Merge pull request #791 from AlexsLemonade/jaclyn-taroni/779-review-p…

…athway Review pathway analysis material for 2024 August
AlexsLemonade · Aug 7, 2024 · 155c784 · 155c784
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diff --git a/RNA-seq/setup/magic-files.md b/RNA-seq/setup/magic-files.md
@@ -0,0 +1,15 @@
+# Cooking show magic
+
+To allow participants who might get bogged down to quickly recover and continue with later notebook sections or exercises, we like to keep a set of output files on hand that they can quickly access as needed.
+This document is for tracking where these files are created and stored.
+
+In general, the files for this module will be stored in `/shared/data/training-data/<date>/RNA-seq/`, and should be created before each training using the latest versions of the referenced notebooks.
+The sub-path for each file should be the same as that for `training-modules/RNA-seq`.
+
+Files are listed below by the notebook that produces them:
+
+- `setup/ref_notebooks/04-nb_cell_line_tximeta.Rmd` (participants create their own notebook for this step)
+  - `data/NB-cell/txi/NB-cell_tximeta.rds`
+- `05-nb_cell_line_DESeq2.Rmd`
+  - `results/NB-cell/NB-cell_DESeq_amplified_v_nonamplified.rds`
+  - `results/NB-cell/NB-cell_DESeq_amplified_v_nonamplified_results.tsv`
diff --git a/pathway-analysis/02-gene_set_enrichment_analysis.Rmd b/pathway-analysis/02-gene_set_enrichment_analysis.Rmd
@@ -132,8 +132,9 @@ hs_hallmark_df <- msigdbr(species = "Homo sapiens",
 # Read in the DGE results
 dge_results_df <- readr::read_tsv(dge_results_file)
 ```
+Let's take a peek at the contents of this file.
 
-```{r}
+```{r head_dge, live = TRUE}
 head(dge_results_df)
 ```
 

diff --git a/pathway-analysis/02-gene_set_enrichment_analysis.nb.html b/pathway-analysis/02-gene_set_enrichment_analysis.nb.html
diff --git a/pathway-analysis/setup/README.md b/pathway-analysis/setup/README.md
@@ -6,6 +6,9 @@ This document provides background for preparing data for use in the guided noteb
 
 ⚠️   **The setup of this module relies on data or results prepared as part of other modules.**
 
+For the GSEA notebook, we use differential expression results from the neuroblastoma cell line data generated in `RNA-seq/05-nb_cell_line_DESeq2.Rmd`.
+The DGE notebook relies on the output of `tximeta`, which can be generated by running `RNA-seq/setup/ref_notebooks/04-nb_cell_line_tximeta.Rmd` after RNA-seq files have been linked appropriately.
+
 For the GSVA notebook, we use a subset of the OpenPBTA stranded dataset (medulloblastoma samples) that we download and process in `machine-learning/setup`. It also depends on a step, performed in `machine-learning/03-openpbta_PLIER.Rmd`, where duplicate gene symbols are collapsed to the row that has the highest mean value.
 
 ### Data Preparation