demo: fix spelling

nbs/99_demo/01-LV_trait_association-basophil_percentage.ipynb

@@ -13,7 +13,7 @@
    "id": "028ec371",
    "metadata": {},
    "source": [
-    "This notebook will show the structure of the main data matrices in PhenoPLIER, and will guide you in analyzing gene associations for a particular trait: basophill percentage, which is presented in the [manuscript](https://greenelab.github.io/phenoplier_manuscript/#phenoplier-an-integration-framework-based-on-gene-co-expression-patterns) in Figure 1c."
+    "This notebook will show the structure of the main data matrices in PhenoPLIER, and will guide you in analyzing gene associations for a particular trait: basophil percentage, which is presented in the [manuscript](https://greenelab.github.io/phenoplier_manuscript/#phenoplier-an-integration-framework-based-on-gene-co-expression-patterns) in Figure 1c."
    ]
   },
   {
@@ -479,8 +479,8 @@
     "PhenomeXcan provides TWAS results (using [Summary-MultiXcan](https://doi.org/10.1371/journal.pgen.1007889) and [Summary-PrediXcan](https://doi.org/10.1038/s41467-018-03621-1)) across ~4,000 traits.\n",
     "If you are interested in PhenomeXcan you can also check out the [Github repo](https://github.com/hakyimlab/phenomexcan) to know how to download results.\n",
     "\n",
-    "For this demo, we'll load a file that contains Summary-MultiXcan (or S-MultiXcan) results for basophill percentage.\n",
-    "This file contains a list of p-values for ~22k genes, where a significant p-value means that the gene's predicted expression (across different tissues) is associated with basophill percentage.\n",
+    "For this demo, we'll load a file that contains Summary-MultiXcan (or S-MultiXcan) results for basophil percentage.\n",
+    "This file contains a list of p-values for ~22k genes, where a significant p-value means that the gene's predicted expression (across different tissues) is associated with basophil percentage.\n",
     "In the notebook I refer to these results generically as \"TWAS results\", meaning that we have gene-trait associations.\n",
     "All these TWAS results were derived solely from GWAS summary stats, so you can also generate yours relatively easily by using [S-MultiXcan](https://doi.org/10.1371/journal.pgen.1007889)."
    ]
@@ -572,7 +572,7 @@
    ],
    "source": [
     "%%bash\n",
-    "# download S-MultiXcan results for basophill percentage\n",
+    "# download S-MultiXcan results for basophil percentage\n",
     "wget https://uchicago.box.com/shared/static/g70nq1c6wjvado242t9yg05jrhvdykrv.gz -O /tmp/smultixcan_30220_raw_ccn30.tsv.gz"
    ]
   },
@@ -808,7 +808,7 @@
    "id": "b16b3391",
    "metadata": {},
    "source": [
-    "# Take a look at genes associated with basophill percentage"
+    "# Take a look at genes associated with basophil percentage"
    ]
   },
   {
@@ -846,7 +846,7 @@
    "id": "554be85b-71ad-4ef0-806a-317d7ce8a20f",
    "metadata": {},
    "source": [
-    "Below I list the top associated genes for basophill percentage."
+    "Below I list the top associated genes for basophil percentage."
    ]
   },
   {
@@ -1586,7 +1586,7 @@
    "source": [
     "They do not seem as significant as those within the top genes in LV603.\n",
     "\n",
-    "If we compute the correlation between LV603 gene weights (`lv603_top_genes`) and gene associations for basophill percentage (`traits_df`) we get this:"
+    "If we compute the correlation between LV603 gene weights (`lv603_top_genes`) and gene associations for basophil percentage (`traits_df`) we get this:"
    ]
   },
   {
@@ -2931,7 +2931,7 @@
    "id": "97fdc4d1-7b6c-4352-b12d-b0778a3e8947",
    "metadata": {},
    "source": [
-    "As you can see, LV603 is at the top of the LVs associations for basophill percentage.\n",
+    "As you can see, LV603 is at the top of the LVs associations for basophil percentage.\n",
     "However, the onesided p-value here (`5.32e-15`) is larger than a simple correlation (`2.94e-27`), suggesting that we have correlated genes at the top of the LV."
    ]
   },

nbs/99_demo/02-LV_cell_types-LV603.ipynb

@@ -13,7 +13,7 @@
    "id": "8310f845",
    "metadata": {},
    "source": [
-    "In the previous notebook, we found that LV603 gene weight's are predictive of gene associations for basophill percentage.\n",
+    "In the previous notebook, we found that LV603 gene weight's are predictive of gene associations for basophil percentage.\n",
     "In a real application, you would run the `gls_cli.py` tool for your trait of interest across all LVs in our models, and get the significant ones. Then you can see in which cell types the LVs' genes are expressed, and this is what we are going to do in this notebook for LV603.\n",
     "\n",
     "To find the cell types associated with an LV, we'll use matrix **B** (see the figure b) below and our [manuscript](https://greenelab.github.io/phenoplier_manuscript/#phenoplier-an-integration-framework-based-on-gene-co-expression-patterns)).\n",

nbs/99_demo/py/01-LV_trait_association-basophil_percentage.py

@@ -18,7 +18,7 @@
 # # Description
 
 # %% [markdown]
-# This notebook will show the structure of the main data matrices in PhenoPLIER, and will guide you in analyzing gene associations for a particular trait: basophill percentage, which is presented in the [manuscript](https://greenelab.github.io/phenoplier_manuscript/#phenoplier-an-integration-framework-based-on-gene-co-expression-patterns) in Figure 1c.
+# This notebook will show the structure of the main data matrices in PhenoPLIER, and will guide you in analyzing gene associations for a particular trait: basophil percentage, which is presented in the [manuscript](https://greenelab.github.io/phenoplier_manuscript/#phenoplier-an-integration-framework-based-on-gene-co-expression-patterns) in Figure 1c.
 
 # %% [markdown]
 # # Modules
@@ -79,13 +79,13 @@
 # PhenomeXcan provides TWAS results (using [Summary-MultiXcan](https://doi.org/10.1371/journal.pgen.1007889) and [Summary-PrediXcan](https://doi.org/10.1038/s41467-018-03621-1)) across ~4,000 traits.
 # If you are interested in PhenomeXcan you can also check out the [Github repo](https://github.com/hakyimlab/phenomexcan) to know how to download results.
 #
-# For this demo, we'll load a file that contains Summary-MultiXcan (or S-MultiXcan) results for basophill percentage.
-# This file contains a list of p-values for ~22k genes, where a significant p-value means that the gene's predicted expression (across different tissues) is associated with basophill percentage.
+# For this demo, we'll load a file that contains Summary-MultiXcan (or S-MultiXcan) results for basophil percentage.
+# This file contains a list of p-values for ~22k genes, where a significant p-value means that the gene's predicted expression (across different tissues) is associated with basophil percentage.
 # In the notebook I refer to these results generically as "TWAS results", meaning that we have gene-trait associations.
 # All these TWAS results were derived solely from GWAS summary stats, so you can also generate yours relatively easily by using [S-MultiXcan](https://doi.org/10.1371/journal.pgen.1007889).
 
 # %% language="bash"
-# # download S-MultiXcan results for basophill percentage
+# # download S-MultiXcan results for basophil percentage
 # wget https://uchicago.box.com/shared/static/g70nq1c6wjvado242t9yg05jrhvdykrv.gz -O /tmp/smultixcan_30220_raw_ccn30.tsv.gz
 
 # %%
@@ -98,7 +98,7 @@
 df.head()
 
 # %% [markdown]
-# # Take a look at genes associated with basophill percentage
+# # Take a look at genes associated with basophil percentage
 
 # %% [markdown]
 # Show the sample size for this trait.
@@ -109,7 +109,7 @@
 display(f"{trait_code} - sample size: {t.n}")
 
 # %% [markdown]
-# Below I list the top associated genes for basophill percentage.
+# Below I list the top associated genes for basophil percentage.
 
 # %%
 traits_df = df[["gene_name", "pvalue"]].dropna().set_index("gene_name")
@@ -185,7 +185,7 @@
 # %% [markdown]
 # They do not seem as significant as those within the top genes in LV603.
 #
-# If we compute the correlation between LV603 gene weights (`lv603_top_genes`) and gene associations for basophill percentage (`traits_df`) we get this:
+# If we compute the correlation between LV603 gene weights (`lv603_top_genes`) and gene associations for basophil percentage (`traits_df`) we get this:
 
 # %%
 lv603_top_genes
@@ -245,7 +245,7 @@
 lv_df
 
 # %% [markdown]
-# As you can see, LV603 is at the top of the LVs associations for basophill percentage.
+# As you can see, LV603 is at the top of the LVs associations for basophil percentage.
 # However, the onesided p-value here (`5.32e-15`) is larger than a simple correlation (`2.94e-27`), suggesting that we have correlated genes at the top of the LV.
 
 # %% [markdown]

nbs/99_demo/py/02-LV_cell_types-LV603.py

@@ -18,7 +18,7 @@
 # # Description
 
 # %% [markdown]
-# In the previous notebook, we found that LV603 gene weight's are predictive of gene associations for basophill percentage.
+# In the previous notebook, we found that LV603 gene weight's are predictive of gene associations for basophil percentage.
 # In a real application, you would run the `gls_cli.py` tool for your trait of interest across all LVs in our models, and get the significant ones. Then you can see in which cell types the LVs' genes are expressed, and this is what we are going to do in this notebook for LV603.
 #
 # To find the cell types associated with an LV, we'll use matrix **B** (see the figure b) below and our [manuscript](https://greenelab.github.io/phenoplier_manuscript/#phenoplier-an-integration-framework-based-on-gene-co-expression-patterns)).