propVarExplained: Proportion of variance explained by eigengenes.

In WGCNA: Weighted Correlation Network Analysis

Proportion of variance explained by eigengenes.

Description

This function calculates the proportion of variance of genes in each module explained by the respective module eigengene.

Usage

propVarExplained(datExpr, colors, MEs, corFnc = "cor", corOptions = "use = 'p'")

Arguments

datExpr	expression data. A data frame in which columns are genes and rows ar samples. NAs are allowed and will be ignored.
colors	a vector giving module assignment for genes given in datExpr. Unique values should correspond to the names of the eigengenes in MEs.
MEs	a data frame of module eigengenes in which each column is an eigengene and each row corresponds to a sample.
corFnc	character string containing the name of the function to calculate correlation. Suggested functions include "cor" and "bicor".
corOptions	further argument to the correlation function.

Details

For compatibility with other functions, entries in color are matched to a substring of names(MEs) starting at position 3. For example, the entry "turquoise" in colors will be matched to the eigengene named "MEturquoise". The first two characters of the eigengene name are ignored and can be arbitrary.

Value

A vector with one entry per eigengene containing the proportion of variance of the module explained by the eigengene.

Author(s)

Peter Langfelder

See Also

moduleEigengenes

WGCNA documentation built on Sept. 18, 2024, 5:08 p.m.