R: Summary correlation preservation measure

setCorrelationPreservation {WGCNA}

R Documentation

Summary correlation preservation measure

Description

Given consensus eigengenes, the function calculates the average correlation preservation pair-wise for all pairs of sets.

Usage

setCorrelationPreservation(
   multiME, 
   setLabels, 
   excludeGrey = TRUE, greyLabel = "grey", 
   method = "absolute")

Arguments

`multiME`	consensus module eigengenes in a multi-set format. A vector of lists with one list corresponding to each set. Each list must contain a component `data` that is a data frame whose columns are consensus module eigengenes.
`setLabels`	names to be used for the sets represented in `multiME`.
`excludeGrey`	logical: exclude the 'grey' eigengene from preservation measure?
`greyLabel`	module label corresponding to the 'grey' module. Usually this will be the character string `"grey"` if the labels are colors, and the number 0 if the labels are numeric.
`method`	character string giving the correlation preservation measure to use. Recognized values are (unique abbreviations of) `"absolute"`, `"hyperbolic"`.

Details

For each pair of sets, the function calculates the average preservation of correlation among the eigengenes. Two preservation measures are available, the abosolute preservation (high if the two correlations are similar and low if they are different), and the hyperbolically scaled preservation, which de-emphasizes preservation of low correlation values.

Value

A data frame with each row and column corresponding to a set given in multiME, containing the pairwise average correlation preservation values. Names and rownames are set to entries of setLabels.

Author(s)

Peter Langfelder

References

Langfelder P, Horvath S (2007) Eigengene networks for studying the relationships between co-expression modules. BMC Systems Biology 2007, 1:54