I have a total of 35 tumor samples classified into 4 subtypes. Subtype A, B, C, and D. I have RNAseq data. I'm interested in identifying modules related to each subtype with co-expression network analysis. I have read the WGCNA tutorial and create a trait.csv
file like below:
Showing here some rows of the trait.csv
file.
SamInfo <- read.csv("trait.csv")
Samples = rownames(datExpr);
traitRows = match(Samples, SamInfo$Samples);
datTraits = SamInfo[traitRows, -1];
rownames(datTraits) = SamInfo[traitRows, 1];
nGenes = ncol(datExpr);
#15648L
nSamples = nrow(datExpr);
#35L
#calculate eigengenes (1st principal component) of modules
MEs0 = moduleEigengenes(datExpr, dynamicColors)$eigengenes
MEs = orderMEs(MEs0)
moduleTraitCor = cor(MEs, datTraits, use = "p");
moduleTraitPvalue = corPvalueStudent(moduleTraitCor, nSamples);
#Will display correlations and their p-values
textMatrix = paste(signif(moduleTraitCor, 2), "\n(", signif(moduleTraitPvalue, 1), ")",
sep = "");
dim(textMatrix) = dim(moduleTraitCor)
par(mar = c(6, 8.5, 3, 3));
#Display the correlation values within a heatmap plot
labeledHeatmap(Matrix = moduleTraitCor, xLabels = names(datTraits),
yLabels = names(MEs), ySymbols = names(MEs), colorLabels = FALSE,
colors = blueWhiteRed(50), textMatrix = textMatrix, setStdMargins = FALSE,
cex.text = 0.5, zlim = c(-1,1), main = paste("Module-trait relationships"))
The Correlation between Subtypes and Modules Heatmap looks like below:
For functions identification (Pathway Analysis), from the above output for each subtype, should I select only the modules that were significantly positively correlated with subtypes, or Can I also use the significantly negatively correlated modules?
Is it good to use the modules that were not significant for pathway analysis?
In case if I can use positive and negative correlated modules, Can I combine those modules and use those genes for Pathway analysis?