# How to assess quality of WGCNA module identification in blockwisemodules()

I'm exploring WGCNA for bulkRNA sequencing analysis with human subjects. I have healthy, myocarditis, and heart failure patients (which can be further broken into ischemic or nonischemic)

I have been spending quite a bit of time tinkering with the parameters in blockwisemodules() to identify modules/MEs, and I'm only able to assess the quality of this step with a rudimentary intuition.

For example, if I use

net = blockwiseModules(datExpr, power = 10,maxBlockSize = 20000,TOMType = "unsigned", minModuleSize = 20,reassignThreshold = 50, mergeCutHeight = 0.35,numericLabels = TRUE, pamStage = TRUE, pamRespectsDendro = TRUE)

I get what is so far the "best" module detection - many modules (although small), and about 60-70% of genes seem to fall into a module. Image below.

I find myself at a bit of a loss as to how to judge the quality of this step. The module detection looks extremely messy compared with the tutorial data, which i wasn't surprised at really, but can anyone provide guidance here?

I can provide other images of what I get from this step that looks like much poorer detection of modules if it would help.

Last related question, I have 19 healthy, and about 41 patient samples, I wasn't sure if the best approach to module detection is to put all samples together, then look at what modules are associated with disease states which i could convert into numeric for that step, or would it be better as i have started - to detect modules in healthy, or patient groups in independent datasets?