Compute community signatures (eigengenes) — compute_eigengene

Computes the community signatures (eigengenes) for an expression matrix given a particular community membership (kME) matrix

Usage

compute_eigengene_matrix(ex, membership_matrix, cutoff = 5, pc_flag = TRUE)

Arguments

ex: matrix of bulk RNA-seq or microarray gene expression data. This should be in log space and greater than or equal to 0.
membership_matrix: a community membership (kME) matrix with genes as rows and communities as columns. Often community_membership or full_community_membership output from icwgcna()
cutoff: number of top genes to use when computing community signatures
pc_flag: indicator. TRUE (default) means to use the 1st principal component (corrected for direction). FALSE uses the mean of scaled and centered top genes.

Value

A matrix with rows being the community signature and columns being samples

Details

This can be used to compute community signatures in a new expression dataset. Note, community signatures are not corrected by icwgcna() iterations so it will not match signatures output from icwgcna() if it is run on the network construction dataset.

When using these community signatures for modeling it may be best to include interaction terms or use tree based methods since dependencies are not addressed in this output matrix.

Examples

if (FALSE) { # \dontrun{
library("UCSCXenaTools")
luad <- getTCGAdata(
  project = "LUAD", mRNASeq = TRUE, mRNASeqType = "normalized",
  clinical = FALSE, download = TRUE
)
ex <- as.matrix(data.table::fread(luad$destfiles), rownames = 1)

results <- icwgcna(ex)

compute_eigengene_matrix(ex, results$community_membership)
} # }