Can I use a matrix of Pearson Correlation Coefficients as input into WGCNA?

Question

[maureen.pittman]

Hello,

I am using public expression data to create weighted co-expression networks in WGCNA. One source I was hoping to use (COXPRESdb) has aggregated expression data in the form of Pearson correlation coefficients calculated for all pairwise comparisons of gene-expression values. Would this matrix be valid input into the WGCNA pipeline?

Specifically, I'm not sure if I could use this matrix to choose a soft thresholding power as outlined in the manual, since it is assumed the starting point is a matrix of gene expression across samples. I also don't know whether the adjacency() and TOMsimilarity() functions should be used on a matrix of gene-pair correlation values instead of the sample matrix. Does it make sense to use this kind of processed data in WGCNA?

Thank you,

Maureen

Answer 1

[Lluís R.]

This is a bit old but since is quite viewed I answer it.
You can use it, this is called in the package similarity matrix, to select the soft thresholding power you can use pickSoftThreshold.fromSimilarity, there are similar function to calculate adjacency