I am going through the very helpful WGCNA tutorials and constructed a co-expression network, but now I am stuck with further steps and interpretation of my modules.
At first some information regarding my data and progress:
- I have 60 samples from humans (assigned to 2 groups a 30 people)
- Microarray data, pre-processed (batch effects, normalization, log2 transformation)
- as input I used ~ 5000 genes, which I previously filtered variance-based
- I decided to use a signed & weighted network (followed step-wise tutorial)
- I ended up with 16 modules
Then, I tried to correlate the eigengene values of the modules with the external trait information. I was insecure if I can use correlation for binary information (my only "trait" information is if the sample is from human with/ without disease). However, I´ve read all posts I was able to find about this and there it was discussed that this is possible and basically a t-test between the groups.
So my code was this: moduleTraitCor = cor(MEs, Anno_180_T, use = "p"); Where I have my eigengenes in MEs and my sample class info in Anno_180_T.
So do you think this approach is ok so far?
My problem is: No single correlation between module eigengenes and the binary trait is significant.
I don´t know what I could do know, even functional enrichment does not really make sense when there is no relationship at all...
My question of interest was if I could find differentially co-expressed modules between disease/healthy persons. And I have additional data from 2 other time points which I thought to maybe analyze with consensus analysis/ eigengene networks...
Thank you for any help/ suggestions!!
PS: I also had a look at CEMiTool, which is somewhat based on WGCNA I think... There they seem to do gene set enrichment analysis for group differences. As far as I understood it mathematically, the difference is that there the expression of the whole module goes in, not only the eigengene. I tried this for my sample and every module was significant... I am really confused.