I've run WGCNA using the 'signed' approach and 18 for the soft threshold power (determined using the 'Scale independence' and 'Mean connectivity' figures as shown in the tutorial). I am confused with the output modules because many of them include genes with opposing expression profiles (negatively correlated).
I've attached an example here where I have plotted the Z-scores (row-wise) of the genes in one of the modules. You can see that the genes in the top ~2/3 are up-regulated in the right-most samples, while the bottom ~1/3 are down-regulated in those same samples. I thought the 'signed' approach should prevent such clustering from happening. Am I misunderstanding something or do I just have poor clustering?
Any other suggestions for prioritizing transcription factors for downstream experiments? I was hoping to use the 'connectivity' metrics from WGCNA for this.