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uiPCASidebars.R
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uiPCASidebars.R
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#'
#' UI definition for the sidebars
#'
library(shiny)
library(shinyBS)
source("uiHelper.R")
source("widgetInPlaceHelp.R")
source("widgetGeneAnnotationKeywordFilter.R")
source("widgetVisualsEditor.R")
source("widgetGeneralPlotSettings.R")
source("widgetGeneAnnotationImporter.R")
source("widgetSampleAnnotationImporter.R")
source("widgetNumericRangeInput.R")
source("widgetExtendedSliderInput.R")
source("widgetInPlaceHelp.R")
source("widgetReadCountPreprocessing.R")
source("widgetReadCountNormalization.R")
source("plotSamplePlot.R")
source("plotGeneVariancePlot.R")
source("plotConditionsVennDiagramPlot.R")
source("plotPCAVariancePlot.R")
source("plotGeneVarianceRangePlot.R")
source("plotAgglomerativeClusteringPlot.R")
source("widgetRelevantGenes.R")
source("widgetReadCountPostprocessing.R")
source("defaultParameters.R")
source("plotLoadingsPlot.R")
#' Creates UI definition for the "data" sidebar
#' This sidebar allows the user to upload necessary data and transform them for later processing
#'
#' @return
#' @export
#'
#' @examples
uiPCASidebarData <- function() {
return(bsCollapse(
id = "sidebardata",
multiple = F,
bsCollapsePanel(requiredDataText("Import read counts"),
value = "data.readcounts.import",
genericImporterInput("pca.data.readcounts.importer")),
bsCollapsePanel(requiredDataText("Import samples annotation"),
value = "data.sample.annotation",
sampleAnnotationImporterUI("data.sample.annotation.importer")),
bsCollapsePanel(optionalDataText("Import gene annotation"),
value = "data.gene.annotation",
geneAnnotationImporterUI("data.gene.annotation.importer")),
bsCollapsePanel(recommendedDataText("Read count processing"),
value = "data.readcounts.processing",
readCountPreprocessingUI("data.readcounts.preprocessing"),
readCountNormalizationUI("data.readcounts.normalization"),
readCountPostprocessingUI("data.readcounts.postprocessing"))
)
)
}
#' Sidebar for filtering of genes based on various criteria
#'
#' @return
#' @export
#'
#' @examples
uiPCASidebarFilterGenes <- function() {
return(bsCollapse(
bsCollapsePanel(optionalDataText("by gene annotation"),
value = "pca.filter.bygenes",
geneAnnotationKeywordFilterInput("pca.pca.genes.set", helpIconText("Limit set of genes", includeText("helptooltips/pca-pca-gene-set.md"))),
hDivider(),
textOutput("pca.pca.genes.set.count")),
bsCollapsePanel(optionalDataText("by gene variance"),
value = "pca.filter.byvariance",
plotGeneVarianceRangePlotUI("pca.pca.genes.count.variance.plot", height = "120px"),
extendedSliderInput("pca.genes.count", "Gene variance cut-off"),
bsCollapse(
bsCollapsePanel(helpIconText("Find minimal threshold",
includeText("helptooltips/pca-pca-gene-set-variance-minimum.md")),
value = "Find minimal threshold",
relevantGenesUI("pca.pca.genes.count.findminimal"))
)
),
id = "sidebarfilter"))
}
#' Creates definition for the "PCA" sidebar
#' This sidebar allows the user to change parameters related to the PCA
#' Those parameters include prcomp() parameters and the selection of genes to analyze
#'
#' @return Shiny UI element
#' @export
#'
#' @examples
uiPCASidebarPCA <- function() {
return(bsCollapse(
bsCollapsePanel(recommendedDataText("Data processing"),
value = "pca.pca.dataprocessing",
checkboxInput("pca.pca.settings.center",
helpIconText("Center data", includeMarkdown("helptooltips/pca-pca-settings-center.md")),
value = default.pca.settings.centering),
checkboxInput("pca.pca.settings.scale",
helpIconText("Scale data", includeMarkdown("helptooltips/pca-pca-settings-scale.md")),
value = default.pca.settings.scaling)
),
bsCollapsePanel(optionalDataText("Output transformations"),
value = "pca.pca.outputtransformations",
radioButtons("pca.pca.settings.relative",
helpIconText("Relative sample positions", includeMarkdown("helptooltips/pca-pca-settings-relative.md")),
choices = c("None" = "none", "Per dimension" = "dimension", "Global" = "global"),
selected = default.pca.settings.relative))
))
}
#' Creates definition for the "PCA" sidebar
#' This sidebar allows users to change settings of the currently viewed plot
#' This includes general output settings (DPI, width, height, ...) als well
#' as plot-specific settings such as visible axes or data visualization
#'
#' @return
#' @export
#'
#' @examples
uiPCASidebarPlot <- function() {
return(verticalLayout(
conditionalPanel("input['pca.nav'] == 'readcounts.processed'", plotAgglomerativeClusteringPlotSettingsUI("readcounts.processed.hclust.plot")),
conditionalPanel("input['pca.nav'] == 'readcounts.filtered'", plotAgglomerativeClusteringPlotSettingsUI("readcounts.filtered.hclust.plot")),
conditionalPanel("input['pca.nav'] == 'readcounts.top.variant'", plotAgglomerativeClusteringPlotSettingsUI("readcounts.top.variant.hclust.plot")),
# Clustering based on PCA
conditionalPanel("input['pca.nav'] == 'pca.samples.transformed'", plotAgglomerativeClusteringPlotSettingsUI("pca.transformed.hclust.plot")),
# Sample plot
conditionalPanel("input['pca.nav'] == 'pca.samples.plot'", plotSamplePlotSettingsUI("pca.samples.plot")),
# Sample conditions venn diagram plot
conditionalPanel("input['pca.nav'] == 'samples.conditions'", plotConditionsVennDiagramPlotSettingsUI("samples.conditions.plot")),
# Gene variances plot
conditionalPanel("input['pca.nav'] == 'genes.variances'", plotGeneVariancePlotSettingsUI("genes.variances.plot")),
# Gene variances plot (filtered genes)
conditionalPanel("input['pca.nav'] == 'genes.variances.filtered'", plotGeneVariancePlotSettingsUI("genes.variances.filtered.plot")),
# PCA PC variances
conditionalPanel("input['pca.nav'] == 'pca.pc.importance'", plotPCAVariancePlotSettingsUI("pca.variance.plot")),
# PCA PC loadings
conditionalPanel("input['pca.nav'] == 'pca.pc.pc'", plotLoadingsPlotSettingsUI("pca.loadings.plot"))
))
}