","previous_headings":"","what":"Get latent dimensions levels from dimension reduction output — get_latent_dimensions","title":"Get latent dimensions levels from dimension reduction output — get_latent_dimensions","text":"Extracts latent dimension levels output dimension reduction method.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_latent_dimensions.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get latent dimensions levels from dimension reduction output — get_latent_dimensions","text":"","code":"get_latent_dimensions(method_output)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_latent_dimensions.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get latent dimensions levels from dimension reduction output — get_latent_dimensions","text":"method_output Integration method output generated via get_output() function.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_latent_dimensions.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get latent dimensions levels from dimension reduction output — get_latent_dimensions","text":"character vector giving labels latent dimensions.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_output.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract output of integration method in standard format — get_output","title":"Extract output of integration method in standard format — get_output","text":"Extract samples score features weight result integration method. get_output() function provides wrapper around methods' specific get_output_*() functions.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_output.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract output of integration method in standard format — get_output","text":"","code":"get_output(method_output, use_average_dimensions = TRUE)  get_output_pca(method_output)  get_output_splsda(method_output)  get_output_spls(method_output, use_average_dimensions = TRUE)  get_output_diablo(method_output, use_average_dimensions = TRUE)  get_output_mofa2(method_output)  get_output_so2pls(method_output, use_average_dimensions = TRUE)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_output.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract output of integration method in standard format — get_output","text":"method_output output integration method. use_average_dimensions Logical, (weighted) average samples scores latent dimension across datasets used? FALSE, separate set sample scores returned dataset latent dimensions. applies sPLS, DIABLO sO2PLS results. Default value TRUE.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_output.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract output of integration method in standard format — get_output","text":"S3 object class output_dimension_reduction, .e. named list, following elements: features_weight: tibble features weight (loadings) latent dimension, columns feature_id, dataset, latent_dimension, weight (unscaled feature weight corresponding latent dimension), importance (corresponds scaled absolute weight, .e. 1 = feature maximum absolute weight corresponding latent dimension dataset, 0 = feature selected corresponding latent dimension) samples_score: tibble samples score latent component, columns sample_id, latent_dimension, score (unscaled samples score corresponding latent dimension) variance_explained: tibble fraction variance explained latent component relevant datasets.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_pca_arguments.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract arguments used in PCA run — get_pca_arguments","title":"Extract arguments used in PCA run — get_pca_arguments","text":"Extracts list arguments used PCA run list PCA results, formats tibble.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_pca_arguments.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract arguments used in PCA run — get_pca_arguments","text":"","code":"get_pca_arguments(pca_result)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_pca_arguments.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract arguments used in PCA run — get_pca_arguments","text":"pca_result result PCA run datasets, computed pcaMethods::pca() function.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_pca_arguments.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract arguments used in PCA run — get_pca_arguments","text":"tibble following columns: \"Omics dataset\", \"PCA method used\", \"Number Principal Components computed\", \"Scaling applied\" \"Dataset centered\".","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_samples.html","id":null,"dir":"Reference","previous_headings":"","what":"Get sample IDs from MultiDataSet — get_samples","title":"Get sample IDs from MultiDataSet — get_samples","text":"Extract list sample IDs dataset MultiDataSet object.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_samples.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get sample IDs from MultiDataSet — get_samples","text":"","code":"get_samples(mo_data)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_samples.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get sample IDs from MultiDataSet — get_samples","text":"mo_data MultiDataSet::MultiDataSet object.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_samples.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get sample IDs from MultiDataSet — get_samples","text":"named list, one element per dataset, element character vector sample IDs.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_samples_metadata.html","id":null,"dir":"Reference","previous_headings":"","what":"Get samples metadata dataframes from MultiDataSet — get_samples_metadata","title":"Get samples metadata dataframes from MultiDataSet — get_samples_metadata","text":"Extracts samples metadata data-frame (phenoData field) dataset MultiDataSet object.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_samples_metadata.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get samples metadata dataframes from MultiDataSet — get_samples_metadata","text":"","code":"get_samples_metadata(mo_data)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_samples_metadata.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get samples metadata dataframes from MultiDataSet — get_samples_metadata","text":"mo_data MultiDataSet::MultiDataSet object.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_samples_metadata.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get samples metadata dataframes from MultiDataSet — get_samples_metadata","text":"named list data-frames, one per dataset mo_data object.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_samples_metadata_combined.html","id":null,"dir":"Reference","previous_headings":"","what":"Get combined samples metadata data-frame from MultiDataSet — get_samples_metadata_combined","title":"Get combined samples metadata data-frame from MultiDataSet — get_samples_metadata_combined","text":"Extracts samples metadata data-frame (phenoData field) dataset MultiDataSet object combine one dataframe.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_samples_metadata_combined.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get combined samples metadata data-frame from MultiDataSet — get_samples_metadata_combined","text":"","code":"get_samples_metadata_combined(mo_data, only_common_cols = TRUE)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_samples_metadata_combined.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get combined samples metadata data-frame from MultiDataSet — get_samples_metadata_combined","text":"mo_data MultiDataSet::MultiDataSet object. only_common_cols Logical, whether retain common columns. TRUE (default value), retain columns present samples metadata datasets. FALSE, retain columns datasets' sample metadata.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_samples_metadata_combined.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get combined samples metadata data-frame from MultiDataSet — get_samples_metadata_combined","text":"data-frame samples metadata.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_samples_score_correlation.html","id":null,"dir":"Reference","previous_headings":"","what":"Get samples score correlation — get_samples_score_correlation","title":"Get samples score correlation — get_samples_score_correlation","text":"Constructs correlation matrix samples score latent dimensions obtained different integration methods.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_samples_score_correlation.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get samples score correlation — get_samples_score_correlation","text":"","code":"get_samples_score_correlation(output_list)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_samples_score_correlation.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get samples score correlation — get_samples_score_correlation","text":"output_list List integration methods output, generated via get_output() function. named, names added beginning latent dimension' label. unnamed, name integration method used instead.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_samples_score_correlation.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get samples score correlation — get_samples_score_correlation","text":"correlation matrix.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_selected_features.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract selected features — get_selected_features","title":"Extract selected features — get_selected_features","text":"Extracts selected features output integration method. features non-null weight least one latent dimension returned. MultiDataSet object supplied, information features features metadata added resulting table.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_selected_features.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract selected features — get_selected_features","text":"","code":"get_selected_features(   method_output,   latent_dimensions = NULL,   datasets = NULL,   mo_data = NULL )"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_selected_features.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract selected features — get_selected_features","text":"method_output Integration method output generated via get_output() function. latent_dimensions Character vector latent dimensions name. Default value NULL (top contributing features returned latent dimensions). datasets Character vector datasets name. Default value NULL (top contributing features returned datasets). mo_data MultiDataSet::MultiDataSet object.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_selected_features.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract selected features — get_selected_features","text":"tibble containing one row per feature latent dimension, giving weight importance score feature corresponding latent dimension. mo_data supplied, information features features metadata added resulting table.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_table_transformations.html","id":null,"dir":"Reference","previous_headings":"","what":"Get table with transformation applied to each dataset — get_table_transformations","title":"Get table with transformation applied to each dataset — get_table_transformations","text":"results transformations datasets, generates table giving dataset transformation applied .","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_table_transformations.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get table with transformation applied to each dataset — get_table_transformations","text":"","code":"get_table_transformations(   transformation_result,   best_normalize_details = FALSE )"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_table_transformations.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get table with transformation applied to each dataset — get_table_transformations","text":"transformation_result list element result transformation applied different dataset, computed transform_dataset function. best_normalize_details Logical, information transformations selected bestNormalize feature displayed? Default value FALSE.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_table_transformations.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get table with transformation applied to each dataset — get_table_transformations","text":"tibble columns 'Dataset' 'Transformation'. best_normalize_details = TRUE, additional column 'Details' lists chsoen transformation applied feature corresponding dataset bestNormalize transformation.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_top_features.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract top features — get_top_features","title":"Extract top features — get_top_features","text":"Extracts features highest contribution latent dimensions constructed integration method. Can retain specific number top contributing features dataset latent dimension, features minimum importance score.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_top_features.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract top features — get_top_features","text":"","code":"get_top_features(   method_output,   n_features = 10,   min_importance = NULL,   latent_dimensions = NULL,   datasets = NULL,   mo_data = NULL )"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_top_features.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract top features — get_top_features","text":"method_output Integration method output generated via get_output() function. n_features Integer, number features extract latent dimension dataset. Ignored min_importance set. Default value 10. include ties. min_importance Numeric value 0 1, minimum importance score used select features. Default value NULL, .e. top n_features features selected instead. latent_dimensions Character vector latent dimensions name. Default value NULL (top contributing features returned latent dimensions). datasets Character vector datasets name. Default value NULL (top contributing features returned datasets). mo_data MultiDataSet::MultiDataSet object.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_top_features.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract top features — get_top_features","text":"tibble containing one row per feature latent dimension, giving weight importance score feature corresponding latent dimension. mo_data supplied, information features features metadata added resulting table.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_transformed_data.html","id":null,"dir":"Reference","previous_headings":"","what":"Get MultiDataSet with transformed data — get_transformed_data","title":"Get MultiDataSet with transformed data — get_transformed_data","text":"Replace original datasets transformed datasets MultiDataSet object results transformations applied datasets.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_transformed_data.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get MultiDataSet with transformed data — get_transformed_data","text":"","code":"get_transformed_data(mo_data, transformation_result)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_transformed_data.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get MultiDataSet with transformed data — get_transformed_data","text":"mo_data MultiDataSet-class object. transformation_result list element result transformation applied different dataset, computed transform_dataset function.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/get_transformed_data.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get MultiDataSet with transformed data — get_transformed_data","text":"MultiDataSet-class object, assay dataset imputed dataset.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/ggpairs_custom.html","id":null,"dir":"Reference","previous_headings":"","what":"ggpairs plot with custom colours — ggpairs_custom","title":"ggpairs plot with custom colours — ggpairs_custom","text":"Creates ggpairs plot (see GGally::ggpairs()) colours shapes can differ upper triangle, lower triangle diagonal plots.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/ggpairs_custom.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"ggpairs plot with custom colours — ggpairs_custom","text":"","code":"ggpairs_custom(   toplot,   vars,   colour_upper = NULL,   colour_diag = colour_upper,   colour_lower = colour_upper,   shape_upper = NULL,   shape_lower = shape_upper,   scale_colour_upper = NULL,   scale_colour_diag = NULL,   scale_colour_lower = NULL,   scale_shape_upper = NULL,   scale_shape_lower = NULL,   title = NULL,   point_size = 1.5 )"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/ggpairs_custom.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"ggpairs plot with custom colours — ggpairs_custom","text":"toplot Tibble wide format, observations rows variables columns. vars Character vector, names columns toplot correspond variables used plot matrix. colour_upper Character, name column toplot use colouring observations upper triangle plots. Default value NULL. colour_diag Character, name column toplot use colouring observations diagonal plots. default, follow colour_upper. colour_lower Character, name column toplot use colouring observations lower triangle plots. default, follow colour_upper. shape_upper Character, name column toplot use shaping observations upper triangle plots. Default value NULL. shape_lower Character, name column toplot use shaping observations lower triangle plots. default, follow shape_upper. scale_colour_upper ggplot2 colour scale use upper triangle plots. Default value NULL (colour_upper NULL, use ggplot2 default colour scales). scale_colour_diag ggplot2 colour scale use diagonal plots. NULL (default), colour scale used upper triangle plots used colour_diag equal colour_upper; colour scale used lower triangle plots used colour_diag equal colour_lower. scale_colour_lower ggplot2 colour scale use lower triangle plots. NULL (default), colour scale used upper triangle plots used. scale_shape_upper ggplot2 shape scale use upper triangle plots. Default value NULL (shape_upper NULL, use ggplot2 default shape scale). scale_shape_lower ggplot2 shape scale use lower triangle plots. NULL (default), shape scale used upper triangle plots used. title Character, title plot. Default value NULL (title added plot). point_size Numeric, size points (pt) plot. Default value 1.5.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/ggpairs_custom.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"ggpairs plot with custom colours — ggpairs_custom","text":"ggmatrix plot.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/ggpairs_custom.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"ggpairs plot with custom colours — ggpairs_custom","text":"","code":"if (FALSE) { library(palmerpenguins) library(ggplot2) data(\"penguins\")  vars <- c(   \"bill_length_mm\",   \"bill_depth_mm\",   \"flipper_length_mm\" )  toplot <- penguins |>   dplyr::filter(!is.na(bill_length_mm))  # simple scatterplots of the variables ggpairs_custom(toplot, vars)  # colouring points by species, using custom colour palette ggpairs_custom(   toplot,   vars,   colour_upper = \"species\",   scale_colour_upper = scale_colour_brewer(palette = \"Set1\") )  # now adding the sex variable as shape of the points ggpairs_custom(   toplot,   vars,   colour_upper = \"species\",   scale_colour_upper = scale_colour_brewer(palette = \"Set1\"),   shape_upper = \"sex\" )  # using the lower plots to show the island as colour ggpairs_custom(   toplot,   vars,   colour_upper = \"species\",   scale_colour_upper = scale_colour_brewer(palette = \"Set1\"),   shape_upper = \"sex\",   colour_lower = \"island\",   scale_colour_lower = scale_colour_viridis_d() )  # showing species and sex in upper plots, body mass and island # in lower plots ggpairs_custom(   toplot,   vars,   colour_upper = \"species\",   scale_colour_upper = scale_colour_brewer(palette = \"Set1\"),   shape_upper = \"sex\",   shape_lower = \"island\",   colour_lower = \"body_mass_g\",   scale_colour_lower = scale_colour_viridis_c(option = \"plasma\") )  # same as above, but the diagonal plots show density per year ggpairs_custom(   toplot,   vars,   colour_upper = \"species\",   scale_colour_upper = scale_colour_brewer(palette = \"Set1\"),   shape_upper = \"sex\",   shape_lower = \"island\",   colour_lower = \"body_mass_g\",   scale_colour_lower = scale_colour_viridis_c(option = \"plasma\"),   colour_diag = \"year\" )  # common legend if the diagonal follows the colour of the lower plots ggpairs_custom(   toplot,   vars,   colour_upper = \"species\",   scale_colour_upper = scale_colour_brewer(palette = \"Set1\"),   colour_lower = \"island\",   scale_colour_lower = scale_colour_brewer(palette = \"Accent\"),   colour_diag = \"island\" ) }"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/hclust_matrix_rows.html","id":null,"dir":"Reference","previous_headings":"","what":"Hierarchical clustering of matrix rows — hclust_matrix_rows","title":"Hierarchical clustering of matrix rows — hclust_matrix_rows","text":"Performs hierarchical clustering rows matrix. Code inspired ComplexHeatmap package.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/hclust_matrix_rows.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Hierarchical clustering of matrix rows — hclust_matrix_rows","text":"","code":"hclust_matrix_rows(x)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/hclust_matrix_rows.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Hierarchical clustering of matrix rows — hclust_matrix_rows","text":"x Matrix.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/hclust_matrix_rows.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Hierarchical clustering of matrix rows — hclust_matrix_rows","text":"dendrogram.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/import_dataset_csv.html","id":null,"dir":"Reference","previous_headings":"","what":"Import a dataset from a csv file — import_dataset_csv","title":"Import a dataset from a csv file — import_dataset_csv","text":"Reads csv file returns matrix rows corresponds features (e.g. markers, genes, phenotypes...) columns correspond samples/observations.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/import_dataset_csv.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Import a dataset from a csv file — import_dataset_csv","text":"","code":"import_dataset_csv(file, col_id, features_as_rows = TRUE, ...)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/import_dataset_csv.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Import a dataset from a csv file — import_dataset_csv","text":"file Character, path dataset csv file. col_id Character, name column file contains ID rows (.e. feature IDs features_as_rows TRUE, sample IDs features_as_rows FALSE). features_as_rows Logical, rows file correspond features? Default value TRUE, .e. file contains features rows samples columns. ... arguments passed readr::read_csv().","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/import_dataset_csv.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Import a dataset from a csv file — import_dataset_csv","text":"matrix samples columns features rows. Feature IDs used row names sample IDs column names.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/import_dataset_csv.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Import a dataset from a csv file — import_dataset_csv","text":"","code":"if (FALSE) { data_geno <- import_dataset_csv(   \"genotype_dataset.csv\",   col_id = \"Marker\",   features_as_rows = TRUE ) data_pheno <- import_dataset_csv(   \"phenotype_dataset.csv\",   col_id = \"Sample\",   features_as_rows = FALSE ) }"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/import_dataset_csv_factory.html","id":null,"dir":"Reference","previous_headings":"","what":"Target factory for csv datasets import — import_dataset_csv_factory","title":"Target factory for csv datasets import — import_dataset_csv_factory","text":"Creates list targets track file import dataset csv file.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/import_dataset_csv_factory.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Target factory for csv datasets import — import_dataset_csv_factory","text":"","code":"import_dataset_csv_factory(   files,   col_ids,   features_as_rowss,   target_name_suffixes )"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/import_dataset_csv_factory.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Target factory for csv datasets import — import_dataset_csv_factory","text":"files Character vector, vector paths dataset csv files. col_ids Character vector, name column file contains ID rows (.e. feature IDs value features_as_rowss TRUE corresponding dataset, sample IDs value features_as_rowss FALSE). features_as_rowss Logical vector, rows file correspond features? target_name_suffixes Character vector, suffix add name targets created target factory dataset.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/import_dataset_csv_factory.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Target factory for csv datasets import — import_dataset_csv_factory","text":"list target objects. example, two files import target_name_suffixes = c(\"geno\", \"transcripto\"), factory returns following targets: dataset_file_geno dataset_file_transcripto: targets tracking genomics dataset file transcriptomics dataset file, respectively. data_geno data_transcripto: targets import genomics transcriptomics dataset, respectively.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/import_dataset_csv_factory.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Target factory for csv datasets import — import_dataset_csv_factory","text":"","code":"if (FALSE) { ## in the _targets.R library(moiraine)  list(   import_dataset_csv_factory(     c(       \"data/genotype_data.csv\",       \"data/rnaseq_data.csv\"     ),     col_ids = c(\"Marker\", \"Sample\"),     features_as_rows = c(TRUE, FALSE),     target_name_suffixes = c(\"geno\", \"transcripto\")   ) ) }"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/import_fmetadata_csv.html","id":null,"dir":"Reference","previous_headings":"","what":"Import feature metadata from a csv file — import_fmetadata_csv","title":"Import feature metadata from a csv file — import_fmetadata_csv","text":"Reads csv file returns dataframe rows correspond features (e.g. markers, genes, phenotypes...) columns correspond information features. Non-ASCII characters replaced ASCII equivalents (using stringi textclean packages).","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/import_fmetadata_csv.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Import feature metadata from a csv file — import_fmetadata_csv","text":"","code":"import_fmetadata_csv(file, col_id, ...)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/import_fmetadata_csv.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Import feature metadata from a csv file — import_fmetadata_csv","text":"file Character, path dataset csv file. col_id Character, name column file contains feature IDs. ... arguments passed readr::read_csv().","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/import_fmetadata_csv.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Import feature metadata from a csv file — import_fmetadata_csv","text":"data-frame features rows features information columns. Feature IDs used row names.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/import_fmetadata_csv.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Import feature metadata from a csv file — import_fmetadata_csv","text":"","code":"if (FALSE) { geno_info_features <- import_fmetadata_csv(   \"genotype_features_info.csv\",   col_id = \"Marker\" ) }"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/import_fmetadata_csv_factory.html","id":null,"dir":"Reference","previous_headings":"","what":"Target factory for csv features metadata import — import_fmetadata_csv_factory","title":"Target factory for csv features metadata import — import_fmetadata_csv_factory","text":"Creates list targets track file import features metadata csv file.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/import_fmetadata_csv_factory.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Target factory for csv features metadata import — import_fmetadata_csv_factory","text":"","code":"import_fmetadata_csv_factory(files, col_ids, target_name_suffixes)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/import_fmetadata_csv_factory.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Target factory for csv features metadata import — import_fmetadata_csv_factory","text":"files Character vector, vector paths features metadata csv files. col_ids Character vector, name column file contains features ID. target_name_suffixes Character vector, suffix add name targets created target factory dataset.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/import_fmetadata_csv_factory.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Target factory for csv features metadata import — import_fmetadata_csv_factory","text":"list target objects. example, two files import target_name_suffixes = c(\"geno\", \"transcripto\"), factory returns following targets: fmetadata_file_geno fmetadata_file_transcripto: targets tracking genomics transcriptomics features metadata files, respectively. fmetadata_geno fmetadata_transcripto: targets import genomics transcriptomics features metadata dataset.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/import_fmetadata_csv_factory.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Target factory for csv features metadata import — import_fmetadata_csv_factory","text":"","code":"if (FALSE) { ## in the _targets.R library(moiraine)  list(   import_fmetadata_csv_factory(     c(       \"data/genotype_fmetadata.csv\",       \"data/rnaseq_fmetadata.csv\"     ),     col_ids = c(\"Marker\", \"Info\"),     target_name_suffixes = c(\"geno\", \"transcripto\")   ) ) }"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/import_fmetadata_gff.html","id":null,"dir":"Reference","previous_headings":"","what":"Import features metadata from a GFF/GTF file — import_fmetadata_gff","title":"Import features metadata from a GFF/GTF file — import_fmetadata_gff","text":"Reads GFF GTF annotation file returns dataframe rows correspond features (e.g. genes transcripts) columns correspond information features. Non-ASCII characters replaced ASCII equivalents (using stringi textclean packages).","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/import_fmetadata_gff.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Import features metadata from a GFF/GTF file — import_fmetadata_gff","text":"","code":"import_fmetadata_gff(file, feature_type, add_fields = NULL)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/import_fmetadata_gff.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Import features metadata from a GFF/GTF file — import_fmetadata_gff","text":"file Character, path dataset GFF GTF file. feature_type Character, type feature extract annotation file. Currently supports 'genes' 'transcripts'. add_fields Character vector, fields GFF/GTF file extract imported default (use run function realised fields extracted function).","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/import_fmetadata_gff.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Import features metadata from a GFF/GTF file — import_fmetadata_gff","text":"data-frame features rows features information columns. Feature IDs used row names.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/import_fmetadata_gff.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Import features metadata from a GFF/GTF file — import_fmetadata_gff","text":"","code":"if (FALSE) { import_fmetadata_gff(   \"bos_taurus_gene_model.gff3\",   \"genes\",   add_fields = c(\"name\", \"description\") ) }"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/import_fmetadata_gff_factory.html","id":null,"dir":"Reference","previous_headings":"","what":"Target factory for GFF/GTF features metadata import — import_fmetadata_gff_factory","title":"Target factory for GFF/GTF features metadata import — import_fmetadata_gff_factory","text":"Creates list targets track file import features metadata GFF/GTF file.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/import_fmetadata_gff_factory.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Target factory for GFF/GTF features metadata import — import_fmetadata_gff_factory","text":"","code":"import_fmetadata_gff_factory(   files,   feature_types,   add_fieldss,   target_name_suffixes )"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/import_fmetadata_gff_factory.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Target factory for GFF/GTF features metadata import — import_fmetadata_gff_factory","text":"files Character vector, vector paths samples metadata GFF GTF files. feature_types Character vector, type features extract annotation file. Currently supports 'genes' 'transcripts'. add_fieldss List, element character vector  field names GFF/GTF file extract imported default. character vector provided, used files read . target_name_suffixes Character vector, suffix add name targets created target factory dataset.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/import_fmetadata_gff_factory.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Target factory for GFF/GTF features metadata import — import_fmetadata_gff_factory","text":"list target objects. example, two files import target_name_suffixes = c(\"geno\", \"transcripto\"), factory returns following targets: fmetadata_file_geno fmetadata_file_transcripto: targets tracking genomics transcriptomics annotation files, respectively. fmetadata_geno fmetadata_transcripto: targets import genomics transcriptomics features metadata datasets, respectively.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/import_fmetadata_gff_factory.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Target factory for GFF/GTF features metadata import — import_fmetadata_gff_factory","text":"","code":"if (FALSE) { ## in the _targets.R library(moiraine)  list(   import_fmetadata_gff_factory(     c(       \"data/annotation.gff\",       \"data/annotationv2.gtf\"     ),     feature_types = c(\"genes\", \"transcripts\"),     add_fieldss = list(       c(\"gene_name\", \"gene_custom_ID\"),       c(\"transcript_name\")     ),     target_name_suffixes = c(\"geno\", \"transcripto\")   ) ) }"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/import_smetadata_csv.html","id":null,"dir":"Reference","previous_headings":"","what":"Import samples metadata from a csv file — import_smetadata_csv","title":"Import samples metadata from a csv file — import_smetadata_csv","text":"Reads csv file returns dataframe rows correspond features (e.g. markers, genes, phenotypes...) columns correspond information features.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/import_smetadata_csv.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Import samples metadata from a csv file — import_smetadata_csv","text":"","code":"import_smetadata_csv(file, col_id, ...)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/import_smetadata_csv.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Import samples metadata from a csv file — import_smetadata_csv","text":"file Character, path dataset csv file. col_id Character, name column file contains ID rows (.e. sample IDs). ... arguments passed readr::read_csv().","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/import_smetadata_csv.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Import samples metadata from a csv file — import_smetadata_csv","text":"data-frame samples rows samples properties columns. Sample IDs used rownames.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/import_smetadata_csv.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Import samples metadata from a csv file — import_smetadata_csv","text":"","code":"if (FALSE) { samples_information <- import_smetadata_csv(   \"samples_information.csv\",   col_id = \"Sample\" ) }"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/import_smetadata_csv_factory.html","id":null,"dir":"Reference","previous_headings":"","what":"Target factory for csv samples metadata import — import_smetadata_csv_factory","title":"Target factory for csv samples metadata import — import_smetadata_csv_factory","text":"Creates list targets track file import samples metadata csv file.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/import_smetadata_csv_factory.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Target factory for csv samples metadata import — import_smetadata_csv_factory","text":"","code":"import_smetadata_csv_factory(files, col_ids, target_name_suffixes)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/import_smetadata_csv_factory.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Target factory for csv samples metadata import — import_smetadata_csv_factory","text":"files Character vector, vector paths samples metadata csv files. col_ids Character vector, name column file contains ID rows (.e. sample IDs). target_name_suffixes Character vector, suffix add name targets created target factory dataset.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/import_smetadata_csv_factory.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Target factory for csv samples metadata import — import_smetadata_csv_factory","text":"list target objects. example, two files import target_name_suffixes = c(\"geno\", \"transcripto\"), factory returns following targets: smetadata_file_geno smetadata_file_transcripto: targets tracking genomics transcriptomics samples metadata files, respectively. smetadata_geno smetadata_transcripto: targets import genomics transcriptomics samples metadata datasets, respectively.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/import_smetadata_csv_factory.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Target factory for csv samples metadata import — import_smetadata_csv_factory","text":"","code":"if (FALSE) { ## in the _targets.R library(moiraine)  list(   import_smetadata_csv_factory(     c(       \"data/genotype_smetadata.csv\",       \"data/rnaseq_smetadata.csv\"     ),     col_ids = c(\"Sample\", \"SampleIDs\"),     target_name_suffixes = c(\"geno\", \"transcripto\")   ) ) }"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/is_equal_or_null.html","id":null,"dir":"Reference","previous_headings":"","what":"Check null or equality — is_equal_or_null","title":"Check null or equality — is_equal_or_null","text":"Tests whether object NULL equal value.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/is_equal_or_null.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check null or equality — is_equal_or_null","text":"","code":"is_equal_or_null(x, val)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/is_equal_or_null.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check null or equality — is_equal_or_null","text":"x object test. val Value compare ","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/is_equal_or_null.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check null or equality — is_equal_or_null","text":"TRUE FALSE.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/join_features_metadata.html","id":null,"dir":"Reference","previous_headings":"","what":"Join feature metadata to table — join_features_metadata","title":"Join feature metadata to table — join_features_metadata","text":"Adds features metadata information table containing feature IDs.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/join_features_metadata.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Join feature metadata to table — join_features_metadata","text":"","code":"join_features_metadata(df, mo_data)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/join_features_metadata.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Join feature metadata to table — join_features_metadata","text":"df Data-frame tibble column feature_id containing feature IDs. mo_data MultiDataSet::MultiDataSet object.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/join_features_metadata.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Join feature metadata to table — join_features_metadata","text":"df table additional columns containing information features features metadata table.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/join_samples_metadata.html","id":null,"dir":"Reference","previous_headings":"","what":"Join samples metadata to table — join_samples_metadata","title":"Join samples metadata to table — join_samples_metadata","text":"Adds samples metadata information table containing sample IDs.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/join_samples_metadata.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Join samples metadata to table — join_samples_metadata","text":"","code":"join_samples_metadata(df, mo_data, datasets = NULL)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/join_samples_metadata.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Join samples metadata to table — join_samples_metadata","text":"df Data-frame tibble column id containing sample IDs. mo_data MultiDataSet::MultiDataSet object. datasets Character vector, name(s) datasets samples metadata extracted. NULL (default value), information datasets used.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/join_samples_metadata.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Join samples metadata to table — join_samples_metadata","text":"df table additional columns containing information samples samples metadata table.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/make_feature_sets_from_df.html","id":null,"dir":"Reference","previous_headings":"","what":"Makes list of feature sets from data-frame — make_feature_sets_from_df","title":"Makes list of feature sets from data-frame — make_feature_sets_from_df","text":"Creates list feature sets annotation data-frame.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/make_feature_sets_from_df.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Makes list of feature sets from data-frame — make_feature_sets_from_df","text":"","code":"make_feature_sets_from_df(annotation_df, col_id, col_set)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/make_feature_sets_from_df.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Makes list of feature sets from data-frame — make_feature_sets_from_df","text":"annotation_df data-frame feature annotation long format, least column feature ID column giving set feature belongs. feature belongs one set, row sets. col_id Character, name column annotation_df data-frame contains features ID. col_set Character, name column annotation_df data-frame contains sets ID.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/make_feature_sets_from_df.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Makes list of feature sets from data-frame — make_feature_sets_from_df","text":"named list, element corresponds set, contains vector features ID features belonging set.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/make_feature_sets_from_fm.html","id":null,"dir":"Reference","previous_headings":"","what":"Makes list of feature sets from features metadata — make_feature_sets_from_fm","title":"Makes list of feature sets from features metadata — make_feature_sets_from_fm","text":"Creates list feature sets features metadata.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/make_feature_sets_from_fm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Makes list of feature sets from features metadata — make_feature_sets_from_fm","text":"","code":"make_feature_sets_from_fm(mo_data, col_names, combine_omics_sets = FALSE)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/make_feature_sets_from_fm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Makes list of feature sets from features metadata — make_feature_sets_from_fm","text":"mo_data MultiDataSet-class object. col_names Named list character, one element per dataset feature sets generated. name element correspond name dataset, value column name feature metadata corresponding dataset use set ID. combine_omics_sets Logical, can sets contain features different omics datasets? FALSE (default), feature sets created omics separately. two sets different omics ID, made unique.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/make_feature_sets_from_fm.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Makes list of feature sets from features metadata — make_feature_sets_from_fm","text":"named list, element corresponds set, contains vector features ID features belonging set.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/mofa_get_weights.html","id":null,"dir":"Reference","previous_headings":"","what":"Get feature weights from MOFA object — mofa_get_weights","title":"Get feature weights from MOFA object — mofa_get_weights","text":"Extracts feature weights trained MOFA MEFISTO model (MOFA2 package).","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/mofa_get_weights.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get feature weights from MOFA object — mofa_get_weights","text":"","code":"mofa_get_weights(   object,   views = \"all\",   factors = \"all\",   abs = FALSE,   scale = \"none\",   as.data.frame = TRUE )"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/mofa_get_weights.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get feature weights from MOFA object — mofa_get_weights","text":"object trained MOFA object. views Character integer vector, name index views (.e. datasets) feature weights extracted. Default value \"\", .e. datasets considered. factors Character integer vector, name index factors feature weights extracted. Default value \"\", .e. factors considered. abs Logical, absolute value weights returned? Default value FALSE. scale Character, type scaling performed feature weights. Possible values 'none', 'by_view', 'by_factor' 'overall' (see Details). Default value 'none'. .data.frame Logical, whether function return long data-frame instead list matrices. Default value TRUE.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/mofa_get_weights.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get feature weights from MOFA object — mofa_get_weights","text":"default, returns tibble columns view, feature, factor, value. Alternatively, .data.frame = FALSE, returns list matrices, one per view, features rows factors columns.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/mofa_get_weights.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Get feature weights from MOFA object — mofa_get_weights","text":"Scaling options: scale = 'none': scaling performed; scale = 'by_view': weights divided maximum absolute weight corresponding view/dataset; scale = 'by_factor': weights divided maximum absolute weight corresponding factor; scale = 'overall': weights divided maximum absolute weight across views/datasets factors considered.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/mofa_plot_cor_covariates.html","id":null,"dir":"Reference","previous_headings":"","what":"Plots the correlation between factors and covariates for MOFA — mofa_plot_cor_covariates","title":"Plots the correlation between factors and covariates for MOFA — mofa_plot_cor_covariates","text":"Plots Pearson correlation MOFA latent factors covariates (obtained samples metadata). function provides ggplot2 version plot created correlate_factors_with_covariates (plot parameter set 'r').","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/mofa_plot_cor_covariates.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plots the correlation between factors and covariates for MOFA — mofa_plot_cor_covariates","text":"","code":"mofa_plot_cor_covariates(   mofa_output,   covariates = NULL,   show_cor = TRUE,   min_show_cor = 0.2,   round_cor = 2,   factor_as_col = TRUE )"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/mofa_plot_cor_covariates.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plots the correlation between factors and covariates for MOFA — mofa_plot_cor_covariates","text":"mofa_output output run_mofa. covariates Character vector, covariates use plot. NULL, covariates retrieved via colnames(MOFA2::samples_metadata(mofa_output)) (except group, id sample) used. Default value NULL. show_cor Logical, correlation values added plot? Default value TRUE. min_show_cor Numeric, minimum value correlation coefficients values added plot (.e. circle appear values text). Ignored show_cor FALSE. Default value 0.2. round_cor Integer, many decimal places show correlation coefficients. Ignored show_cor FALSE. Default value 2. factor_as_col Logical, factors represented columns? FALSE, represented rows instead. Default value TRUE.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/mofa_plot_cor_covariates.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plots the correlation between factors and covariates for MOFA — mofa_plot_cor_covariates","text":"ggplot.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/n_features.html","id":null,"dir":"Reference","previous_headings":"","what":"Number of features in each dataset of MultiDataSet object — n_features","title":"Number of features in each dataset of MultiDataSet object — n_features","text":"Gives number features dataset MultiDataSet object.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/n_features.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Number of features in each dataset of MultiDataSet object — n_features","text":"","code":"n_features(mo_data)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/n_features.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Number of features in each dataset of MultiDataSet object — n_features","text":"mo_data MultiDataSet::MultiDataSet object.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/n_features.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Number of features in each dataset of MultiDataSet object — n_features","text":"named integer vector, element number features corresponding dataset.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/n_samples.html","id":null,"dir":"Reference","previous_headings":"","what":"Number of samples in each dataset of MultiDataSet object — n_samples","title":"Number of samples in each dataset of MultiDataSet object — n_samples","text":"Gives number samples dataset MultiDataSet object.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/n_samples.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Number of samples in each dataset of MultiDataSet object — n_samples","text":"","code":"n_samples(mo_data)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/n_samples.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Number of samples in each dataset of MultiDataSet object — n_samples","text":"mo_data MultiDataSet::MultiDataSet object.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/n_samples.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Number of samples in each dataset of MultiDataSet object — n_samples","text":"named integer vector, element number sample corresponding dataset.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/options_list_as_tibble.html","id":null,"dir":"Reference","previous_headings":"","what":"Returns options list as a tibble — options_list_as_tibble","title":"Returns options list as a tibble — options_list_as_tibble","text":"Transforms list options (parameters) tibble name options (parameters) one column, value second column. Vector values collapsed span one column.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/options_list_as_tibble.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Returns options list as a tibble — options_list_as_tibble","text":"","code":"options_list_as_tibble(options_list)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/options_list_as_tibble.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Returns options list as a tibble — options_list_as_tibble","text":"options_list named list, element corresponds one option parameter name element corresponds name option/parameter.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/options_list_as_tibble.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Returns options list as a tibble — options_list_as_tibble","text":"tibble, Parameter column giving list options parameters, Value column giving values corresponding option parameter.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/pca_complete_data_factory.html","id":null,"dir":"Reference","previous_headings":"","what":"Target factory for PCA run and missing values imputation on each omics\ndataset — pca_complete_data_factory","title":"Target factory for PCA run and missing values imputation on each omics\ndataset — pca_complete_data_factory","text":"Creates list targets perform PCA run omics dataset MultiDataSet object using dynamic branching, imputes missing values datasets using results PCA runs.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/pca_complete_data_factory.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Target factory for PCA run and missing values imputation on each omics\ndataset — pca_complete_data_factory","text":"","code":"pca_complete_data_factory(   mo_data_target,   dataset_names = NULL,   target_name_prefix = \"\",   complete_data_name = NULL,   ... )"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/pca_complete_data_factory.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Target factory for PCA run and missing values imputation on each omics\ndataset — pca_complete_data_factory","text":"mo_data_target Symbol, name target containing MultiDataSet object. dataset_names Character vector, names datasets PCA run. NULL, PCA run datasets. Default value NULL. target_name_prefix Character, prefix add name targets created factory. Default value \"\". complete_data_name Character, name target containing MultiDataSet missing data imputed created. NULL, selected automatically. Default value NULL. ... arguments passed run_pca_matrix() function.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/pca_complete_data_factory.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Target factory for PCA run and missing values imputation on each omics\ndataset — pca_complete_data_factory","text":"List targets. target_name_prefix = \"\" complete_data_name = NULL, following targets created: dataset_names_pca: target containing character vector gives names datasets PCA run. dataset_mats_pca: dynamic branching target applies get_dataset_matrix() function dataset specified dataset_names. results saved list. Note using dynamic branching, names list meaningful. Rather, use sapply(pca_pca_runs_listruns_list, attr, \"dataset_name\") assess element list corresponds omics dataset. pca_runs_list: dynamic branching target applies run_pca_matrix() function matrix dataset_mats_pca. results saved list. Note using dynamic branching, names list meaningful. Rather, use sapply(pca_runs_list, attr, \"dataset_name\") assess element list corresponds omics dataset. complete_set: target returns MultiDataSet missing values imputed.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/pca_complete_data_factory.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Target factory for PCA run and missing values imputation on each omics\ndataset — pca_complete_data_factory","text":"","code":"if (FALSE) { ## in the _targets.R library(moiraine) list(   # ... code for importing datasets etc    ## mo_set is the target containing the MultiDataSet object   ## Example 1: running a PCA on all datasets   run_pca_factory(mo_set),    ## Example 2: running a PCA on 'rnaseq' and 'metabolome' datasets   run_pca_factory(     mo_set,     c(\"rnaseq\", \"metabolome\"),     complete_data_name = \"mo_data_complete\"   ) ) }"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/perf_splsda.html","id":null,"dir":"Reference","previous_headings":"","what":"Assess optimal number of components for sPLS-DA on omics dataset from MultiDataSet object — perf_splsda","title":"Assess optimal number of components for sPLS-DA on omics dataset from MultiDataSet object — perf_splsda","text":"Performs cross-validation PLS-DA run (implemented mixOmics package) omics dataset MultiDataSet object. allows estimate optimal number latent components construct. intended feature preselection omics dataset (see examples ).","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/perf_splsda.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Assess optimal number of components for sPLS-DA on omics dataset from MultiDataSet object — perf_splsda","text":"","code":"perf_splsda(   splsda_input,   ncomp_max = 5,   validation = \"Mfold\",   folds = 5,   nrepeat = 50,   measure = \"BER\",   distance = \"centroids.dist\",   cpus = 1,   progressBar = TRUE )"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/perf_splsda.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Assess optimal number of components for sPLS-DA on omics dataset from MultiDataSet object — perf_splsda","text":"splsda_input Input sPLS-DA functions mixOmics, created get_input_splsda(). ncomp_max Integer, maximum number latent components test estimating number latent components use. Default value 5. validation Character, cross-validation method use, can one \"Mfold\" \"loo\" (see mixOmics::perf()). Default value \"Mfold\". folds Integer, number folds use M-fold cross-validation (see mixOmics::perf()). Default value 5. nrepeat Integer, number times cross-validation repeated (see mixOmics::perf()). measure Performance measure used select optimal value ncomp, can one \"BER\" \"overall\" (see mixOmics::perf()). Default value \"BER\". distance Distance metric used select optimal value ncomp, can one \"max.dist\", \"centroids.dist\" \"mahalanobis.dist\" (see mixOmics::perf()). Default value \"centroids.dist\". cpus Integer, number cpus use. progressBar Logical, whether display progress bar optimisation ncomp. Default value TRUE.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/perf_splsda.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Assess optimal number of components for sPLS-DA on omics dataset from MultiDataSet object — perf_splsda","text":"list per output mixOmics::perf() function, following additional elements: dataset_name: name dataset analysed; group: column name samples information data-frame used samples group; optim_ncomp: optimal number latent components per measure distance specified; optim_measure: measure used select optimal number latent components; optim_distance: distance metric used select optimal number latent components. addition, name dataset analysed column name samples information data-frame used samples group stored attributes dataset_name group, respectively.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/perf_splsda.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Assess optimal number of components for sPLS-DA on omics dataset from MultiDataSet object — perf_splsda","text":"function uses plsda  perf function mixOmics package.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_correlation_matrix.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot correlation matrix — plot_correlation_matrix","title":"Plot correlation matrix — plot_correlation_matrix","text":"Plots correlation matrix using corrplot package.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_correlation_matrix.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot correlation matrix — plot_correlation_matrix","text":"","code":"plot_correlation_matrix(cormat, ...)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_correlation_matrix.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot correlation matrix — plot_correlation_matrix","text":"cormat correlation matrix. ... arguments passed corrplot::corrplot() function.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_correlation_matrix.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot correlation matrix — plot_correlation_matrix","text":"correlation plot","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_correlation_matrix_full.html","id":null,"dir":"Reference","previous_headings":"","what":"Plots a full correlation matrix (corrplot-style) — plot_correlation_matrix_full","title":"Plots a full correlation matrix (corrplot-style) — plot_correlation_matrix_full","text":"Generates plot correlation matrix style corrplot package.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_correlation_matrix_full.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plots a full correlation matrix (corrplot-style) — plot_correlation_matrix_full","text":"","code":"plot_correlation_matrix_full(   mat,   rows_title = NULL,   cols_title = NULL,   title = NULL,   show_cor = TRUE,   min_show_cor = 0.2,   round_cor = 2 )"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_correlation_matrix_full.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plots a full correlation matrix (corrplot-style) — plot_correlation_matrix_full","text":"mat Correlation matrix plot. rows_title Character, title rows. Default value NULL. cols_title Character, title cols. Default value NULL. title Character, title plot. Default value NULL. show_cor Logical, correlation values added plot? Default value TRUE. min_show_cor Numeric, minimum value correlation coefficients values added plot (.e. circle appear values text). Ignored show_cor FALSE. Default value 0.2. round_cor Integer, many decimal places show correlation coefficients. Ignored show_cor FALSE. Default value 2.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_correlation_matrix_full.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plots a full correlation matrix (corrplot-style) — plot_correlation_matrix_full","text":"ggplot.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_data_covariate.html","id":null,"dir":"Reference","previous_headings":"","what":"Plots omics data vs sample covariate — plot_data_covariate","title":"Plots omics data vs sample covariate — plot_data_covariate","text":"given set features, plots value sample covariate samples metadata. Depending whether covariate continuous discrete, generate either scatterplot violin plot.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_data_covariate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plots omics data vs sample covariate — plot_data_covariate","text":"","code":"plot_data_covariate(   mo_data,   covariate,   features,   samples = NULL,   only_common_samples = FALSE,   colour_by = NULL,   shape_by = NULL,   point_alpha = 1,   add_se = TRUE,   add_boxplot = TRUE,   ncol = NULL,   label_cols = NULL,   truncate = NULL )"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_data_covariate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plots omics data vs sample covariate — plot_data_covariate","text":"mo_data MultiDataSet::MultiDataSet object. covariate Character, name column one samples metadata tables mo_data use x-axis plot. features Character vector, ID features show plot. samples Character vector, ID samples include plot. NULL (default), samples corresponding dataset used. only_common_samples Logical, whether samples present datasets plotted. Default value FALSE. colour_by Character, name column one samples metadata tables mo_data use colour observations plot. Default value NULL. shape_by Character, name column one samples metadata tables mo_data use shape observations plot. point_alpha Numeric 0 1, opacity points plot (1 = fully opaque, 0 = fully transparent). Default value 1. add_se Logical, confidence interval drawn around smoothing curves numerical covariates? Default value TRUE. add_boxplot Logical, boxplot drawn top points categorical covariates? Default value TRUE. ncol Integer, number columns faceted plot. Default value NULL. label_cols Character named list character, giving dataset name column corresponding features metadata use label. one value, used datasets. list, names must correspond names datasets mo_data. dataset missing list value provided, feature IDs used labels. Alternatively, use feature_id get feature IDs labels. truncate Integer, width labels truncated (avoid issues long labels plots). NULL (default value), truncation performed.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_data_covariate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plots omics data vs sample covariate — plot_data_covariate","text":"ggplot.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_data_covariate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plots omics data vs sample covariate — plot_data_covariate","text":"","code":"if (FALSE) { ## Selecting at random 3 features from each dataset random_features <- get_features(mo_set) |>   map(sample, size = 3, replace = FALSE) |>   unlist() |>   unname()  ## Plotting features value against a discrete samples covariate plot_data_covariate(   mo_set,   \"feedlot\",   random_features,   only_common_samples = TRUE,   colour_by = \"status\",   shape_by = \"geno_comp_cluster\" )  ## Plotting features value against a continuous samples covariate plot_data_covariate(   mo_set,   \"day_on_feed\",   random_features,   only_common_samples = TRUE,   colour_by = \"status\",   shape_by = \"geno_comp_cluster\" ) }"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_data_heatmap.html","id":null,"dir":"Reference","previous_headings":"","what":"Plots omics data as heatmap — plot_data_heatmap","title":"Plots omics data as heatmap — plot_data_heatmap","text":"given set features, plots value across samples heatmap.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_data_heatmap.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plots omics data as heatmap — plot_data_heatmap","text":"","code":"plot_data_heatmap(   mo_data,   features,   center = FALSE,   scale = FALSE,   samples = NULL,   only_common_samples = FALSE,   samples_info = NULL,   features_info = NULL,   colours_list = NULL,   label_cols = NULL,   truncate = NULL,   legend_title_size = 10,   legend_text_size = 10,   legend_ncol = 1,   ... )"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_data_heatmap.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plots omics data as heatmap — plot_data_heatmap","text":"mo_data MultiDataSet::MultiDataSet object. features Character vector, ID features show plot. center Logical, whether data centered (feature-wise). Default value FALSE. scale Logical, whether data scaled (feature-wise). Default value FALSE. samples Character vector, ID samples include plot. NULL (default), samples used. only_common_samples Logical, whether samples present datasets plotted. Default value FALSE. samples_info Character vector, column names samples metadata tables datasets represented plot samples annotation. features_info Named list character vectors, element corresponds dataset, gives column names features metadata dataset represented plot features annotation. names list must correspond dataset names mo_data object. colours_list Named list, element gives colour palette use samples features annotation. Names must match values samples_info vector elements features_info list. continuous palettes, must use circlize::colorRamp2() function (see ComplexHeatmap reference book). label_cols Character named list character, giving dataset name column corresponding features metadata use label. one value, used datasets. list, names must correspond names datasets mo_data. dataset missing list value provided, feature IDs used labels. Alternatively, use feature_id get feature IDs labels. truncate Integer, width labels truncated (avoid issues long labels plots). NULL (default value), truncation performed. legend_title_size Integer, size points legend title. legend_text_size Integer, size points legend elements text. legend_ncol Integer, number columns legend. Default value 1. ... Additional arguments passed ComplexHeatmap::Heatmap() function.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_data_heatmap.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plots omics data as heatmap — plot_data_heatmap","text":"ComplexHeatmap::Heatmap object.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_data_heatmap.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plots omics data as heatmap — plot_data_heatmap","text":"","code":"if (FALSE) { ## Selecting at random 3 features from each dataset random_features <- get_features(mo_set) |>   map(sample, size = 3, replace = FALSE) |>   unlist() |>   unname()  plot_data_heatmap(   mo_set,   random_features,   center = TRUE,   scale = TRUE,   show_column_names = FALSE,   only_common_samples = TRUE,   samples_info = c(\"status\", \"day_on_feed\"),   features_info = c(\"chromosome\"),   colours_list = list(     \"status\" = c(\"Control\" = \"gold\", \"BRD\" = \"navyblue\"),     \"day_on_feed\" = colorRamp2(c(5, 65), c(\"white\", \"pink3\"))   ),   label_cols = list(      \"rnaseq\" = \"Name\",     \"metabolome\" = \"name\"   )  ) }"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_density_data.html","id":null,"dir":"Reference","previous_headings":"","what":"Per-dataset density plot for MultiDataSet object — plot_density_data","title":"Per-dataset density plot for MultiDataSet object — plot_density_data","text":"Displays density plot values dataset MultiDataSet object.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_density_data.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Per-dataset density plot for MultiDataSet object — plot_density_data","text":"","code":"plot_density_data(   mo_data,   datasets = names(mo_data),   combined = TRUE,   scales = \"fixed\" )"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_density_data.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Per-dataset density plot for MultiDataSet object — plot_density_data","text":"mo_data MultiDataSet::MultiDataSet object. datasets Character vector, names datasets include plot. default, datasets included. combined Logical, different datasets represented plot? FALSE (default value), dataset represented subplot. Default value TRUE. scales Character, axes plotted combined = FALSE. Can either 'fixed', .e. limits applied axes subplot; 'free', .e. axis limits adapted subplot. Ignored combined = TRUE. Default value 'fixed.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_density_data.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Per-dataset density plot for MultiDataSet object — plot_density_data","text":"ggplot.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_feature_preselection_cov.html","id":null,"dir":"Reference","previous_headings":"","what":"Diagnostics plots for COV-based feature preselection — plot_feature_preselection_cov","title":"Diagnostics plots for COV-based feature preselection — plot_feature_preselection_cov","text":"Displays COV distribution across features original (.e. non-filtered) datasets, vertical red line showing cut-used preselection function.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_feature_preselection_cov.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Diagnostics plots for COV-based feature preselection — plot_feature_preselection_cov","text":"","code":"plot_feature_preselection_cov(cov_list)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_feature_preselection_cov.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Diagnostics plots for COV-based feature preselection — plot_feature_preselection_cov","text":"cov_list list result COV calculation dataset filtered, returned select_features_cov function.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_feature_preselection_cov.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Diagnostics plots for COV-based feature preselection — plot_feature_preselection_cov","text":"ggplot.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_feature_preselection_mad.html","id":null,"dir":"Reference","previous_headings":"","what":"Diagnostics plots for MAD-based feature preselection — plot_feature_preselection_mad","title":"Diagnostics plots for MAD-based feature preselection — plot_feature_preselection_mad","text":"Displays MAD distribution across features original (.e. non-filtered) datasets, vertical red line showing cut-used preselection function.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_feature_preselection_mad.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Diagnostics plots for MAD-based feature preselection — plot_feature_preselection_mad","text":"","code":"plot_feature_preselection_mad(mad_list)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_feature_preselection_mad.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Diagnostics plots for MAD-based feature preselection — plot_feature_preselection_mad","text":"mad_list list result MAD calculation dataset filtered, returned select_features_mad function.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_feature_preselection_mad.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Diagnostics plots for MAD-based feature preselection — plot_feature_preselection_mad","text":"ggplot.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_feature_preselection_splsda.html","id":null,"dir":"Reference","previous_headings":"","what":"Diagnostics plots for sPLS-DA-based feature preselection — plot_feature_preselection_splsda","title":"Diagnostics plots for sPLS-DA-based feature preselection — plot_feature_preselection_splsda","text":"Displays PLS-DA classification performance across different number latent components prefiltered dataset. classification error rates computed different measures (column facets) different distance metrics (colours). vertical grey bar represents dataset number latent components selected feature preselection step. addition, circle highlights measure distance metric used select number latent component.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_feature_preselection_splsda.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Diagnostics plots for sPLS-DA-based feature preselection — plot_feature_preselection_splsda","text":"","code":"plot_feature_preselection_splsda(   perf_splsda_res,   measure = NULL,   distance = NULL )"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_feature_preselection_splsda.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Diagnostics plots for sPLS-DA-based feature preselection — plot_feature_preselection_splsda","text":"perf_splsda_res list result perf_splsda dataset filtered. measure measure(s) displayed? Can one \"BER\" \"overall\". NULL, measures displayed. Default value NULL. distance measure(s) displayed? Can one \"max.dist\", \"centroids.dist\" \"mahalanobis.dist\". NULL, measures displayed. Default value NULL.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_feature_preselection_splsda.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Diagnostics plots for sPLS-DA-based feature preselection — plot_feature_preselection_splsda","text":"ggplot.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_features_weight_covariate.html","id":null,"dir":"Reference","previous_headings":"","what":"Plots features weight against covariate — plot_features_weight_covariate","title":"Plots features weight against covariate — plot_features_weight_covariate","text":"Plots features weight importance result integration method covariate features metadata.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_features_weight_covariate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plots features weight against covariate — plot_features_weight_covariate","text":"","code":"plot_features_weight_covariate(   method_output,   mo_data,   covariate,   features_metric = c(\"signed_importance\", \"weight\", \"importance\"),   remove_null_weight = FALSE,   latent_dimensions = NULL,   colour_by = NULL,   shape_by = NULL,   point_alpha = 0.5,   add_se = TRUE,   add_boxplot = TRUE,   scales = \"free_x\" )"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_features_weight_covariate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plots features weight against covariate — plot_features_weight_covariate","text":"method_output Integration method output generated via get_output() function. mo_data MultiDataSet object (used extract samples information). covariate Character named list character, giving dataset name column corresponding features metadata use x-axis plot. one value, used datasets. list, names must correspond names datasets mo_data. dataset present list, excluded plot. features_metric Character, features metric plotted y-axis. one 'signed_importance' (default value), 'weight' 'importance'. remove_null_weight Logical, features null weight/importance removed plot? Default value FALSE. latent_dimensions Character vector giving latent dimensions display. Default value NULL, .e. latent dimensions shown. colour_by Character named list character, giving dataset name column corresponding feature metadata use colour features plot. one value, used datasets. list, names must correspond names datasets covariate. Default value NULL. shape_by Character named list character, giving dataset name column corresponding feature metadata use shape features plot. one value, used datasets. list, names must correspond names datasets covariate. Default value NULL. point_alpha Numeric 0 1, opacity points plot (1 = fully opaque, 0 = fully transparent). Default value 0.5. add_se Logical, confidence interval drawn around smoothing curves numerical covariates? Default value TRUE. add_boxplot Logical, boxplot drawn top points categorical covariates? Default value TRUE. scales Character, value use scales argument ggplot2::facet_grid(). Default value 'free_x'.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_features_weight_covariate.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Plots features weight against covariate — plot_features_weight_covariate","text":"covariate numeric, function creates scatter plot, loess curve summarise trend covariate features weight. colour_by used, corresponding variable numeric, loess curve take account variable. instead colour_by variable character factor, loess curve fitted separately category. covariate numeric, function creates violin/boxplot. colour_by used, corresponding variable numeric, violins boxplots take account variable. instead colour_by variable character factor, separate violin boxplot drawn category.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_features_weight_distr.html","id":null,"dir":"Reference","previous_headings":"","what":"Plots features weight distribution — plot_features_weight_distr","title":"Plots features weight distribution — plot_features_weight_distr","text":"Plots features weight importance per dataset latent dimension.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_features_weight_distr.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plots features weight distribution — plot_features_weight_distr","text":"","code":"plot_features_weight_distr(   method_output,   latent_dimensions = NULL,   datasets = NULL,   features_metric = c(\"signed_importance\", \"weight\", \"importance\"),   top_n = 0,   mo_data = NULL,   label_cols = NULL,   truncate = NULL,   text_size = 2.5 )"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_features_weight_distr.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plots features weight distribution — plot_features_weight_distr","text":"method_output Integration method output generated via get_output() function. latent_dimensions Character vector giving latent dimensions display. Default value NULL, .e. latent dimensions shown. datasets Character vector giving datasets display. Default value NULL, .e. datasets shown. features_metric Character, attribute plotted: can 'signed_importance' (.e. importance value weight sign), 'importance' 'weight'. Default value 'signed_importance'. top_n Integer, number top features (terms importance) label shown. Default value 0. mo_data MultiDataSet object. used label_cols NULL. label_cols Character named list character, giving dataset name column corresponding features metadata use label. one value, used datasets. list, names must correspond names datasets mo_data. dataset missing list value provided, feature IDs used labels. Alternatively, use feature_id get feature IDs labels. truncate Integer, width labels truncated (avoid issues long labels plots). NULL (default value), truncation performed. text_size Numeric, size feature labels.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_features_weight_distr.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plots features weight distribution — plot_features_weight_distr","text":"patchwork plots.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_features_weight_pair.html","id":null,"dir":"Reference","previous_headings":"","what":"Plots features weight as a scatterplot — plot_features_weight_pair","title":"Plots features weight as a scatterplot — plot_features_weight_pair","text":"Plots features weight pair latent dimensions one two dimension reduction analysis scatterplot.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_features_weight_pair.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plots features weight as a scatterplot — plot_features_weight_pair","text":"","code":"plot_features_weight_pair(   method_output,   latent_dimensions,   datasets = NULL,   features_metric = c(\"signed_importance\", \"weight\", \"importance\"),   include_missing_features = TRUE,   top_n = 5,   metric = \"geometric\",   label_cols = NULL,   mo_data = NULL,   truncate = NULL,   ncol = NULL,   label_size = 3 )"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_features_weight_pair.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plots features weight as a scatterplot — plot_features_weight_pair","text":"method_output single Integration method output generated via get_output() function, list two integration method outputs. latent_dimensions Character vector length 2 (method_output single output object), named list length 2 (method_output list two output objects). first case, gives name two latent dimensions represented. second case, names list correspond names methods, values character giving corresponding method name latent dimension display. datasets Character vector, names datasets features weight plotted. Default value NULL, .e. relevant datasets shown. features_metric Character, features metric plotted y-axis. one 'signed_importance' (default value), 'weight' 'importance'. include_missing_features Logical, whether show features input one method , comparing results two different integration methods. Default value TRUE. top_n Integer, number top features (according consensus importance metric) highlight plot. Default value 5. metric Character, one metrics use compute consensus score. Can one 'min', 'max', 'average', 'product', 'l2' (L2-norm), 'geometric' (geometric mean) 'harmonic' (harmonic mean). Default value 'geometric'. Names must match output_list. label_cols Character named list character, giving dataset name column corresponding features metadata use label. one value, used datasets. list, names must correspond names datasets mo_data. dataset missing list value provided, feature IDs used labels. Alternatively, use feature_id get feature IDs labels. mo_data MultiDataSet object. used label_cols NULL. truncate Integer, width labels truncated (avoid issues long labels plots). NULL (default value), truncation performed. ncol Integer, number columns datasets combined plot. Default value NULL, .e. picked automatically. label_size Integer, size features label. Default value 3.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_features_weight_pair.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plots features weight as a scatterplot — plot_features_weight_pair","text":"ggplot.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_features_weight_set.html","id":null,"dir":"Reference","previous_headings":"","what":"Plots features weight in/not in a set — plot_features_weight_set","title":"Plots features weight in/not in a set — plot_features_weight_set","text":"Plots distribution features weight integration method, depending whether features belong feature set interest.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_features_weight_set.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plots features weight in/not in a set — plot_features_weight_set","text":"","code":"plot_features_weight_set(   method_output,   feature_set,   set_name = \"set\",   features_metric = c(\"signed_importance\", \"weight\", \"importance\"),   add_missing_features = FALSE,   mo_data = NULL,   datasets = NULL,   latent_dimensions = NULL,   point_alpha = 0.5,   add_boxplot = TRUE,   scales = \"free_x\" )"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_features_weight_set.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plots features weight in/not in a set — plot_features_weight_set","text":"method_output Integration method output generated via get_output() function. feature_set Character vector, features ID belonging features set interest. set_name Character, name set. Default value 'set'. features_metric Character, features metric plotted y-axis. one 'signed_importance' (default value), 'weight' 'importance'. add_missing_features Logical, whether features multi-omics dataset (provided mo_data argument) weight integration results (e.g. selected pre-processing step) added results. TRUE (default value), added weight importance 0. mo_data MultiDataSet-class object. add_missing_features true, features multi-omics dataset weight integration method result added weight importance 0. datasets Character vector, name datasets features importance plotted. NULL (default value), datasets considered. latent_dimensions Character vector, latent dimensions represent plot. NULL (default value), latent dimensions represented. point_alpha Numeric 0 1, opacity points plot (1 = fully opaque, 0 = fully transparent). Default value 0.5. add_boxplot Logical, boxplot drawn top points categorical covariates? Default value TRUE. scales Character, value use scales argument ggplot2::facet_grid(). Default value 'free_x'.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_features_weight_set.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plots features weight in/not in a set — plot_features_weight_set","text":"ggplot.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_meansd_data.html","id":null,"dir":"Reference","previous_headings":"","what":"Per-dataset mean-sd trend plots for MultiDataSet object — plot_meansd_data","title":"Per-dataset mean-sd trend plots for MultiDataSet object — plot_meansd_data","text":"Displays dataset MultiDataSet object trend features mean standard deviation across samples.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_meansd_data.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Per-dataset mean-sd trend plots for MultiDataSet object — plot_meansd_data","text":"","code":"plot_meansd_data(   mo_data,   datasets = names(mo_data),   by_rank = FALSE,   colour_log10 = TRUE )"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_meansd_data.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Per-dataset mean-sd trend plots for MultiDataSet object — plot_meansd_data","text":"mo_data MultiDataSet::MultiDataSet object. datasets Character vector, names datasets include plot. default, datasets included. by_rank Logical, x-axis display rank features (ordered mean) rather features mean? Default value FALSE, .e. x axis represents mean features. colour_log10 colour legend log10 scale? Default value TRUE.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_meansd_data.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Per-dataset mean-sd trend plots for MultiDataSet object — plot_meansd_data","text":"ggplot.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_samples_coordinates_pca.html","id":null,"dir":"Reference","previous_headings":"","what":"Samples score plots for single-omics PCA — plot_samples_coordinates_pca","title":"Samples score plots for single-omics PCA — plot_samples_coordinates_pca","text":"Produces pairwise samples score plot PCA run omics dataset, using GGally::ggpairs().","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_samples_coordinates_pca.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Samples score plots for single-omics PCA — plot_samples_coordinates_pca","text":"","code":"plot_samples_coordinates_pca(pca_result, pcs = NULL, datasets = NULL, ...)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_samples_coordinates_pca.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Samples score plots for single-omics PCA — plot_samples_coordinates_pca","text":"pca_result List PCA results datasets, computed run_pca() function. pcs Integer vector named list integer vectors, principal components display dataset. integer vector (e.g. 1:5), used datasets. Alternatively, different set PCs can specified named list (e.g. list('snps' = 1:4, 'rnaseq' = 1:5)). length list must match number datasets displayed, names must match dataset names. Default value NULL, .e. principal components plotted dataset. datasets Optional, character vector datasets plots created. ... arguments passed plot_samples_score().","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_samples_coordinates_pca.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Samples score plots for single-omics PCA — plot_samples_coordinates_pca","text":"list ggmatrix plots (single ggmatrix plot pcs length 1).","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_samples_coordinates_pca.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Samples score plots for single-omics PCA — plot_samples_coordinates_pca","text":"","code":"if (FALSE) { ## Default: plotting all PCs for all datasets plot_samples_coordinates_pca(pca_result)  ## Plotting only the first 3 PCs for each dataset plot_samples_coordinates_pca(   pca_result,   pcs = 1:3 )  ## Plotting the first 3 PCs for the genomics dataset, 4 PCs for the ## transcriptomics dataset, 5 PCs for the metabolomics dataset plot_samples_coordinates_pca(   pca_result,   pcs = list(     \"snps\" = 1:3,     \"rnaseq\" = 1:4,     \"metabolome\" = 1:5   ) )  ## Plotting the first 3 PCs for the genomics and transcriptomics datasets plot_samples_coordinates_pca(   pca_result,   pcs = 1:3,   datasets = c(\"snps\", \"rnaseq\") )  # Plotting the first 3 PCs for the genomics dataset and 4 PCs for the ## transcriptomics dataset (no plot for the metabolomics dataset) plot_samples_coordinates_pca(   pca_result,   pcs = list(     \"snps\" = 1:3,     \"rnaseq\" = 1:4   ),   datasets = c(\"snps\", \"rnaseq\") ) }"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_samples_score.html","id":null,"dir":"Reference","previous_headings":"","what":"Plots sample scores as scatterplot matrix — plot_samples_score","title":"Plots sample scores as scatterplot matrix — plot_samples_score","text":"Plots samples score dimension reduction analysis matrix scatterplots. one latent dimension, plotted boxplot instead.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_samples_score.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plots sample scores as scatterplot matrix — plot_samples_score","text":"","code":"plot_samples_score(   method_output,   latent_dimensions = NULL,   mo_data = NULL,   colour_upper = NULL,   colour_diag = colour_upper,   colour_lower = colour_upper,   shape_upper = NULL,   shape_lower = shape_upper,   scale_colour_upper = NULL,   scale_colour_diag = NULL,   scale_colour_lower = NULL,   scale_shape_upper = NULL,   scale_shape_lower = NULL,   title = NULL,   point_size = 1.5 )"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_samples_score.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plots sample scores as scatterplot matrix — plot_samples_score","text":"method_output Integration method output generated via get_output() function. latent_dimensions Character vector giving latent dimensions display. NULL (default value), latent dimensions shown. mo_data MultiDataSet object (used extract samples information). colour_upper Character, name column one samples metadata tables mo_data use colouring observations upper triangle plots. Default value NULL. colour_diag Character, name column one samples metadata tables mo_data use colouring observations diagonal plots. default, follow colour_upper. colour_lower Character, name column one samples metadata tables mo_data use colouring observations lower triangle plots. default, follow colour_upper. shape_upper Character, name column one samples metadata tables mo_data use shaping observations upper triangle plots. Default value NULL. shape_lower Character, name column one samples metadata tables mo_data use shaping observations lower triangle plots. default, follow shape_upper. scale_colour_upper ggplot2 colour scale use upper triangle plots. Default value NULL (colour_upper NULL, use ggplot2 default colour scales). scale_colour_diag ggplot2 colour scale use diagonal plots. NULL (default), colour scale used upper triangle plots used colour_diag equal colour_upper; colour scale used lower triangle plots used colour_diag equal colour_lower. scale_colour_lower ggplot2 colour scale use lower triangle plots. NULL (default), colour scale used upper triangle plots used. scale_shape_upper ggplot2 shape scale use upper triangle plots. Default value NULL (shape_upper NULL, use ggplot2 default shape scale). scale_shape_lower ggplot2 shape scale use lower triangle plots. NULL (default), shape scale used upper triangle plots used. title Character, title plot. NULL (default value), method name method_output used construct plot title. point_size Numeric, size points (pt) plot. Default value 1.5.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_samples_score.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plots sample scores as scatterplot matrix — plot_samples_score","text":"ggmatrix plot.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_samples_score.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plots sample scores as scatterplot matrix — plot_samples_score","text":"","code":"if (FALSE) { ## Let's say we've already prepared a MultiDataSet mo_data, in which the ## datasets have samples metadata with columns treatment (discrete), ## weeks (continuous), tissue_type (discrete), disease_score (continuous).  library(ggplot2)  pca_res <- run_pca(mo_data, \"metabolome\") output_pca <- get_output_pca(output_pca) pcs <- paste0(\"Principal component \", 1:4)  # Simple matrix of scatterplot to visualised PCs two by two plot_samples_score(   output_pca,   pcs )  # Colouring points according to weeks plot_samples_score(   output_pca,   pcs,   colour_upper = \"weeks\" ) # Adding a custom colour palette plot_samples_score(   output_pca,   pcs,   colour_upper = \"weeks\",   scale_colour_upper = scale_colour_viridis_c() )  # Adding the treatment as shape plot_samples_score(   output_pca,   pcs,   colour_upper = \"weeks\",   shape_upper = \"treatment\" )  # Using the lower triangle of the plots to display disease score # Again can pass custom colour scale through scale_colour_lower plot_samples_score(   output_pca,   pcs,   colour_upper = \"weeks\",   shape_upper = \"treatment\",   colour_lower = \"disease_score\" )  # By default the diagonal plots follow the colour of the upper plots, # but can follow the lower plots instead plot_samples_score(   output_pca,   pcs,   colour_upper = \"weeks\",   shape_upper = \"treatment\",   colour_lower = \"disease_score\",   colour_diag = \"disease_score\" )  # or diagonal can show a different variable plot_samples_score(   output_pca,   pcs,   colour_upper = \"weeks\",   shape_upper = \"treatment\",   colour_lower = \"tissue_type\" )  # also the lower plots can have a different shape than the upper plots plot_samples_score(   output_pca,   pcs,   colour_upper = \"weeks\",   shape_upper = \"treatment\",   colour_lower = \"disease_score\",   shape_lower = \"tissue_type\" ) }"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_samples_score_covariate.html","id":null,"dir":"Reference","previous_headings":"","what":"Plots sample scores against covariate — plot_samples_score_covariate","title":"Plots sample scores against covariate — plot_samples_score_covariate","text":"Plots samples score result integration method covariate samples metadata.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_samples_score_covariate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plots sample scores against covariate — plot_samples_score_covariate","text":"","code":"plot_samples_score_covariate(   method_output,   mo_data,   covariate,   latent_dimensions = NULL,   colour_by = NULL,   shape_by = NULL,   point_alpha = 1,   add_se = TRUE,   add_boxplot = TRUE,   ncol = NULL )"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_samples_score_covariate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plots sample scores against covariate — plot_samples_score_covariate","text":"method_output Integration method output generated via get_output() function. mo_data MultiDataSet object (used extract samples information). covariate Character, name column one samples metadata tables mo_data use x-axis plot. latent_dimensions Character vector giving latent dimensions display. Default value NULL, .e. latent dimensions shown. colour_by Character, name column one samples metadata tables mo_data use colour samples plot. Default value NULL. shape_by Character, name column one samples metadata tables mo_data use shape samples plot. Default value NULL. point_alpha Numeric 0 1, opacity points plot (1 = fully opaque, 0 = fully transparent). Default value 1. add_se Logical, confidence interval drawn around smoothing curves numerical covariates? Default value TRUE. add_boxplot Logical, boxplot drawn top points categorical covariates? Default value TRUE. ncol Integer, number columns faceted plot. Default value NULL.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_samples_score_covariate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plots sample scores against covariate — plot_samples_score_covariate","text":"ggplot.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_samples_score_covariate.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Plots sample scores against covariate — plot_samples_score_covariate","text":"covariate numeric, function creates scatter plot, loess curve summarise trend covariate samples score. colour_by used, corresponding variable numeric, loess curve take account variable. instead colour_by variable character factor, loess curve fitted separately category. covariate numeric, function creates violin/boxplot. colour_by used, corresponding variable numeric, violins boxplots take account variable. instead colour_by variable character factor, separate violin boxplot drawn category.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_samples_score_pair.html","id":null,"dir":"Reference","previous_headings":"","what":"Plots sample scores as a scatterplot — plot_samples_score_pair","title":"Plots sample scores as a scatterplot — plot_samples_score_pair","text":"Plots samples score pair latent dimensions dimension reduction analysis scatterplot.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_samples_score_pair.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plots sample scores as a scatterplot — plot_samples_score_pair","text":"","code":"plot_samples_score_pair(   method_output,   latent_dimensions,   mo_data = NULL,   colour_by = NULL,   shape_by = NULL )"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_samples_score_pair.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plots sample scores as a scatterplot — plot_samples_score_pair","text":"method_output single Integration method output generated via get_output() function, list two integration method outputs. latent_dimensions Character vector length 2 (method_output single output object), named list length 2 (method_output list two output objects). first case, gives name two latent dimensions represented. second case, names list correspond names methods, values character giving corresponding method name latent dimension display. mo_data MultiDataSet object (used extract samples information). colour_by Character, name column one samples metadata tables mo_data use colouring samples plot. Default value NULL. shape_by Character, name column one samples metadata tables mo_data use shape samples plot. Default value NULL.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_samples_score_pair.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plots sample scores as a scatterplot — plot_samples_score_pair","text":"ggplot.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_samples_score_pair.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plots sample scores as a scatterplot — plot_samples_score_pair","text":"","code":"if (FALSE) { ## Let diablo_res be the output from a DIABLO analysis, and mofa_res the ## output from a MOFA analysis. Let mo_set be the corresponding MultiDataSet object. output_diablo <- get_output_diablo(diablo_res) output_mofa <- get_output_mofa2(mofa_res)  ## Scatterplot of the first two DIABLO components plot_samples_score_pair(output_diablo, c(\"Component 1\", \"Component 2\"))  ## Adding samples information to the plot - here 'Time' and 'Treatment' should ## two columns in the samples metadata of one of the datasets in mo_set plot_samples_score_pair(   output_diablo,   c(\"Component 1\", \"Component 2\"),   mo_data <- mo_set,   colour_by = \"Time\",   shape_by = \"Treatment\" )  ## Comparing the first MOFA factor to the first DIABLO component plot_samples_score_pair(   list(output_diablo, output_mofa),   list(\"DIABLO\" = \"Component 1\", \"MOFA\" = \"Factor 1\"),   mo_data <- mo_set,   colour_by = \"Time\",   shape_by = \"Treatment\" )  ## Giving custom names to the methods plot_samples_score_pair(   list(\"DIABLO prefiltered\" = output_diablo, \"MOFA full\" = output_mofa),   list(\"DIABLO prefiltered\" = \"Component 1\", \"MOFA full\" = \"Factor 1\") ) }"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_samples_upset.html","id":null,"dir":"Reference","previous_headings":"","what":"Upset plot of samples — plot_samples_upset","title":"Upset plot of samples — plot_samples_upset","text":"Generates upset plot compare samples present omics dataset.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_samples_upset.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Upset plot of samples — plot_samples_upset","text":"","code":"plot_samples_upset(mo_data)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_samples_upset.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Upset plot of samples — plot_samples_upset","text":"mo_data MultiDataSet::MultiDataSet object.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_samples_upset.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Upset plot of samples — plot_samples_upset","text":"upset plot.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_screeplot_pca.html","id":null,"dir":"Reference","previous_headings":"","what":"Screeplots for single-omics PCA — plot_screeplot_pca","title":"Screeplots for single-omics PCA — plot_screeplot_pca","text":"Produces scree plot (percentage variance explained principal component) PCA run omics dataset, using ggplot2.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_screeplot_pca.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Screeplots for single-omics PCA — plot_screeplot_pca","text":"","code":"plot_screeplot_pca(pca_result, cumulative = FALSE, datasets = NULL)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_screeplot_pca.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Screeplots for single-omics PCA — plot_screeplot_pca","text":"pca_result List PCA runs result datasets, computed run_pca() function. cumulative Logical, cumulative variance plotted? Default FALSE. datasets Optional, character vector names datasets plot. NULL (default value), datasets plotted.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_screeplot_pca.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Screeplots for single-omics PCA — plot_screeplot_pca","text":"ggplot2 plot.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_top_features.html","id":null,"dir":"Reference","previous_headings":"","what":"Plots top features importance — plot_top_features","title":"Plots top features importance — plot_top_features","text":"Plots top features importance per dataset latent dimension.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_top_features.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plots top features importance — plot_top_features","text":"","code":"plot_top_features(   method_output,   latent_dimensions = NULL,   group_latent_dims = TRUE,   datasets = NULL,   n_features = 20,   mo_data = NULL,   label_cols = NULL,   truncate = NULL,   nrow = 1 )"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_top_features.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plots top features importance — plot_top_features","text":"method_output Integration method output generated via get_output() function. latent_dimensions Character vector giving latent dimensions display. Default value NULL, .e. latent dimensions shown. group_latent_dims Logical, integrations methods construct datasets- specific versions latent dimension, grouped? e.g. DIABLO constructs snps- rnaseq version component 1, two grouped \"Component 1\"? Default value TRUE. datasets Character vector giving datasets display. Default value NULL, .e. datasets shown. n_features Integer, number top features display per dataset latent dimension. mo_data MultiDataSet object. used label_cols NULL. label_cols Character named list character, giving dataset name column corresponding features metadata use label. one value, used datasets. list, names must correspond names datasets mo_data. dataset missing list value provided, feature IDs used labels. Alternatively, use feature_id get feature IDs labels. truncate Integer, width labels truncated (avoid issues long labels plots). NULL (default value), truncation performed. nrow Integer, number rows dataset panels plotted latent dimensions.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_top_features.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plots top features importance — plot_top_features","text":"patchwork plots.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_variance_explained.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot of variance explained — plot_variance_explained","title":"Plot of variance explained — plot_variance_explained","text":"Displays percentage variance explained latent dimension output dimension reduction method dataset analysed.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_variance_explained.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot of variance explained — plot_variance_explained","text":"","code":"plot_variance_explained(   method_output,   datasets = NULL,   latent_dimensions = NULL,   ncol = NULL,   free_y_axis = FALSE,   cumulative = FALSE )"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_variance_explained.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot of variance explained — plot_variance_explained","text":"method_output Integration method output generated via get_output() function. datasets Character vector giving datasets display. Default value NULL, .e. datasets shown. latent_dimensions Character vector giving latent dimensions display. Default value NULL, .e. latent dimensions shown. ncol Integer, number columns faceted plot. Default value NULL. free_y_axis Logical, whether y-axis (representing percentage variance) range datasets. Default value FALSE. cumulative Logical, whether cumulative percentage variance explained plotted. Default FALSE.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_variance_explained.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot of variance explained — plot_variance_explained","text":"ggplot.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_x_continuous.html","id":null,"dir":"Reference","previous_headings":"","what":"Scatter plot function — plot_x_continuous","title":"Scatter plot function — plot_x_continuous","text":"Creates scatter plot","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_x_continuous.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Scatter plot function — plot_x_continuous","text":"","code":"plot_x_continuous(toplot, x, y, colour, shape, point_alpha = 1, add_se = TRUE)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_x_continuous.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Scatter plot function — plot_x_continuous","text":"toplot Tibble data plot. x Character, name column toplot use x-axis. y Character, name column toplot use y-axis. colour Character, name column toplot use colour. shape Character, name column toplot use shape. point_alpha Numeric 0 1, opacity points plot (1 = fully opaque, 0 = fully transparent). Default value 1. add_se Logical, confidence interval drawn around smoothing curve scatterplots? Default value TRUE.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_x_continuous.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Scatter plot function — plot_x_continuous","text":"ggplot.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_x_continuous.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Scatter plot function — plot_x_continuous","text":"function adds loess curve summarise trend covariate samples score. colour_by used, corresponding variable numeric, loess curve take account variable. instead colour_by variable character factor, loess curve fitted separately category.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_x_discrete.html","id":null,"dir":"Reference","previous_headings":"","what":"Violin plot function function — plot_x_discrete","title":"Violin plot function function — plot_x_discrete","text":"Creates violin plot","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_x_discrete.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Violin plot function function — plot_x_discrete","text":"","code":"plot_x_discrete(   toplot,   x,   y,   colour,   shape,   point_alpha = 1,   add_boxplot = TRUE )"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_x_discrete.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Violin plot function function — plot_x_discrete","text":"toplot Tibble data plot. x Character, name column toplot use x-axis. y Character, name column toplot use y-axis. colour Character, name column toplot use colour. shape Character, name column toplot use shape. point_alpha Numeric 0 1, opacity points plot (1 = fully opaque, 0 = fully transparent). Default value 1. add_boxplot Logical, boxplot drawn top points violin plots? Default value TRUE.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_x_discrete.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Violin plot function function — plot_x_discrete","text":"ggplot.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_x_discrete.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Violin plot function function — plot_x_discrete","text":"colour_by used, corresponding variable numeric, violins boxplots take account variable. instead colour_by variable character factor, separate violin boxplot drawn category.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_x_wrapper.html","id":null,"dir":"Reference","previous_headings":"","what":"Wrapper to create plot — plot_x_wrapper","title":"Wrapper to create plot — plot_x_wrapper","text":"Wrapper around plot_x_continuous() plot_x_discrete(), choose one use depending whether x-axis variable continuous discrete.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_x_wrapper.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Wrapper to create plot — plot_x_wrapper","text":"","code":"plot_x_wrapper(   toplot,   x,   y,   colour,   shape,   point_alpha = 1,   add_se = TRUE,   add_boxplot = TRUE,   facet_wrap = NULL,   ncol_wrap = NULL,   facet_grid = NULL,   scales_facet = \"free_y\" )"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_x_wrapper.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Wrapper to create plot — plot_x_wrapper","text":"toplot Tibble data plot. x Character, name column toplot use x-axis. y Character, name column toplot use y-axis. colour Character, name column toplot use colour. shape Character, name column toplot use shape. point_alpha Numeric 0 1, opacity points plot (1 = fully opaque, 0 = fully transparent). Default value 1. add_se Logical, confidence interval drawn around smoothing curve scatterplots? Default value TRUE. add_boxplot Logical, boxplot drawn top points violin plots? Default value TRUE. facet_wrap Character, name column toplot use faceting (using ggplot2::facet_wrap()). Default NULL. ncol_wrap Integer, number columns faceted plot using facet_wrap. Default value NULL. facet_grid Character vector length 2, name columns toplot use row (first element) column (second element) faceting (using ggplot2::facet_grid()). ignored facet_wrap NULL. Default NULL. scales_facet Character, value use scales argument ggplot2::facet_wrap() ggplot2::facet_grid(). Default value 'free_y'.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/plot_x_wrapper.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Wrapper to create plot — plot_x_wrapper","text":"ggplot.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/reduce_feature_sets_data.html","id":null,"dir":"Reference","previous_headings":"","what":"Reduce feature sets to match multi-omics dataset — reduce_feature_sets_data","title":"Reduce feature sets to match multi-omics dataset — reduce_feature_sets_data","text":"Removes list feature sets features present multi-omics dataset.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/reduce_feature_sets_data.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Reduce feature sets to match multi-omics dataset — reduce_feature_sets_data","text":"","code":"reduce_feature_sets_data(feature_sets, mo_data, datasets = names(mo_data))"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/reduce_feature_sets_data.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Reduce feature sets to match multi-omics dataset — reduce_feature_sets_data","text":"feature_sets Named list, element corresponds feature set, contains vector features ID features belonging set. mo_data MultiDataSet-class object. datasets Character vector, names datasets features assignment checked. default, datasets mo_data considered.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/reduce_feature_sets_data.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Reduce feature sets to match multi-omics dataset — reduce_feature_sets_data","text":"feature sets list, .e. named list element corresponds feature set, containing ID features belong set present multi-omics dataset.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/replace_dataset.html","id":null,"dir":"Reference","previous_headings":"","what":"Replace matrix dataset within a MultiDataSet object — replace_dataset","title":"Replace matrix dataset within a MultiDataSet object — replace_dataset","text":"Replaces matrix omics dataset new matrix MultiDataSet object.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/replace_dataset.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Replace matrix dataset within a MultiDataSet object — replace_dataset","text":"","code":"replace_dataset(mo_data, dataset_name, new_data)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/replace_dataset.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Replace matrix dataset within a MultiDataSet object — replace_dataset","text":"mo_data MultiDataSet::MultiDataSet object. dataset_name Character, name dataset matrix data changed. new_data Matrix, new data. features rows samples columns. Rownames match corresponding feature IDs, colnames match corresponding sample IDs.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/replace_dataset.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Replace matrix dataset within a MultiDataSet object — replace_dataset","text":"MultiDataSet object.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/round_dataset.html","id":null,"dir":"Reference","previous_headings":"","what":"Round values in omics dataset from MultiDataSet object — round_dataset","title":"Round values in omics dataset from MultiDataSet object — round_dataset","text":"Rounds values given omics dataset within MultiDataSet object. Can also limit range possible values.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/round_dataset.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Round values in omics dataset from MultiDataSet object — round_dataset","text":"","code":"round_dataset(   mo_data,   dataset_name,   ndecimals = 0,   min_val = -Inf,   max_val = Inf )"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/round_dataset.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Round values in omics dataset from MultiDataSet object — round_dataset","text":"mo_data MultiDataSet::MultiDataSet object. dataset_name Character, name dataset matrix data changed. ndecimals Integer, number decimals keep dataset. Default value 0. min_val Numeric, minimum value allowed dataset. Values min_val set min_val. max_val Numeric, maximum value allowed dataset. Values max_val set max_val.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/round_dataset.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Round values in omics dataset from MultiDataSet object — round_dataset","text":"MultiDataSet object.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/round_dataset.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Round values in omics dataset from MultiDataSet object — round_dataset","text":"","code":"if (FALSE) { ## Let's imagine that we imputed missing values in the genomics dataset from ## mo_data using NIPALS-PCA. The imputed values are continuous, but the ## dataset contains dosage values for a diploid organism (i.e. values can ## be 0, 1, 2). We'll round the imputed values and make sure they can't be ## negative or higher than 2. round_dataset(mo_data, \"snps\", min_val = 0, max_val = 2) }"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/run_pairwise_pls.html","id":null,"dir":"Reference","previous_headings":"","what":"Pairwise PLS datasets comparison — run_pairwise_pls","title":"Pairwise PLS datasets comparison — run_pairwise_pls","text":"Runs Projection Latent Structure (PLS) analysis pair omics datasets, per mixOmics package.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/run_pairwise_pls.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Pairwise PLS datasets comparison — run_pairwise_pls","text":"","code":"run_pairwise_pls(mixomics_data, datasets_name, ..., verbose = TRUE)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/run_pairwise_pls.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Pairwise PLS datasets comparison — run_pairwise_pls","text":"mixomics_data mixOmics input object created get_input_mixomics_supervised. datasets_name Character vector length 2, names two omics datasets analyse. ... Additional parameters passed pls function. verbose Logical, details printed execution? Default value TRUE.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/run_pairwise_pls.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Pairwise PLS datasets comparison — run_pairwise_pls","text":"named list; element object class pls, provides result PLS run. name datasets analysed stored character vector datasets_name attribute.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/run_pairwise_pls.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Pairwise PLS datasets comparison — run_pairwise_pls","text":"Note one latent component computed first latent component used assess correlation datasets.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/run_pairwise_pls.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Pairwise PLS datasets comparison — run_pairwise_pls","text":"","code":"if (FALSE) { run_pairwise_pls(mo_set, c(\"rnaseq\", \"metabolome\")) }"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/run_pca.html","id":null,"dir":"Reference","previous_headings":"","what":"Run PCA on MultiDataSet — run_pca","title":"Run PCA on MultiDataSet — run_pca","text":"Runs Principal Component Analysis omics dataset MultiDataSet object. wrapper function around get_dataset_matrix() run_pca_matrix() functions.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/run_pca.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Run PCA on MultiDataSet — run_pca","text":"","code":"run_pca(   mo_data,   dataset_name,   n_pcs = 10,   scale = \"none\",   center = TRUE,   method = NULL )"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/run_pca.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Run PCA on MultiDataSet — run_pca","text":"mo_data MultiDataSet::MultiDataSet object. dataset_name Character, name omics dataset PCA run. n_pcs numeric, number Principal Components compute. Default value 10. scale character, type scaling applied dataset running PCA. one 'none', 'pareto', 'vector', 'uv' (see pcaMethods::pca()). Default value 'none'. center boolean, dataset centred prior running PCA? Default value TRUE. method character, type PCA applied dataset. See pcaMethods::listPcaMethods(). list available methods. Default value 'svd' datasets missing value, 'nipals' datasets missing values.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/run_pca.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Run PCA on MultiDataSet — run_pca","text":"pcaMethods::pcaRes object containing result PCA analysis. attribute dataset_name specifies name dataset analysed.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/run_pca.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Run PCA on MultiDataSet — run_pca","text":"facilitate use dynamic branching targets package, dataset_name attribute resulting object set value dataset_name parameter, can accessed via attr(res_pca, \"dataset_name\").","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/run_pca_matrix.html","id":null,"dir":"Reference","previous_headings":"","what":"Run PCA on matrix — run_pca_matrix","title":"Run PCA on matrix — run_pca_matrix","text":"Runs Principal Component Analysis omics matrix, using pcaMethods::pca() function.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/run_pca_matrix.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Run PCA on matrix — run_pca_matrix","text":"","code":"run_pca_matrix(mat, n_pcs = 10, scale = \"none\", center = TRUE, method = NULL)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/run_pca_matrix.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Run PCA on matrix — run_pca_matrix","text":"mat Matrix omics measurement, features rows samples columns. n_pcs numeric, number Principal Components compute. Default value 10. scale character, type scaling applied dataset running PCA. one 'none', 'pareto', 'vector', 'uv' (see pcaMethods::pca()). Default value 'none'. center boolean, dataset centred prior running PCA? Default value TRUE. method character, type PCA applied dataset. See pcaMethods::listPcaMethods(). list available methods. Default value 'svd' datasets missing value, 'nipals' datasets missing values.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/run_pca_matrix.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Run PCA on matrix — run_pca_matrix","text":"pcaMethods::pcaRes object containing result PCA analysis. attribute dataset_name specifies name dataset analysed.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/run_splsda.html","id":null,"dir":"Reference","previous_headings":"","what":"Performs sPLS-DA on omics dataset from MultiDataSet object — run_splsda","title":"Performs sPLS-DA on omics dataset from MultiDataSet object — run_splsda","text":"Performs sPLS-DA (implemented mixOmics) package omics dataset MultiDataSet object. intended feature preselection omics dataset (see get_filtered_dataset_splsda).","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/run_splsda.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Performs sPLS-DA on omics dataset from MultiDataSet object — run_splsda","text":"","code":"run_splsda(   splsda_input,   perf_res,   to_keep_n = NULL,   to_keep_prop = NULL,   ncomp = NULL )"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/run_splsda.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Performs sPLS-DA on omics dataset from MultiDataSet object — run_splsda","text":"splsda_input Input sPLS-DA functions mixOmics, created get_input_splsda(). perf_res Result perf_splsda function. supplied, sPLS-DA run dataset specified argument dataset_name number latent components specified argument comp. to_keep_n Integer, number features retain dataset. less number features dataset. NULL NA, to_keep_prop used instead. to_keep_prop Numeric, proportion features retain dataset. ignored to_keep_n supplied. Value > 0 < 1. ncomp Integer, number latent components construct. Ignored perf_res supplied. Default value NULL.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/run_splsda.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Performs sPLS-DA on omics dataset from MultiDataSet object — run_splsda","text":"list per output splsda function.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/run_splsda.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Performs sPLS-DA on omics dataset from MultiDataSet object — run_splsda","text":"function uses plsda function mixOmics package. Note sPLS-DA method can select feature several latent components, number features retained dataset might less number specified to_keep_n argument.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/select_features_cov.html","id":null,"dir":"Reference","previous_headings":"","what":"Select features based on Coefficient of Variation from MultiDataSet — select_features_cov","title":"Select features based on Coefficient of Variation from MultiDataSet — select_features_cov","text":"Computes Coefficient Variation (COV) feature omics dataset MultiDataSet object, select features highest COV values. wrapper function around get_dataset_matrix() select_features_cov_matrix() functions.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/select_features_cov.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Select features based on Coefficient of Variation from MultiDataSet — select_features_cov","text":"","code":"select_features_cov(   mo_data,   dataset_name,   to_keep_n = NULL,   to_keep_prop = NULL,   with_ties = TRUE )"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/select_features_cov.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Select features based on Coefficient of Variation from MultiDataSet — select_features_cov","text":"mo_data MultiDataSet-class object. dataset_name Character, name omics dataset apply feature pre-selection. to_keep_n Integer, number features retain dataset. less number features dataset. NULL NA, to_keep_prop used instead. to_keep_prop Numeric, proportion features retain dataset. ignored to_keep_n supplied. Value > 0 < 1. with_ties ties kept together? TRUE, may return features requested. Default value TRUE.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/select_features_cov.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Select features based on Coefficient of Variation from MultiDataSet — select_features_cov","text":"tibble columns feature_id, cov selected (logical, indicates whether feature selected based COV value). addition, name dataset filtered stored return object attribute dataset_name (can accessed via attr(res, \"dataset_name\")).","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/select_features_cov_matrix.html","id":null,"dir":"Reference","previous_headings":"","what":"Select features based on Coefficient of Variation from matrix — select_features_cov_matrix","title":"Select features based on Coefficient of Variation from matrix — select_features_cov_matrix","text":"Computes Coefficient Variation (COV) feature omics dataset MultiDataSet object, select features highest COV values.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/select_features_cov_matrix.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Select features based on Coefficient of Variation from matrix — select_features_cov_matrix","text":"","code":"select_features_cov_matrix(   mat,   to_keep_n = NULL,   to_keep_prop = NULL,   with_ties = TRUE )"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/select_features_cov_matrix.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Select features based on Coefficient of Variation from matrix — select_features_cov_matrix","text":"mat Matrix omics measurement, features rows samples columns. to_keep_n Integer, number features retain dataset. less number features dataset. NULL NA, to_keep_prop used instead. to_keep_prop Numeric, proportion features retain dataset. ignored to_keep_n supplied. Value > 0 < 1. with_ties ties kept together? TRUE, may return features requested. Default value TRUE.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/select_features_cov_matrix.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Select features based on Coefficient of Variation from matrix — select_features_cov_matrix","text":"tibble columns feature_id, cov selected (logical, indicates whether feature selected based COV value). addition, name dataset filtered stored return object attribute dataset_name (can accessed via attr(res, \"dataset_name\")).","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/select_features_mad.html","id":null,"dir":"Reference","previous_headings":"","what":"Select features based on Median Absolute Deviation from MultiDataSet — select_features_mad","title":"Select features based on Median Absolute Deviation from MultiDataSet — select_features_mad","text":"Computes Median Absolute Deviation (MAD) feature omics dataset MultiDataSet object, select features highest MAD values. wrapper function around get_dataset_matrix() select_features_mad_matrix() functions.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/select_features_mad.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Select features based on Median Absolute Deviation from MultiDataSet — select_features_mad","text":"","code":"select_features_mad(   mo_data,   dataset_name,   to_keep_n = NULL,   to_keep_prop = NULL,   with_ties = TRUE )"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/select_features_mad.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Select features based on Median Absolute Deviation from MultiDataSet — select_features_mad","text":"mo_data MultiDataSet-class object. dataset_name Character, name omics dataset apply feature pre-selection. to_keep_n Integer, number features retain dataset. less number features dataset. NULL NA, to_keep_prop used instead. to_keep_prop Numeric, proportion features retain dataset. ignored to_keep_n supplied. Value > 0 < 1. with_ties ties kept together? TRUE, may return features requested. Default value TRUE.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/select_features_mad.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Select features based on Median Absolute Deviation from MultiDataSet — select_features_mad","text":"tibble columns feature_id, mad selected (logical, indicates whether feature selected based MAD value). addition, name dataset filtered stored return object attribute dataset_name (can accessed via attr(res, \"dataset_name\")).","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/select_features_mad_matrix.html","id":null,"dir":"Reference","previous_headings":"","what":"Select features based on Median Absolute Deviation from matrix — select_features_mad_matrix","title":"Select features based on Median Absolute Deviation from matrix — select_features_mad_matrix","text":"Computes Median Absolute Deviation (MAD) feature omics dataset MultiDataSet object, select features highest MAD values.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/select_features_mad_matrix.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Select features based on Median Absolute Deviation from matrix — select_features_mad_matrix","text":"","code":"select_features_mad_matrix(   mat,   to_keep_n = NULL,   to_keep_prop = NULL,   with_ties = TRUE )"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/select_features_mad_matrix.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Select features based on Median Absolute Deviation from matrix — select_features_mad_matrix","text":"mat Matrix omics measurement, features rows samples columns. to_keep_n Integer, number features retain dataset. less number features dataset. NULL NA, to_keep_prop used instead. to_keep_prop Numeric, proportion features retain dataset. ignored to_keep_n supplied. Value > 0 < 1. with_ties ties kept together? TRUE, may return features requested. Default value TRUE.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/select_features_mad_matrix.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Select features based on Median Absolute Deviation from matrix — select_features_mad_matrix","text":"tibble columns feature_id, mad selected (logical, indicates whether feature selected based MAD value). addition, name dataset filtered stored return object attribute dataset_name (can accessed via attr(res, \"dataset_name\")).","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/show_consensus_metrics.html","id":null,"dir":"Reference","previous_headings":"","what":"Illustrates importance consensus metrics — show_consensus_metrics","title":"Illustrates importance consensus metrics — show_consensus_metrics","text":"Plots heatmap illustrate behaviour different importance consensus metrics.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/show_consensus_metrics.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Illustrates importance consensus metrics — show_consensus_metrics","text":"","code":"show_consensus_metrics(   metrics = c(\"min\", \"harmonic\", \"geometric\", \"product\", \"average\", \"l2\", \"max\") )"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/show_consensus_metrics.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Illustrates importance consensus metrics — show_consensus_metrics","text":"metrics Character vector metrics show. valid values metric argument consensus_importance_metric(), .e. \"min\", \"max\", \"average\", \"product\", \"l2\", \"geometric\", \"harmonic\".","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/show_consensus_metrics.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Illustrates importance consensus metrics — show_consensus_metrics","text":"ggplot.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/so2pls_compare_samples_joint_components.html","id":null,"dir":"Reference","previous_headings":"","what":"Compares sO2PLS samples joint component scores between the two datasets — so2pls_compare_samples_joint_components","title":"Compares sO2PLS samples joint component scores between the two datasets — so2pls_compare_samples_joint_components","text":"Plots comparison samples joint component scores obtained two datasets sO2PLS run (OmicsPLS package).","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/so2pls_compare_samples_joint_components.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compares sO2PLS samples joint component scores between the two datasets — so2pls_compare_samples_joint_components","text":"","code":"so2pls_compare_samples_joint_components(so2pls_res, components = NULL, ...)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/so2pls_compare_samples_joint_components.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compares sO2PLS samples joint component scores between the two datasets — so2pls_compare_samples_joint_components","text":"so2pls_res output o2m function. components Optional, integer vector joint components plotted. Default NULL, .e. joint components represented. ... arguments passed plot_samples_score_pair.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/so2pls_compare_samples_joint_components.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compares sO2PLS samples joint component scores between the two datasets — so2pls_compare_samples_joint_components","text":"patchwork ggplots.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/so2pls_crossval_o2m.html","id":null,"dir":"Reference","previous_headings":"","what":"Wrapper for OmicsPLS::crossval_o2m function — so2pls_crossval_o2m","title":"Wrapper for OmicsPLS::crossval_o2m function — so2pls_crossval_o2m","text":"Wrapper function around crossval_o2m function. main purpose wrapper add result names datasets facilitate plotting. result previous call crossval_o2m_adjR2 so2pls_crossval_o2m_adjR2 provided, used set values test , ax ay.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/so2pls_crossval_o2m.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Wrapper for OmicsPLS::crossval_o2m function — so2pls_crossval_o2m","text":"","code":"so2pls_crossval_o2m(   omicspls_input,   cv_adj_res = NULL,   a = 1:5,   ax = seq(0, 10, by = 2),   ay = seq(0, 10, by = 2),   nr_folds = 10,   ... )"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/so2pls_crossval_o2m.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Wrapper for OmicsPLS::crossval_o2m function — so2pls_crossval_o2m","text":"omicspls_input named list length 2, produced get_input_omicspls. cv_adj_res Data-frame returned crossval_o2m_adjR2 so2pls_crossval_o2m_adjR2. Default value NULL. Vector positive integers, number joint components test. Ignored cv_adj_res NULL. ax Vector non-negative integers, number specific components test first dataset. Ignored cv_adj_res NULL. ay Vector non-negative integers, number specific components test second dataset. Ignored cv_adj_res NULL. nr_folds Positive integer, number folds use cross-validation. Default value 10. ... arguments passed crossval_o2m_adjR2 function.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/so2pls_crossval_o2m.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Wrapper for OmicsPLS::crossval_o2m function — so2pls_crossval_o2m","text":"list class \"cvo2m\" original sorted Prediction errors number folds used.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/so2pls_crossval_o2m.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Wrapper for OmicsPLS::crossval_o2m function — so2pls_crossval_o2m","text":"result previous call crossval_o2m_adjR2 so2pls_crossval_o2m_adjR2 provided cv_adj_res parameter, optimal values n, nx ny extracted , values , ax ay set follows: = max(n - 1, 1):(n + 1) ax = max(nx - 1, 0):(nx + 1) ay = max(ny - 1, 0):(ny + 1)","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/so2pls_crossval_o2m_adjR2.html","id":null,"dir":"Reference","previous_headings":"","what":"Wrapper for OmicsPLS::crossval_o2m_adjR2 function — so2pls_crossval_o2m_adjR2","title":"Wrapper for OmicsPLS::crossval_o2m_adjR2 function — so2pls_crossval_o2m_adjR2","text":"Wrapper function around crossval_o2m_adjR2 function. main purpose wrapper add result names datasets facilitate plotting.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/so2pls_crossval_o2m_adjR2.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Wrapper for OmicsPLS::crossval_o2m_adjR2 function — so2pls_crossval_o2m_adjR2","text":"","code":"so2pls_crossval_o2m_adjR2(   omicspls_input,   a = 1:5,   ax = seq(0, 10, by = 2),   ay = seq(0, 10, by = 2),   nr_folds = 10,   ... )"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/so2pls_crossval_o2m_adjR2.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Wrapper for OmicsPLS::crossval_o2m_adjR2 function — so2pls_crossval_o2m_adjR2","text":"omicspls_input named list length 2, produced get_input_omicspls. Vector positive integers, number joint components test. ax Vector non-negative integers, number specific components test first dataset. ay Vector non-negative integers, number specific components test second dataset. nr_folds Positive integer, number folds use cross-validation. Default value 10. ... arguments passed crossval_o2m_adjR2 function.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/so2pls_crossval_o2m_adjR2.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Wrapper for OmicsPLS::crossval_o2m_adjR2 function — so2pls_crossval_o2m_adjR2","text":"data-frame four columns: MSE, n, nx ny. row corresponds element .","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/so2pls_crossval_sparsity.html","id":null,"dir":"Reference","previous_headings":"","what":"Perform cross-validation to find the optimal number of features/groups to keep for each joint component for sO2PLS — so2pls_crossval_sparsity","title":"Perform cross-validation to find the optimal number of features/groups to keep for each joint component for sO2PLS — so2pls_crossval_sparsity","text":"Computes optimal number features/groups keep joint component sO2PLS run. Directly copied crossval_sparsity function, improved output plotting purposes.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/so2pls_crossval_sparsity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Perform cross-validation to find the optimal number of features/groups to keep for each joint component for sO2PLS — so2pls_crossval_sparsity","text":"","code":"so2pls_crossval_sparsity(   omicspls_input,   n,   nx,   ny,   nr_folds = 10,   keepx_seq = NULL,   keepy_seq = NULL,   groupx = NULL,   groupy = NULL,   tol = 1e-10,   max_iterations = 100 )"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/so2pls_crossval_sparsity.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Perform cross-validation to find the optimal number of features/groups to keep for each joint component for sO2PLS — so2pls_crossval_sparsity","text":"omicspls_input named list length 2, produced get_input_omicspls. n Integer, number joint PLS components. Must positive. nx Integer, number orthogonal components X. Negative values interpreted 0. ny Integer, number orthogonal components Y. Negative values interpreted 0. nr_folds integer, number folds cross-validation. Default value 10. keepx_seq Numeric vector, many features/groups keep cross-validation joint components X. Sparsity joint component selected sequentially. keepy_seq Numeric vector, many features/groups keep cross-validation joint components Y. Sparsity joint component selected sequentially. groupx Character vector, group name X-feature. length must equal number features X. order group names must corresponds order features. NULL, groups considered. Default value NULL. groupy Character vector, group name Y-feature. length must equal number features Y. order group names must corresponds order features. NULL, groups considered. Default value NULL. tol Numeric, threshold NIPALS method deemed converged. Must positive. Default value 1e-10. max_iterations Integer, maximum number iterations NIPALS method.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/so2pls_crossval_sparsity.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Perform cross-validation to find the optimal number of features/groups to keep for each joint component for sO2PLS — so2pls_crossval_sparsity","text":"list following elements: Best: vector giving join component number features keep X Y yield highest covariance joint components X Y (elements x1, y1, x2, y2, etc), number features keep X Y yielding highest covariance 1 standard error rule (elements x_1sd1, y_1sd1, x_1sd2, y_1sd2, etc). Covs: list, many elements number joint components (n). element matrix giving average covariance joint components X Y obtained across folds, tested values keepx (columns) keepy (rows). SEcov: list, many elements number joint components (n). element matrix giving standard error covariance joint components X Y obtained across folds, tested values keepx (columns) keepy (rows).","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/so2pls_get_components.html","id":null,"dir":"Reference","previous_headings":"","what":"Get list of latent components from sO2PLS results — so2pls_get_components","title":"Get list of latent components from sO2PLS results — so2pls_get_components","text":"Extracts list joint specific latent components sO2PLS results.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/so2pls_get_components.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get list of latent components from sO2PLS results — so2pls_get_components","text":"","code":"so2pls_get_components(so2pls_res)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/so2pls_get_components.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get list of latent components from sO2PLS results — so2pls_get_components","text":"so2pls_res sO2PLS results generated get_output_so2pls() function.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/so2pls_get_components.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get list of latent components from sO2PLS results — so2pls_get_components","text":"list following elements: joint: character vector name joint latent components. specific: named list length 2. element corresponds dataset (names list datasets name), character vector name specific latent components corresponding dataset.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/so2pls_get_optim_keep.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract optimal number of features to keep from cross-validation results for\nsO2PLS — so2pls_get_optim_keep","title":"Extract optimal number of features to keep from cross-validation results for\nsO2PLS — so2pls_get_optim_keep","text":"Extracts optimal number features retain datasets X Y joint components.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/so2pls_get_optim_keep.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract optimal number of features to keep from cross-validation results for\nsO2PLS — so2pls_get_optim_keep","text":"","code":"so2pls_get_optim_keep(cv_res, use_1sd_rule = TRUE)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/so2pls_get_optim_keep.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract optimal number of features to keep from cross-validation results for\nsO2PLS — so2pls_get_optim_keep","text":"cv_res List, result call so2pls_crossval_sparsity() OmicsPLS::crossval_sparsity(). use_1sd_rule Boolean, 1 standard deviation rule used selecting optimal number features retain? See Details.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/so2pls_get_optim_keep.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract optimal number of features to keep from cross-validation results for\nsO2PLS — so2pls_get_optim_keep","text":"list elements keepx keepy, vector length equal number joint components, ith element giving number features retain dataset X (keepx) Y (keepy) -th joint component.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/so2pls_get_optim_keep.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Extract optimal number of features to keep from cross-validation results for\nsO2PLS — so2pls_get_optim_keep","text":"1-SD rule means retaining smallest number features yielding average covariance within 1SD maximum covariance obtained.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/so2pls_get_optim_ncomp.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract optimal number of components from cross-validation results for sO2PLS — so2pls_get_optim_ncomp","title":"Extract optimal number of components from cross-validation results for sO2PLS — so2pls_get_optim_ncomp","text":"Extracts optimal number components (joint dataset-specific) estimated via cross-validation results sO2PLS.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/so2pls_get_optim_ncomp.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract optimal number of components from cross-validation results for sO2PLS — so2pls_get_optim_ncomp","text":"","code":"so2pls_get_optim_ncomp(cv_res)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/so2pls_get_optim_ncomp.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract optimal number of components from cross-validation results for sO2PLS — so2pls_get_optim_ncomp","text":"cv_res cvo2m object, output crossval_o2m function.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/so2pls_get_optim_ncomp.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract optimal number of components from cross-validation results for sO2PLS — so2pls_get_optim_ncomp","text":"vector three integer values: n: optimal number joint components nx: optimal number specific components dataset X (first dataset) ny: optimal number specific components dataset Y (second dataset)","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/so2pls_get_optim_ncomp_adj.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract optimal number of components from adjusted cross-validation results for sO2PLS — so2pls_get_optim_ncomp_adj","title":"Extract optimal number of components from adjusted cross-validation results for sO2PLS — so2pls_get_optim_ncomp_adj","text":"Extracts optimal number components (joint dataset-specific) estimated via adjusted cross-validation results sO2PLS.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/so2pls_get_optim_ncomp_adj.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract optimal number of components from adjusted cross-validation results for sO2PLS — so2pls_get_optim_ncomp_adj","text":"","code":"so2pls_get_optim_ncomp_adj(cv_res)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/so2pls_get_optim_ncomp_adj.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract optimal number of components from adjusted cross-validation results for sO2PLS — so2pls_get_optim_ncomp_adj","text":"cv_res Data-frame, output crossval_o2m_adjR2 function.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/so2pls_get_optim_ncomp_adj.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract optimal number of components from adjusted cross-validation results for sO2PLS — so2pls_get_optim_ncomp_adj","text":"vector three integer values: n: optimal number joint components nx: optimal number specific components dataset X (first dataset) ny: optimal number specific components dataset Y (second dataset)","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/so2pls_get_variance_explained.html","id":null,"dir":"Reference","previous_headings":"","what":"Percentage of variance explained for sO2PLS — so2pls_get_variance_explained","title":"Percentage of variance explained for sO2PLS — so2pls_get_variance_explained","text":"Generates table giving percentage variance explained component sO2PLS corresponding dataset.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/so2pls_get_variance_explained.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Percentage of variance explained for sO2PLS — so2pls_get_variance_explained","text":"","code":"so2pls_get_variance_explained(so2pls_res)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/so2pls_get_variance_explained.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Percentage of variance explained for sO2PLS — so2pls_get_variance_explained","text":"so2pls_res output o2m function.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/so2pls_get_variance_explained.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Percentage of variance explained for sO2PLS — so2pls_get_variance_explained","text":"tibble columns latent_dimension, dataset prop_var_expl.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/so2pls_get_wa_coord.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes average sample coordinates for sO2PLS joint components — so2pls_get_wa_coord","title":"Computes average sample coordinates for sO2PLS joint components — so2pls_get_wa_coord","text":"Computes average sample coordinates sO2PLS joint components across two datasets.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/so2pls_get_wa_coord.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes average sample coordinates for sO2PLS joint components — so2pls_get_wa_coord","text":"","code":"so2pls_get_wa_coord(so2pls_res)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/so2pls_get_wa_coord.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes average sample coordinates for sO2PLS joint components — so2pls_get_wa_coord","text":"so2pls_res output o2m function.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/so2pls_get_wa_coord.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes average sample coordinates for sO2PLS joint components — so2pls_get_wa_coord","text":"matrix samples coordinates, samples rows joint components columns.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/so2pls_o2m.html","id":null,"dir":"Reference","previous_headings":"","what":"Wrapper for OmicsPLS::o2m function — so2pls_o2m","title":"Wrapper for OmicsPLS::o2m function — so2pls_o2m","text":"Wrapper function around o2m function. main purpose wrapper add result names datasets facilitate plotting.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/so2pls_o2m.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Wrapper for OmicsPLS::o2m function — so2pls_o2m","text":"","code":"so2pls_o2m(   omicspls_input,   cv_res = NULL,   sparsity_res = NULL,   n = NULL,   nx = NULL,   ny = NULL,   sparse = FALSE,   keepx = NULL,   keepy = NULL,   ... )"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/so2pls_o2m.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Wrapper for OmicsPLS::o2m function — so2pls_o2m","text":"omicspls_input named list length 2, produced get_input_omicspls. cv_res Named integer vector length 3, names n, nx, ny. obtained so2pls_get_optim_ncomp_adj so2pls_get_optim_ncomp. sparsity_res Named list length 2, names keepx keepy. obtained so2pls_get_optim_keep. n Positive integer, number joint components compute. Ignored cv_res NULL. nx Positive integer, number specific components compute first dataset. Ignored cv_res NULL. ny Positive integer, number specific components compute second dataset. Ignored cv_res NULL. sparse Logical, feature selection performed? Default value FALSE. sparsity_res NULL, set TRUE. keepx Integer integer vector length n, number features first dataset retain joint component. Ignored sparsity_res NULL. keepy Integer integer vector length n, number features second dataset retain joint component. Ignored sparsity_res NULL. ... arguments passed o2m.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/so2pls_o2m.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Wrapper for OmicsPLS::o2m function — so2pls_o2m","text":"list (see o2m).","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/so2pls_plot_cv.html","id":null,"dir":"Reference","previous_headings":"","what":"Plots cross-validation results for sO2PLS — so2pls_plot_cv","title":"Plots cross-validation results for sO2PLS — so2pls_plot_cv","text":"Plots results cross-validation sO2PLS run (OmicsPLS package)","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/so2pls_plot_cv.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plots cross-validation results for sO2PLS — so2pls_plot_cv","text":"","code":"so2pls_plot_cv(cv_res, nb_col = NULL)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/so2pls_plot_cv.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plots cross-validation results for sO2PLS — so2pls_plot_cv","text":"cv_res cvo2m object, output crossval_o2m function. nb_col Integer, number columns use faceted plot. Default value NULL (number columns chosen automatically).","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/so2pls_plot_cv.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plots cross-validation results for sO2PLS — so2pls_plot_cv","text":"ggplot.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/so2pls_plot_cv_adj.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot adjusted cross-validation results for sO2PLS — so2pls_plot_cv_adj","title":"Plot adjusted cross-validation results for sO2PLS — so2pls_plot_cv_adj","text":"Plots results adjusted cross-validation sO2PLS run (OmicsPLS package)","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/so2pls_plot_cv_adj.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot adjusted cross-validation results for sO2PLS — so2pls_plot_cv_adj","text":"","code":"so2pls_plot_cv_adj(cv_res, with_labels = TRUE)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/so2pls_plot_cv_adj.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot adjusted cross-validation results for sO2PLS — so2pls_plot_cv_adj","text":"cv_res Data-frame, output crossval_o2m_adjR2 function. with_labels Boolean, whether optimal values nx ny value n displayed. Default value TRUE.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/so2pls_plot_cv_adj.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot adjusted cross-validation results for sO2PLS — so2pls_plot_cv_adj","text":"ggplot","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/so2pls_plot_cv_sparsity.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot sparsity cross-validation results for sO2PLS — so2pls_plot_cv_sparsity","title":"Plot sparsity cross-validation results for sO2PLS — so2pls_plot_cv_sparsity","text":"Plots results sparsity cross-validation sO2PLS run (OmicsPLS package).","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/so2pls_plot_cv_sparsity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot sparsity cross-validation results for sO2PLS — so2pls_plot_cv_sparsity","text":"","code":"so2pls_plot_cv_sparsity(cv_res)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/so2pls_plot_cv_sparsity.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot sparsity cross-validation results for sO2PLS — so2pls_plot_cv_sparsity","text":"cv_res List, result call so2pls_crossval_sparsity.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/so2pls_plot_cv_sparsity.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot sparsity cross-validation results for sO2PLS — so2pls_plot_cv_sparsity","text":"ggplot.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/so2pls_plot_cv_sparsity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Plot sparsity cross-validation results for sO2PLS — so2pls_plot_cv_sparsity","text":"produced plot one facet joint component. x-axis corresponds number features retained X dataset construct joint component, y-axis number features retained Y dataset construct joint component. colour point ith facet represents average covariance obtained joint ith components two datasets cross-validation folds. size points' shadow correspond covariance standard error across cross-validation folds. joint component, setting yielding maximum average covariance highlighted orange, one yielding highest average covariance 1-SD rule red.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/so2pls_plot_joint_components_coefficients.html","id":null,"dir":"Reference","previous_headings":"","what":"Plots sO2PLS contributions between datasets joint components — so2pls_plot_joint_components_coefficients","title":"Plots sO2PLS contributions between datasets joint components — so2pls_plot_joint_components_coefficients","text":"Plots regression coefficients link joint components two datasets, SO2PLS run (OmicsPLS package).","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/so2pls_plot_joint_components_coefficients.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plots sO2PLS contributions between datasets joint components — so2pls_plot_joint_components_coefficients","text":"","code":"so2pls_plot_joint_components_coefficients(so2pls_res, datasets = NULL)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/so2pls_plot_joint_components_coefficients.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plots sO2PLS contributions between datasets joint components — so2pls_plot_joint_components_coefficients","text":"so2pls_res output o2m function. datasets Optional, character vector names datasets plotted. Default NULL, .e. datasets considered.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/so2pls_plot_joint_components_coefficients.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plots sO2PLS contributions between datasets joint components — so2pls_plot_joint_components_coefficients","text":"patchwork ggplots.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/so2pls_plot_samples_joint_components.html","id":null,"dir":"Reference","previous_headings":"","what":"Plots sO2PLS joint components samples scores — so2pls_plot_samples_joint_components","title":"Plots sO2PLS joint components samples scores — so2pls_plot_samples_joint_components","text":"Plots samples scores average joint components sO2PLS run (OmicsPLS package).","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/so2pls_plot_samples_joint_components.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plots sO2PLS joint components samples scores — so2pls_plot_samples_joint_components","text":"","code":"so2pls_plot_samples_joint_components(so2pls_res, ...)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/so2pls_plot_samples_joint_components.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plots sO2PLS joint components samples scores — so2pls_plot_samples_joint_components","text":"so2pls_res output o2m function. ... arguments passed plot_samples_score().","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/so2pls_plot_samples_joint_components.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plots sO2PLS joint components samples scores — so2pls_plot_samples_joint_components","text":"ggmatrix plot.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/so2pls_plot_samples_specific_components.html","id":null,"dir":"Reference","previous_headings":"","what":"Plots sO2PLS specific components samples scores — so2pls_plot_samples_specific_components","title":"Plots sO2PLS specific components samples scores — so2pls_plot_samples_specific_components","text":"Plots samples scores datasets specific components sO2PLS run (OmicsPLS package).","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/so2pls_plot_samples_specific_components.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plots sO2PLS specific components samples scores — so2pls_plot_samples_specific_components","text":"","code":"so2pls_plot_samples_specific_components(so2pls_res, dataset = NULL, ...)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/so2pls_plot_samples_specific_components.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plots sO2PLS specific components samples scores — so2pls_plot_samples_specific_components","text":"so2pls_res output o2m function. dataset Character, name dataset specific components plotted. Default NULL, .e. specific components datasets plotted. ... arguments passed plot_samples_score().","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/so2pls_plot_samples_specific_components.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plots sO2PLS specific components samples scores — so2pls_plot_samples_specific_components","text":"list ggmatrix plots (one per dataset), one plot dataset used specify dataset.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/so2pls_plot_summary.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot summary of sO2PLS run — so2pls_plot_summary","title":"Plot summary of sO2PLS run — so2pls_plot_summary","text":"Plots summary variation sO2PLS run (OmicsPLS package).","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/so2pls_plot_summary.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot summary of sO2PLS run — so2pls_plot_summary","text":"","code":"so2pls_plot_summary(so2pls_res, datasets = NULL)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/so2pls_plot_summary.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot summary of sO2PLS run — so2pls_plot_summary","text":"so2pls_res output o2m function. datasets Optional, character vector names datasets selected features extracted. Default NULL, .e. datasets considered.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/so2pls_plot_summary.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot summary of sO2PLS run — so2pls_plot_summary","text":"patchwork ggplots.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/so2pls_print_cv_adj.html","id":null,"dir":"Reference","previous_headings":"","what":"Print adjusted cross-validation results for sO2PLS — so2pls_print_cv_adj","title":"Print adjusted cross-validation results for sO2PLS — so2pls_print_cv_adj","text":"Prints results adjusted cross-validation sO2PLS run (OmicsPLS package)","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/so2pls_print_cv_adj.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Print adjusted cross-validation results for sO2PLS — so2pls_print_cv_adj","text":"","code":"so2pls_print_cv_adj(cv_res)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/so2pls_print_cv_adj.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Print adjusted cross-validation results for sO2PLS — so2pls_print_cv_adj","text":"cv_res Data-frame, output crossval_o2m_adjR2 function.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/so2pls_print_cv_adj.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Print adjusted cross-validation results for sO2PLS — so2pls_print_cv_adj","text":"tibble.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/so2pls_print_cv_sparsity.html","id":null,"dir":"Reference","previous_headings":"","what":"Print sparsity cross-validation results for sO2PLS — so2pls_print_cv_sparsity","title":"Print sparsity cross-validation results for sO2PLS — so2pls_print_cv_sparsity","text":"Prints results sparsity cross-validation sO2PLS run (OmicsPLS package)","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/so2pls_print_cv_sparsity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Print sparsity cross-validation results for sO2PLS — so2pls_print_cv_sparsity","text":"","code":"so2pls_print_cv_sparsity(cv_res_optim)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/so2pls_print_cv_sparsity.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Print sparsity cross-validation results for sO2PLS — so2pls_print_cv_sparsity","text":"cv_res_optim Named list, output so2pls_get_optim_keep function.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/so2pls_print_cv_sparsity.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Print sparsity cross-validation results for sO2PLS — so2pls_print_cv_sparsity","text":"tibble, giving dataset (dataset column) joint component (columns) optimal number features retain, well total number features per dataset retain.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/so2pls_screeplot.html","id":null,"dir":"Reference","previous_headings":"","what":"Screeplot sO2PLS run — so2pls_screeplot","title":"Screeplot sO2PLS run — so2pls_screeplot","text":"Plots percentage variation explained latent component sO2PLS (OmicsPLS package).","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/so2pls_screeplot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Screeplot sO2PLS run — so2pls_screeplot","text":"","code":"so2pls_screeplot(so2pls_res, datasets = NULL)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/so2pls_screeplot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Screeplot sO2PLS run — so2pls_screeplot","text":"so2pls_res output o2m function. datasets Optional, character vector names datasets selected features extracted. Default NULL, .e. datasets considered.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/so2pls_screeplot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Screeplot sO2PLS run — so2pls_screeplot","text":"patchwork ggplots.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/so2pls_screeplot.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Screeplot sO2PLS run — so2pls_screeplot","text":"Note plots set possible add custom colour palette get different colours dataset (see example).","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/so2pls_screeplot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Screeplot sO2PLS run — so2pls_screeplot","text":"","code":"if (FALSE) { ## by default, same colour used for both datasets (cannot find a way to fix that cleanly) so2pls_screeplot(so2pls_final_res)  ## Add a colour palette to get different colour for each dataset so2pls_screeplot(so2pls_final_res) &  scale_fill_brewer(palette = \"Set1\", drop = F) }"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/spls_get_optim_ncomp.html","id":null,"dir":"Reference","previous_headings":"","what":"Select the optimal ncomp from sPLS cross-validation results — spls_get_optim_ncomp","title":"Select the optimal ncomp from sPLS cross-validation results — spls_get_optim_ncomp","text":"Select optimal number components compute sPLS run cross-validation results obtained mixOmics::perf() PLS sPLS result, using mean Q2.total values. Note function experimental, corresponding diagnostic plots considered selecting optimal number components use.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/spls_get_optim_ncomp.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Select the optimal ncomp from sPLS cross-validation results — spls_get_optim_ncomp","text":"","code":"spls_get_optim_ncomp(spls_perf, thr = 0.0975, min_ncomp = 1)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/spls_get_optim_ncomp.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Select the optimal ncomp from sPLS cross-validation results — spls_get_optim_ncomp","text":"spls_perf List, result mixOmics::perf(). thr Numeric, threshold used Q2 values. Default value 0.0975. min_ncomp Integer, minimum ncomp value returned. Default value 1, .e. argument play role selecting comp value. Can useful want least 2 latent components final plots.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/spls_get_optim_ncomp.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Select the optimal ncomp from sPLS cross-validation results — spls_get_optim_ncomp","text":"integer, optimal number components use sPLS run.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/spls_get_optim_ncomp.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Select the optimal ncomp from sPLS cross-validation results — spls_get_optim_ncomp","text":"selection made follows: Q2 values threshold specified thr, number components yielding highest Q2 value selected. Q2 values threshold, number components yielding lowest Q2 value selected. Q2 values increasing, number components n selected n+1 smallest number components Q2 value threshold. Q2 values decreasing, number components n selected n+1 smallest number components Q2 value threshold.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/spls_get_params.html","id":null,"dir":"Reference","previous_headings":"","what":"Get parameters from sPLS run — spls_get_params","title":"Get parameters from sPLS run — spls_get_params","text":"Extracts ncomp, keepX keepY parameters sPLS run format table.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/spls_get_params.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get parameters from sPLS run — spls_get_params","text":"","code":"spls_get_params(spls_res)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/spls_get_params.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get parameters from sPLS run — spls_get_params","text":"spls_res output spls spls_run.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/spls_get_params.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get parameters from sPLS run — spls_get_params","text":"tibble.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/spls_get_wa_coord.html","id":null,"dir":"Reference","previous_headings":"","what":"Computes average sample coordinates for sPLS components — spls_get_wa_coord","title":"Computes average sample coordinates for sPLS components — spls_get_wa_coord","text":"Computes average sample coordinates sPLS components across two datasets.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/spls_get_wa_coord.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computes average sample coordinates for sPLS components — spls_get_wa_coord","text":"","code":"spls_get_wa_coord(spls_res)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/spls_get_wa_coord.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computes average sample coordinates for sPLS components — spls_get_wa_coord","text":"spls_res output spls_run() mixOmics::spls() function.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/spls_get_wa_coord.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computes average sample coordinates for sPLS components — spls_get_wa_coord","text":"matrix samples coordinates, samples rows joint components columns.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/spls_plot_tune.html","id":null,"dir":"Reference","previous_headings":"","what":"Displays results of sPLS tuning — spls_plot_tune","title":"Displays results of sPLS tuning — spls_plot_tune","text":"Displays results cross-validation tune number components retain dataset sPLS run. Similar mixOmics::plot.tune.spls().","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/spls_plot_tune.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Displays results of sPLS tuning — spls_plot_tune","text":"","code":"spls_plot_tune(spls_tune_res)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/spls_plot_tune.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Displays results of sPLS tuning — spls_plot_tune","text":"spls_tune_res result spls_tune() mixOmics::tune.spls().","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/spls_plot_tune.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Displays results of sPLS tuning — spls_plot_tune","text":"ggplot.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/spls_plot_tune.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Displays results of sPLS tuning — spls_plot_tune","text":"plot displays correlation RSS latent components obtained corresponding values keepX (x-axis) keepY (y-axis) latent components full model (.e. retains features). colour points shows mean correlation/RSS across cross-validation folds, size points' shadow (gray) represents coefficient variation (COV) correlation/RSS, .e. standard error divided mean. point corresponding optimal value keepX keepY indicating red border.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/spls_plot_var.html","id":null,"dir":"Reference","previous_headings":"","what":"Plots sPLS features correlation circle — spls_plot_var","title":"Plots sPLS features correlation circle — spls_plot_var","text":"Displays sPLS correlation circle plot, uses available feature metadata display feature names.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/spls_plot_var.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plots sPLS features correlation circle — spls_plot_var","text":"","code":"spls_plot_var(   spls_res,   mo_data,   label_cols = \"feature_id\",   truncate = NULL,   ... )"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/spls_plot_var.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plots sPLS features correlation circle — spls_plot_var","text":"spls_res output mixOmics::spls() spls_run(). mo_data MultiDataSet::MultiDataSet object. label_cols Character named list character, giving dataset name column corresponding features metadata use label. one value, used datasets. list, names must correspond names datasets mo_data. Default value feature_id, .e. default ID features used label. truncate Integer, width labels truncated (avoid issues long labels plots). NULL (default value), truncation performed. ... Additional arguments passed mixOmics::plotVar().","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/spls_plot_var.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plots sPLS features correlation circle — spls_plot_var","text":"plot (see mixOmics::plotVar()).","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/spls_plot_var.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plots sPLS features correlation circle — spls_plot_var","text":"","code":"if (FALSE) { # Use the default features ID for the plot spls_plot_var(   spls_final_run,   mo_data,   \"feature_id\",   overlap = FALSE,   cex = c(3, 3),   comp = 1:2 )  # Using a different column from the feature metadata of each omics dataset spls_plot_var(   spls_final_run,   mo_presel_supervised,   c(     \"rnaseq\" = \"Name\",     \"metabolome\" = \"name\"   ),   overlap = FALSE,   cex = c(3, 3),   comp = 1:2 ) }"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/spls_run.html","id":null,"dir":"Reference","previous_headings":"","what":"Run sPLS algorithm — spls_run","title":"Run sPLS algorithm — spls_run","text":"Runs sPLS algorithm (mixOmics::spls()) mixOmics package.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/spls_run.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Run sPLS algorithm — spls_run","text":"","code":"spls_run(spls_input, ...)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/spls_run.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Run sPLS algorithm — spls_run","text":"spls_input mixOmics input object created get_input_spls(). ... Arguments passed mixOmics::spls().","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/spls_run.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Run sPLS algorithm — spls_run","text":"object class mixo.spls (keepX /keepY arguments provided) mix.pls (), see mixOmics::spls() mixOmics::pls().","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/spls_tune.html","id":null,"dir":"Reference","previous_headings":"","what":"Performs cross-validation for mixomics sPLS to select optimal keepX and keepY — spls_tune","title":"Performs cross-validation for mixomics sPLS to select optimal keepX and keepY — spls_tune","text":"Peforms cross-validation assess optimal number features retain dataset sPLS run (implemented mixOmics package).","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/spls_tune.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Performs cross-validation for mixomics sPLS to select optimal keepX and keepY — spls_tune","text":"","code":"spls_tune(spls_input, keepX = NULL, keepY = NULL, cpus = NULL, ...)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/spls_tune.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Performs cross-validation for mixomics sPLS to select optimal keepX and keepY — spls_tune","text":"spls_input mixOmics input object created get_input_mixomics_unsupervised(). keepX Numeric vector, values number features retain dataset X test. Default value NULL (default sequence values used, see details). keepY Numeric vector, values number features retain dataset Y test. Default value NULL (default sequence values used, see details). cpus Integer, number CPUs use running code parallel. advanced users, see BPPARAM argument mixOmics::tune.spls(). ... arguments passed mixOmics::tune.spls().","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/spls_tune.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Performs cross-validation for mixomics sPLS to select optimal keepX and keepY — spls_tune","text":"list (see mixOmics::tune.spls()).","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/spls_tune.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Performs cross-validation for mixomics sPLS to select optimal keepX and keepY — spls_tune","text":"value provided keepX keepY, sequence seq(5, 30, 5) used, truncated retain values inferior equal number features X Y dataset.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/subset_features.html","id":null,"dir":"Reference","previous_headings":"","what":"Subset a MultiDataSet object by feature — subset_features","title":"Subset a MultiDataSet object by feature — subset_features","text":"Subsets MultiDataSet object based list feature IDs provided.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/subset_features.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Subset a MultiDataSet object by feature — subset_features","text":"","code":"subset_features(mo_data, features_id)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/subset_features.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Subset a MultiDataSet object by feature — subset_features","text":"mo_data MultiDataSet::MultiDataSet object. features_id Character vector, vector feature IDs (across datasets) select. Also accepts lists (e.g. list vector feature IDs per dataset).","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/subset_features.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Subset a MultiDataSet object by feature — subset_features","text":"MultiDataSet::MultiDataSet object features specified.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/subset_features.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Subset a MultiDataSet object by feature — subset_features","text":"","code":"if (FALSE) { ## works with a vector of feature IDs: subset_features(mo_data, c(\"featureA\", \"featureB\", \"featureC\"))  ## or with a list of feature IDs (typically one per dataset, but doesn't ## have to be): subset_features(   mo_data,   list(     c(\"omics1_featureA\", \"omics1_featureB\", \"omics1_featureC\"),     c(\"omics2_featureA\", \"omics2_featureB\"),     c(\"omics3_featureA\", \"omics3_featureB\", \"omics3_featureC\"),   ) ) }"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/transform_bestNormalise_auto.html","id":null,"dir":"Reference","previous_headings":"","what":"Applies the bestNormalize function to rows of a matrix — transform_bestNormalise_auto","title":"Applies the bestNormalize function to rows of a matrix — transform_bestNormalise_auto","text":"Applies appropriate normalisation method feature (row) matrix, via bestNormalize function bestNormalize package.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/transform_bestNormalise_auto.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Applies the bestNormalize function to rows of a matrix — transform_bestNormalise_auto","text":"","code":"transform_bestNormalise_auto(mat, return_matrix_only = FALSE, ...)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/transform_bestNormalise_auto.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Applies the bestNormalize function to rows of a matrix — transform_bestNormalise_auto","text":"mat Numeric matrix. return_matrix_only Logical, transformed matrix returned? TRUE, function return matrix. FALSE, function instead returns list transformed data potentially information relevant transformation. Default value FALSE. ... arguments passed bestNormalize function.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/transform_bestNormalise_auto.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Applies the bestNormalize function to rows of a matrix — transform_bestNormalise_auto","text":"Depending return_matrix_only, either matrix transformed data, list following elements: transformed_data: matrix transformed data; info_transformation: named list one element per feature (row), giving details transformation applied feature (see output bestNormalize).","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/transform_bestNormalise_manual.html","id":null,"dir":"Reference","previous_headings":"","what":"Applies a normalisation method from bestNormalize to rows of a matrix — transform_bestNormalise_manual","title":"Applies a normalisation method from bestNormalize to rows of a matrix — transform_bestNormalise_manual","text":"Applies chosen normalisation method feature (row) matrix, via bestNormalize package.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/transform_bestNormalise_manual.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Applies a normalisation method from bestNormalize to rows of a matrix — transform_bestNormalise_manual","text":"","code":"transform_bestNormalise_manual(mat, method, return_matrix_only = FALSE, ...)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/transform_bestNormalise_manual.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Applies a normalisation method from bestNormalize to rows of a matrix — transform_bestNormalise_manual","text":"mat Numeric matrix. method Character, name normalisation method apply. Possible values \"arcsinh_x\", \"boxcox\", \"center_scale\", \"exp_x\", \"log_x\", \"orderNorm\", \"sqrt_x\", \"yeojohnson\". See Details. return_matrix_only Logical, transformed matrix returned? TRUE, function return matrix. FALSE, function instead returns list transformed data potentially information relevant transformation. Default value FALSE. ... arguments passed method function bestNormalize package.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/transform_bestNormalise_manual.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Applies a normalisation method from bestNormalize to rows of a matrix — transform_bestNormalise_manual","text":"Depending return_matrix_only, either matrix transformed data, list following elements: transformed_data: matrix transformed data; info_transformation: named list one element per feature (row), giving details transformation applied feature (see output bestNormalize function corresponding method).","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/transform_bestNormalise_manual.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Applies a normalisation method from bestNormalize to rows of a matrix — transform_bestNormalise_manual","text":"Applies normalisation method implemented bestNormalize package. method argument corresponds function bestNormalize package applied rows matrix. See vignette bestNormalize package information transformations.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/transform_dataset.html","id":null,"dir":"Reference","previous_headings":"","what":"Applies a transformation to a dataset from a MultiDataSet object — transform_dataset","title":"Applies a transformation to a dataset from a MultiDataSet object — transform_dataset","text":"Applies transformation dataset MultiDataSet object. Implemented transformations : Variance Stabilising Normalisation (vsn package), Variance Stabilising Transformation (DESeq2 package - count data), appropriate feature-wise normalisation BestNormalise package.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/transform_dataset.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Applies a transformation to a dataset from a MultiDataSet object — transform_dataset","text":"","code":"transform_dataset(   mo_data,   dataset,   transformation,   return_multidataset = FALSE,   return_matrix_only = FALSE,   verbose = TRUE,   log_base = 2,   pre_log_function = zero_to_half_min,   method,   ... )"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/transform_dataset.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Applies a transformation to a dataset from a MultiDataSet object — transform_dataset","text":"mo_data MultiDataSet-class object. dataset Character, name dataset transform. transformation Character, transformation applied. Possible values : vsn, vst-deseq2, logx best-normalize-auto best-normalize-manual. See Details. return_multidataset Logical, MultiDataSet object original data replaced transformed data returned? FALSE, output function depends return_matrix_only. Default value FALSE. return_matrix_only Logical, transformed matrix returned? TRUE, function return matrix. FALSE, function instead returns list transformed data well information relevant transformation. Ignored return_multidataset TRUE. Default value FALSE. verbose Logical, information transformation printed? Default value TRUE. log_base Numeric, base respect logarithms computed. Default value 2. used transformation = 'logx'. pre_log_function Function applied matrix log transformation (e.g. apply offset values avoid issues zeros). Default value zero_to_half_min() function. used transformation = 'logx'. method Character, transformation = 'best-normalize-manual', normalisation method applied. See possible values transform_bestNormalise_manual(). Ignored transformations. ... arguments passed bestNormalize::bestNormalize() function method function bestNormalize package.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/transform_dataset.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Applies a transformation to a dataset from a MultiDataSet object — transform_dataset","text":"return_multidataset = TRUE: MultiDataSet::MultiDataSet object, original data transformed dataset replaced. return_multidataset = FALSE return_matrix_only = TRUE: matrix transformed data. return_multidataset = FALSE return_matrix_only = FALSE: list two elements, transformed_data containing matrix transformed data, info_transformation containing information transformation (depends transformation applied).","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/transform_dataset.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Applies a transformation to a dataset from a MultiDataSet object — transform_dataset","text":"Currently implemented transformations recommendations based dataset type: vsn: Variance Stabilising normalisation, implemented vsn::justvsn() function vsn package. method originally developed microarray intensities. transformation recommended microarray, metabolome, chemical intensity-based datasets. practice, applies transform_vsn() function. vst-deseq2: Variance Stabilising Transformation, implemented DESeq2::varianceStabilizingTransformation() function DESeq2 package. method applicable count data . transformation recommended RNAseq similar count-based datasets. practice, applies transform_vst() function. logx: log-transformation (default log2, base can specified). practice, applies transform_logx() function. best-normalize-auto: appropriate normalisation method automatically selected number options, implemented bestNormalize::bestNormalize() function bestNormalize package. transformation recommended phenotypes measured different scales (since transformation method selected potentially different across features), preferably reasonable number features (less 100) avoid large computation times. practice, applies transform_bestNormalise_auto() function. best-normalize-manual: performs transformation (specified method argument) feature dataset. transformation recommended phenotypes data different phenotypes measured scale. different normalisation methods : \"arcsinh_x\": data transformed log(x + sqrt(x^2 + 1)); \"boxcox\": Box Cox transformation; \"center_scale\": data centered scaled; \"exp_x\": data transformed exp(x); \"log_x\": data transformed log_b(x+) (b either selected automatically per variable passed arguments); \"orderNorm\": Ordered Quantile technique; \"sqrt_x\": data transformed sqrt(x + ) (selected automatically per variable passed argument), \"yeojohnson\": Yeo-Johnson transformation.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/transform_logx.html","id":null,"dir":"Reference","previous_headings":"","what":"Applies a log-x transformation to matrix — transform_logx","title":"Applies a log-x transformation to matrix — transform_logx","text":"Applies log-x transformation (default log2) log() function.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/transform_logx.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Applies a log-x transformation to matrix — transform_logx","text":"","code":"transform_logx(   mat,   return_matrix_only = FALSE,   log_base = 2,   pre_log_function = zero_to_half_min )"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/transform_logx.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Applies a log-x transformation to matrix — transform_logx","text":"mat Numeric matrix. return_matrix_only Logical, transformed matrix returned? TRUE, function return matrix. FALSE, function instead returns list transformed data potentially information relevant transformation. Default value FALSE. log_base Numeric, base respect logarithms computed. pre_log_function Function applied matrix log transformation (e.g. apply offset values avoid issues zeros). Default value zero_to_half_min() function.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/transform_logx.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Applies a log-x transformation to matrix — transform_logx","text":"Depending return_matrix_only, either matrix transformed data, list following elements: transformed_data: matrix transformed data; info_transformation: list log base used function applied prior log-transformation.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/transform_vsn.html","id":null,"dir":"Reference","previous_headings":"","what":"Applies Variance Stabilising Normalisation (vsn) to matrix — transform_vsn","title":"Applies Variance Stabilising Normalisation (vsn) to matrix — transform_vsn","text":"Applies Variance Stabilising Normalisation performed vsn package via justvsn function.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/transform_vsn.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Applies Variance Stabilising Normalisation (vsn) to matrix — transform_vsn","text":"","code":"transform_vsn(mat, return_matrix_only = FALSE, ...)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/transform_vsn.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Applies Variance Stabilising Normalisation (vsn) to matrix — transform_vsn","text":"mat Numeric matrix. return_matrix_only Logical, transformed matrix returned? TRUE, function return matrix. FALSE, function instead returns list transformed data potentially information relevant transformation. Default value FALSE. ... arguments passed vsn2.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/transform_vsn.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Applies Variance Stabilising Normalisation (vsn) to matrix — transform_vsn","text":"Depending return_matrix_only, either matrix transformed data, list following elements: transformed_data: matrix transformed data; info_transformation: NULL.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/transform_vst.html","id":null,"dir":"Reference","previous_headings":"","what":"Applies Variance Stabilising Transformation (DESeq2) to matrix — transform_vst","title":"Applies Variance Stabilising Transformation (DESeq2) to matrix — transform_vst","text":"Applies Variance Stabilising Transformation (VST) performed DESeq2 package via varianceStabilizingTransformation function. Includes size factor normalisation prior VST. applies matrix count.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/transform_vst.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Applies Variance Stabilising Transformation (DESeq2) to matrix — transform_vst","text":"","code":"transform_vst(mat, return_matrix_only = FALSE)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/transform_vst.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Applies Variance Stabilising Transformation (DESeq2) to matrix — transform_vst","text":"mat Numeric matrix, must contain integers . return_matrix_only Logical, transformed matrix returned? TRUE, function return matrix. FALSE, function instead returns list transformed data potentially information relevant transformation. Default value FALSE.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/transform_vst.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Applies Variance Stabilising Transformation (DESeq2) to matrix — transform_vst","text":"Depending return_matrix_only, either matrix transformed data, list following elements: transformed_data: matrix transformed data; info_transformation: DESeqTransform object, details transformation.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/transformation_datasets_factory.html","id":null,"dir":"Reference","previous_headings":"","what":"Target factory for datasets transformation — transformation_datasets_factory","title":"Target factory for datasets transformation — transformation_datasets_factory","text":"Create list targets apply transformation methods one datasets MultiDataSet object.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/transformation_datasets_factory.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Target factory for datasets transformation — transformation_datasets_factory","text":"","code":"transformation_datasets_factory(   mo_data_target,   transformations,   return_matrix_only = FALSE,   target_name_prefix = \"\",   transformed_data_name = NULL,   log_bases = 2,   pre_log_functions = zero_to_half_min,   methods,   ... )"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/transformation_datasets_factory.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Target factory for datasets transformation — transformation_datasets_factory","text":"mo_data_target Symbol, name target containing MultiDataSet object. transformations Named character vector, name element name dataset transform, corresponding element gives type transformation apply dataset (e.g. c(rnaseq = 'vst-deseq2', phenotypes = 'best-normalize-auto')). See Details list available transformations. 'best-normalize-auto' selected, need provide methods argument well. return_matrix_only Logical, transformed matrix returned transformation? TRUE, transformed matrices stored. FALSE, instead transformation, list transformed data potentially information relevant transformation saved. Default value FALSE. target_name_prefix Character, prefix add name targets created target factory. Default value \"\". transformed_data_name Character, name target containing MultiDataSet transformed data created. NULL, selected automatically. Default value NULL. log_bases Numeric named numeric list, gives dataset 'logx' transformation selected log base use. one value, used concerned datasets. Otherwise, can specify different log-base concerned dataset passing named list. pre_log_functions Function named list functions, gives dataset `'logx`` transformation selected function applied matrix log transformation (e.g. apply offset values avoid issues zeros). Default value zero_to_half_min() function. one value, used concerned datasets. Otherwise, can specify different log-base concerned dataset passing named list. methods Character named character list, gives dataset 'best-normalize-manual' transformation selected normalisation method applied. See possible values Details. one value, used concerned datasets. Otherwise, can specify different method concerned dataset passing named list. ... arguments passed transform_dataset function method function bestNormalize package. relevant 'best-normalize-XX' transformations.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/transformation_datasets_factory.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Target factory for datasets transformation — transformation_datasets_factory","text":"list target objects. target_name_prefix = \"\" transformed_data_name = NULL, following targets created: transformations_spec: generates grouped tibble row corresponds one dataset tranformed, columns specifying dataset name transformation apply. transformations_runs_list: dynamic branching target runs transform_dataset() function dataset. Returns list. transformed_set: target returns MultiDataSet object original data replaced transformed data.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/transformation_datasets_factory.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Target factory for datasets transformation — transformation_datasets_factory","text":"Currently implemented transformations recommendations based dataset type: vsn: Variance Stabilising normalisation, implemented vsn::justvsn() function vsn package. method originally developed microarray intensities. transformation recommended microarray, metabolome, chemical intensity-based datasets. practice, applies transform_vsn() function. vst-deseq2: Variance Stabilising Transformation, implemented DESeq2::varianceStabilizingTransformation() function DESeq2 package. method applicable count data . transformation recommended RNAseq similar count-based datasets. practice, applies transform_vst() function. logx: log-transformation (default log2, base can specified). practice, applies transform_logx() function. best-normalize-auto: appropriate normalisation method automatically selected number options, implemented bestNormalize::bestNormalize() function bestNormalize package. transformation recommended phenotypes measured different scales (since transformation method selected potentially different across features), preferably reasonable number features (less 100) avoid large computation times. practice, applies transform_bestNormalise_auto() function. best-normalize-manual: performs transformation (specified method argument) feature dataset. transformation recommended phenotypes data different phenotypes measured scale. different normalisation methods : \"arcsinh_x\": data transformed log(x + sqrt(x^2 + 1)); \"boxcox\": Box Cox transformation; \"center_scale\": data centered scaled; \"exp_x\": data transformed exp(x); \"log_x\": data transformed log_b(x+) (b either selected automatically per variable passed arguments); \"orderNorm\": Ordered Quantile technique; \"sqrt_x\": data transformed sqrt(x + ) (selected automatically per variable passed argument), \"yeojohnson\": Yeo-Johnson transformation.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/transformation_datasets_factory.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Target factory for datasets transformation — transformation_datasets_factory","text":"","code":"if (FALSE) { ## in the _targets.R library(moiraine)  list(   ## add code here to load the different datasets    ## the following target creates a MultiDataSet object from previously   ## created omics sets (geno_set, trans_set, etc)   tar_target(     mo_set,     create_multiomics_set(geno_set, trans_set, metabo_set, pheno_set)   ),    ## Example 1   transformation_datasets_factory(mo_set,     c(       rnaseq = \"vst-deseq2\",       metabolome = \"vsn\",       phenotypes = \"best-normalize-auto\"     ),     return_matrix_only = FALSE,     transformed_data_name = \"mo_set_transformed\"   ),    ## Example 2 - with a log2 transformation for both datasets   transformation_datasets_factory(     mo_set_complete,     c(       \"rnaseq\" = \"logx\",       \"metabolome\" = \"logx\"     ),     log_bases = 2,     pre_log_functions = zero_to_half_min   ),    ## Example 3 - with different log bases for each dataset and a different   ## preprocessing function to be run before applying the log   transformation_datasets_factory(     mo_set_complete,     c(       \"rnaseq\" = \"logx\",       \"metabolome\" = \"logx\"     ),     log_bases = list(rnaseq = 10, metabolome = 2),     pre_log_functions = list(       rnaseq = \\(x) x + 0.5,       metabolome = zero_to_half_min      )   ) ) }"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/zero_to_half_min.html","id":null,"dir":"Reference","previous_headings":"","what":"Replace zeros with half-min in matrix — zero_to_half_min","title":"Replace zeros with half-min in matrix — zero_to_half_min","text":"Replace zero values matrix half minimum non-null value matrix.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/zero_to_half_min.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Replace zeros with half-min in matrix — zero_to_half_min","text":"","code":"zero_to_half_min(mat)"},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/zero_to_half_min.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Replace zeros with half-min in matrix — zero_to_half_min","text":"mat Numeric matrix.","code":""},{"path":"https://bookish-disco-p832pyq.pages.github.io/reference/zero_to_half_min.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Replace zeros with half-min in matrix — zero_to_half_min","text":"matrix zero values replaced.","code":""}]
diff --git a/sitemap.xml b/sitemap.xml
index 3c271c9..61fcaf0 100644
--- a/sitemap.xml
+++ b/sitemap.xml
@@ -531,6 +531,9 @@
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     <loc>https://bookish-disco-p832pyq.pages.github.io/reference/transform_dataset.html</loc>
   </url>
+  <url>
+    <loc>https://bookish-disco-p832pyq.pages.github.io/reference/transform_logx.html</loc>
+  </url>
   <url>
     <loc>https://bookish-disco-p832pyq.pages.github.io/reference/transform_vsn.html</loc>
   </url>
@@ -540,4 +543,7 @@
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     <loc>https://bookish-disco-p832pyq.pages.github.io/reference/transformation_datasets_factory.html</loc>
   </url>
+  <url>
+    <loc>https://bookish-disco-p832pyq.pages.github.io/reference/zero_to_half_min.html</loc>
+  </url>
 </urlset>