Hi, I am trying to run WGCNA on RNA seq data and I end up with few modules. I've seen a similar post but unlike those data, mine present a better scale free topology index. I run 3 different databases and always have similar results, I am wondering if I am missing something.

Fitting Index

Here is the code:

adjacency = adjacency(datExpr,
                  type = "signed", 
                  power = 5)


TOM = TOMsimilarity(adjacency,
                TOMType = "signed",
                TOMDenom = "mean",
                suppressTOMForZeroAdjacencies = FALSE,
                verbose = 5)

And the results

The count matrix was created from FASQT files in Galaxy using Bowtie2/HTSeq. Then all samples were normalized using DESeq2 and exported. Further filtering based on counts, row variance and protein coding genes, as well as Log2 transformation were conducted in R. Final number of genes was 14692

Thanks

The only obvious thing is that the power 5 is quite low for a "signed" network. I would raise it to say 10 or use 'signed hybrid" for network type. However, that may not help you get more modules. You should plot a sample clustering tree and/or PCA plot to make sure you don't have a few large sample clusters; if you do, investigate whether they are biologically plausible/interesting or whether they are likely to be technical, and run adjustment if the clusters aren't driven by a variable of interest.