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
In any analysis that is based on the number of reads mapping to a gene, the longer a gene is the more reads will map to it. 

In DE analysis (such as DESeq and edgeR), this means that genes that a long gene is more likely to be called significantly differential than a short gene as there is less noise. 

I don't know of any literature on how WGCNA might be affected by this, however, one can imagine that where the expression is more accurately estimated (as it is with longer genes) there is a higher chance of the correlation being more significant. So, yes, I might image that you would find that longer genes are more likely to be assigned to clusters. 

Might even be a short paper in it if you could demonstrate it. 
