Question: How to calculate eigengenes on arbitrary expression data, not inside WGCNA
0
gravatar for tospo
14 months ago by
tospo40
United Kingdom
tospo40 wrote:

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!

rna-seq eigengene wgcna rnaseq • 1.4k views
ADD COMMENTlink modified 14 months ago by Jean-Karim Heriche18k • written 14 months ago by tospo40
6
gravatar for Jean-Karim Heriche
14 months ago by
EMBL Heidelberg, Germany
Jean-Karim Heriche18k wrote:

Eigengenes were first defined in this paper. Singular value decomposition (SVD) is what you're looking for. The eigengenes are the right singular vectors of the SVD of the expression matrix. If X is your data with genes as rows and samples as columns, the SVD of X is X=USV' and the eigengenes are defined as the vectors in V. In R,

eigengenes <- svd(X)$v

EDIT: Fixed link to paper.

ADD COMMENTlink modified 14 months ago • written 14 months ago by Jean-Karim Heriche18k

thank you, I will try that!

ADD REPLYlink written 14 months ago by tospo40

BTW: the link to "this paper" doesn't work

ADD REPLYlink written 14 months ago by tospo40
1

Fixed. Sorry about that.

ADD REPLYlink written 14 months ago by Jean-Karim Heriche18k
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