moduleMemberCorrelations {ProCoNA} | R Documentation |
Computes the relation between peptides and eigenvector summaries and also peptides and phenotypes.
moduleMemberCorrelations(pnet, pepdat, phenotypes)
pnet |
The peptide net object |
pepdat |
The peptide data matrix |
phenotypes |
The matrix of traits |
Matrix of Pearson correlations with peptides in rows.
David L Gibbs
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")