I would like to compare the results from different algorithms for clustering data from an RNAseq experiment. One methodology we used is WGCNA, which represents each cluster/module by a module eigengene.
What I am now looking for is a way to calculate such eigengenes on arbitrary lists of genes and their expression profiles. I want to use that to reduce the clusters generated by other methods to a set of "representative genes" and compare that to the WGCNA output.
Unfortunately, I'm not clear about how to get from a list of genes and their expression profiles to an eigengene. The functions inside the WGCNA R package are deeply tied into the WGCNA analysis and I can't see how to use them on arbitrary data frames of gene expression data. Any hints would be highly welcome!