I have read that is not recommend to use differentially expressed genes as input in WGCNA. However, I have seen some papers where they have use applied WGCNA on DEG.
My question is when is it valid to do this? Can I do this if I did not choose the soft thresholding power by scale-free topology?
Thanks for your reply. I want to use WGCNA to identify the modules with the highest correlation with certain trait, select the genes that belong to such modules and then, perform further analysis. So, is just a way to prioritize some genes from the complete list of differentially expressed genes. Would you consider this an appropriate approach?
That approach sounds fine but, if you just use the differentially expressed genes (DEGs) as input to WGCNA, then it is a 'supervised' / 'biased' approach, but possibly no different than doing hierarchical clustering on DEGs in a supervised way.