moduleMemberCorrelations {ProCoNA} | R Documentation

## moduleMemberCorrelations

### Description

Computes the relation between peptides and eigenvector summaries and also peptides and phenotypes.

### Usage

    moduleMemberCorrelations(pnet, pepdat, phenotypes)

### Arguments

`pnet` | The peptide net object
`pepdat` | The peptide data matrix
`phenotypes` | The matrix of traits

### Value

Matrix of Pearson correlations with peptides in rows.

### Author(s)

David L Gibbs

### Examples

    data(ProCoNA_Data)
    #net1 <- buildProconaNetwork("peptide network", peptideData)
    n <- length(samples(net1))
    phenotypes <- matrix(rnorm(10*n), nrow=60)
    pepcor <- moduleMemberCorrelations(net1, peptideData, phenotypes)

    # To plot the heatmap:
    # moduleCors <- correlationWithPhenotypesHeatMap(net1, phenotypes, modules = 1:5,
    #    plot = NULL, title = "Module-trait relationships", textSize = 0.5)

    #########################################################################
    # quick function to write out the tables for specific modules.
    #moduleData <- function(pepnet, pepcors, module, pepinfo, fileprefix) {
    #  moduleX <- pepnet@peptides[which(pepnet@mergedColors==module)]
    #  moduleInfo <- pepinfo[which(pepinfo$Mass_Tag_ID %in% moduleX),]
    #  moduleCors <- pepcors[which(pepcors$Module==module),]
    #  corname <- paste(fileprefix, "_correlations.csv", sep="")
    #  write.table(moduleCors, file=corname, sep=",", row.names=F)
    #  infoname <- paste(fileprefix, "_peptide_info.csv", sep="")
    #  write.table(moduleInfo, file=infoname, sep=",", row.names=F)
    #}
    ########################################################################

    # WRITE OUT A TABLE WITH THE BELOW FUNCTION CALL  :)#
    # moduleData(peptideNetwork, pepcor, 1, masstagdb, "Module_1")

[Package ProCoNA version 1.18.0 Index]