diff --git a/04-advanced-topics/network-analysis_rnaseq_01_wgcna.Rmd b/04-advanced-topics/network-analysis_rnaseq_01_wgcna.Rmd index 6553f6e2..bee42c53 100644 --- 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. + +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). + +For this example data set, we will skip this step (there are no obvious outliers) and proceed. + ## 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 index b81a41c6..b294fb8d 100644 --- a/04-advanced-topics/network-analysis_rnaseq_01_wgcna.html +++ b/04-advanced-topics/network-analysis_rnaseq_01_wgcna.html @@ -3713,6 +3713,7 @@
  • Dimension Reduction - UMAP
  • Pathway Analysis - ORA
  • Pathway Analysis - GSEA
    • +
  • Pathway Analysis - GSVA
  • Ensembl Gene ID Annotation
  • Ortholog Mapping
    • @@ -4028,7 +4029,7 @@

    4.2 Import and set up data

    # Read in metadata TSV file
 metadata <- readr::read_tsv(metadata_file)
    ## 
-## ── Column specification ──────────────────────────────────────────────────────────────────
+## ── Column specification ───────────────────────────────────────────────────────────────────────────────
 ## cols(
 ##   .default = col_character(),
 ##   refinebio_age = col_logical(),
@@ -4048,7 +4049,7 @@ 
    4.2 Import and set up data
    
   # Here we are going to store the gene IDs as rownames so that we can have a numeric matrix to perform calculations on later
   tibble::column_to_rownames("Gene")
    ## 
-## ── Column specification ──────────────────────────────────────────────────────────────────
+## ── Column specification ───────────────────────────────────────────────────────────────────────────────
 ## cols(
 ##   .default = col_double(),
 ##   Gene = col_character()
@@ -4101,6 +4102,7 @@ 
    4.4 Perform DESeq2 normalization
 
    # Normalize and transform the data in the `DESeqDataSet` object using the `vst()`
 # function from the `DESEq2` R package
 dds_norm <- vst(dds)
    
+
    At this point, if your data has any outliers, you should look into removing them as they can affect your WGCNA results. WGCNA’s tutorial has an example of exploring your data for outliers you can reference.
    
 
 
    
 
    4.5 Format normalized data for WGCNA
    
@@ -4249,7 +4251,7 @@ 
    4.11 Let’s make plot of module
 ) +
   ggforce::geom_sina() +
   theme_classic()
    -

    +

    This makes sense! Looks like module 52 has elevated expression post treatment.