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!