I am using WGCNA for the first time to identify gene co-expression modules across a time course with 40+ samples (RNA-Seq). I’ve removed lowly expressed genes and focused the analysis on the 15,000 most dynamically expressed genes. The free-scale topology and other indicators (sample clustering, etc) look good and similar to other example datasets. The network is signed.
When I plot the "representative" merged eigengene module expression I get eigengene profiles for each module that agree well with my biological expectations. Nevertheless, when I look closer to investigate specific genes of interest in any particular module their expression pattern is completely discordant and sometimes entirely opposite to that of the “representative” eigengene.
Is this expected and if it is to what level? What could be causing this discordance? What parameters can I modify to make the coexpression clusters “tighter”? Is that necessary?
I suspect one of the factors that might be resulting is this pattern is eigenmodule merging. I used a merging distance threshold of 0.2 to merge eigenmodules but it might be a bit too strict although I'm not sure if there is a better way to choose this threshold. I am in fact expecting large numbers of modules as the conditions I’m comparing are fairly biologically different.
Find the module dendrogram below, as well as the merging cut-off (in red).
Any insights/pointers/suggestions would be greatly appreciated.