The cor and cor p value in the verboseScatterplot don't match with cor() and corPvalueStudent() results. The codes as below:

  geneTraitSignificance = as.data.frame(cor(VAX_Expr, sel_trait, use = "p"))
  GSPvalue = as.data.frame(corPvalueStudent(as.matrix(geneTraitSignificance), nSamples))

  verboseScatterplot(abs(geneModuleMembership[moduleGenes,column]),
                         abs(df_geneTraitGS[moduleGenes,i]),
                         xlab = paste("Module Membership in", module, "module"),
                         ylab = paste("Gene significance for",colnames(df_geneTraitGS)[i]),
                         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)

verboseScatterplot calculates the p-value of the correlation with corPvalueStudent. corPvalueStudent requires the right number of samples to calculate the correct p-value, is a bit more complex than just the input number of samples.  However the function corAndPvalue calculates the p-value and the correlation correctly using the right number of samples to calculate the p-value.