WGCNA Part 4: Warn about Outliers (#364)

* Put in basic changes: navbar, dict, snakefile, Rmd * More polishing and info and refs * Update file paths * Bring back docker changes * Add to dictionary * Add a couple refs * Add next steps * Add some polishing and refs * Address the straightforward items from cbethell 's review * Incorporate jashapiro review from #358 * Style Rmds * Bring over part1 changes and re-render * Add last set of steps * Push this partcular plot version in case we wanna come back to it * Commit this multiple module pheatmap in case I want to return to it * ComplexHeatmap is mostly wrangled * It's working! * Save to PDFs * Fix color function and re-render * Add outlier thing * Style Rmds * Re-rendered html * switch the whole outlier thing to just a comment * re-render after staging merge Co-authored-by: GitHub Actions <[email protected]>
AlexsLemonade · Dec 1, 2020 · 7f84383 · 7f84383
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diff --git a/04-advanced-topics/network-analysis_rnaseq_01_wgcna.Rmd b/04-advanced-topics/network-analysis_rnaseq_01_wgcna.Rmd
@@ -335,6 +335,12 @@ For more information about these transformation methods, [see here](https://alex
 dds_norm <- vst(dds)
 ```
 
+At this point, if your data set has any outlier samples, you should look into removing them as they can affect your WGCNA results. 
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+WGCNA's tutorial has [an example of exploring your data for outliers you can reference](https://horvath.genetics.ucla.edu/html/CoexpressionNetwork/Rpackages/WGCNA/Tutorials/FemaleLiver-01-dataInput.pdf).
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+For this example data set, we will skip this step (there are no obvious outliers) and proceed.
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 ## Format normalized data for WGCNA
 
 Extract the normalized counts to a matrix and transpose it so we can pass it to WGCNA. 

diff --git a/04-advanced-topics/network-analysis_rnaseq_01_wgcna.html b/04-advanced-topics/network-analysis_rnaseq_01_wgcna.html