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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.

enter image description here

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:

enter image description here

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?

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If you can run pathway analysis programmatically on all modules, yes, do it on all modules. Otherwise, select the modules with strongest associations with each subtype, both positive and negative. I would keep the modules separate because they may have very different biological functions.

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  • $\begingroup$ Thanks for the answer. One small question. Lets say from the above image, for Subtype A I have selected modules salmon, grey60, tan (negative), and midnightblue (positive) for pathway analysis. I see that salmon module is also significantly positive with Subtype C. So, then the pathways I get from salmon module are related to which subtype? $\endgroup$
    – stack_learner
    Jun 9, 2020 at 17:19
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    $\begingroup$ Well, I don't see the figure anymore so I can only go by your description, but if salmon module is negatively correlated with A, it means (most of) the genes in the module are downregulated in subtype A and upregulated in (positively associated with) subtype C. $\endgroup$
    – Peter Langfelder
    Jun 10, 2020 at 18:54
  • $\begingroup$ thanq. sorry for the heatmap with correlation values modules and trait information. Have some doubt whether here MEs0 = moduleEigengenes(datExpr, ?)$eigengenes I have to use dynamicColors which is before merging or moduleColors which is after merging. In the above image I used dynamicColors. 2) there are few modules like turquoise and blue with more than 8000 and 5000 genes. The number is huge. How do I reduce the number? $\endgroup$
    – stack_learner
    Jun 12, 2020 at 12:56
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    $\begingroup$ You can use either merged or un-merged colors; I would suggest using the merged ones. $\endgroup$
    – Peter Langfelder
    Jun 13, 2020 at 22:58
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    $\begingroup$ Some modules are often very large which just reflects a factor (driving expression or co-expression) that strongly affects a lot of genes. This could be biological or technical, and could be important to you or a nuisance confounder. You could try to identify the factor e.g. from sample clustering (could be a batch effect) or by relating the MEs of largest modules to known sample characteristics (age, sex, race etc). If you identify a match, adjust the data for it, or simply adjust the data for the ME and run WGCNA again. $\endgroup$
    – Peter Langfelder
    Jun 13, 2020 at 22:59

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