Dear all,

I have the code as this:

Define numbers of genes and samples

 nGenes = ncol(VAX_Expr)
 nSamples = nrow(VAX_Expr)

Recalculate MEs (1st principal component) with color labels

 MEs0 = moduleEigengenes(VAX_Expr, moduleColors)$eigengenes

ME is the correlation matrix of module and sample, by row is sample, by column is module

 MEs = orderMEs(MEs0)

make correlation btween module and trait

 moduleTraitCor = cor(MEs, VAX_Bench, use = "p")
 moduleTraitPvalue = corPvalueStudent(moduleTraitCor, nSamples)

The correlation value btw trait and module can be found in the moduleTraitCor, however, when I plot GS vs MM, the cor and corp is not the same in the moduleTraitCor and moduleTraitPvalue.

Caculate the module membership (GS for each module)

  geneModuleMembership = as.data.frame(cor(VAX_Expr, MEs, use = "p"))
  MMPvalue = as.data.frame(corPvalueStudent(as.matrix(geneModuleMembership), nSamples))

names (colors) of the modules

  modNames = substring(names(MEs), 3)

  names(geneModuleMembership) = paste("MM", modNames, sep="")
  names(MMPvalue) = paste("p.MM", modNames, sep="")

  module = "brown"
  column = match(module, modNames)    
  moduleGenes = moduleColors == module     
  verboseScatterplot(abs(geneModuleMembership[moduleGenes,column]),
                     abs(df_geneTraitGS[moduleGenes,1]),
                     xlab = paste("Module Membership in", module, "module"),
                     ylab = c("Gene significance"),
                     main = paste("Module membership vs. Gene significance\n"),
                     cex.main = 1.2, cex.lab = 1.2, cex.axis = 1.2, 
                     pch = 21, col = "black", bg = module)