Dear all,

I was recently asked in a lab meeting whether there may be a gene length bias in the results produced by WGCNA, if the input data consists of normalized counts or variance-stabilized transformed data from DESeq2 (which does not correct for gene length). I am not sure how to answer this, as [there are several papers that used DESeq2 normalized counts as input for WGCNA][1], and I thought that this was actually [recommended by the authors of WGCNA][2].
 
In other words, could my detected expression modules be biased for gene length, with some modules being particularly driven by the length of the genes in that module as opposed to its actual expression?


  [1]: https://scholar.google.com/scholar?hl=en&as_sdt=0%2C9&q=deseq2%20wgcna&btnG=
  [2]: https://horvath.genetics.ucla.edu/html/CoexpressionNetwork/Rpackages/WGCNA/faq.html
Like DEG analysis, WGCNA is looking at each gene independently of one another. All that matters is how the expression of the same gene compares across samples. That comparison will be proportionally the same whether you normalize by gene length or not, because all the values within each comparison are from the same gene, so they have the same length. The modules are formed by finding genes that have the same patterns of expression across samples, not by comparing actual gene expression values between different genes.