I have microarray gene expression data from healthy mouses. 4 Timepoints 6 animals each. My aim is to study the development of the tissue and finding marker genes and key players in this process.
I normalized the data using GCRMA and found about 8800 differentially expressed genes by applying fold change and SAM (Fold change cut off:2, q<0,01).
Now I would like to perform WGCNA, but I am completly overstrained and donīt know how to begin. I already did the tutorials on WGCNA, but I think they all not really fit to my problem or my data.
So my question to you, has anyone a good suggestion or hint for my how to plan my WGCNA?
I read in one of the tutorials that Langfelder et al. used Foldchanges as a start in doing the Pearson correlation. (In his training data, he has really small values from <1 to >-1, not how I think fold change data would look like).
And if I should use fold changes should I assume day 0 as a reverence and do the correlation on the other 3 timepoints singulary? Or do I find my networks in providing the computer all my timepoints at once (or maybe just the mean of the samples) and R calculates the different modules on basis of the expression profile on the different time points.
So, i hope anyone could follow me with that. I would be so glad if someone could give me a hint in the right direction or can at least provide me with a good source to find a answer.
Thanks a lot