Hello, I'm trying to go through the WGCNA tutorial on mice liver data from https://horvath.genetics.ucla.edu/html/CoexpressionNetwork/Rpackages/WGCNA/Tutorials/.
I understood the concepts from expression matrix -> soft thresholding -> adjacency matrix -> dissTOM -> hclust. This is where I'm starting to get confused: after hclust and I generate the dendrogram using "dynamic tree cut", and it detection a set of modules, I color modules with "dynamicColors". But then the tutorial uses moduleEigengenes() to generate another set of modules (albeit less modules than from hclust()).
My questions is:
does moduleEigengenes() use any information from hclust() generated modules? or is it just another way to generate modules? and you compare that to modules generated by hclust()?? but then I read from a presentation slide (https://edu.isb-sib.ch/pluginfile.php/158/course/section/65/01SIB2016_wgcna.pdf) that moduleEigengenes() merges similar modules... so... does moduleEigengenes() merge similar modules generated by hclust()?? But from the code
MEList = moduleEigengenes(datExpr, colors = dynamicColors)
the only thing moduleEigengenes() takes as input that remotely comes from the hclust() is dynamicColors, doesn't seem to be using modules generated by hclust() at all... am I missing something?
but after moduleEigengenes(), the tutorial hclust() again using "as.dist(MEDiss)" instead of "dissTOM" as was with the first hclust()...
very confused, any insight would be very appreciated thanks!
Ming
Cross-posted on Biioconductor: https://support.bioconductor.org/p/124477/