I just started to use WGCNA and can not really adapt the tutorials to my case.
I have some single channel microarray data coming from 40 samples: 6 of them are control samples, the other ones are from 10 different treatments (from 3 to 6 samples for each treatment). The treatment is the only difference among the samples - the only "clinical trait" that can be related to the analysis.
My data are log2 transformed, quantile normalized intensity signals from a single channel source, therefore positive and possibly quite large; conversely, the data from the WGCNA samples are from a 2-channels source and are given as the ratio of the mean log10 intensity, i.e. small magnitude and negative signs (GEO accession: GSE2814).
My research question is to create a coexpression network to be used for pathway detection, therefore it would be nice to have one network for each treatment. But since I will perform some other analysis before pathway detection, it is not really a problem if I get one single network using all the samples together. Moreover, according the WGCNA FAQ page (http://labs.genetics.ucla.edu/horvath/CoexpressionNetwork/Rpackages/WGCNA/faq.html) it is not advisable to perform WGCNA with less than 20 samples (that is a much larger number than the samples I have for each treatment).
These are my most pestering question, and possible workarounds:
1) how to pass from a two-channel microarray to a single channel microarray? At first I thought of using logFCs outputted by analysis with limma package, but the already mentioned WGCNA FAQ page warns that differential expression analysis should not be performed before WGCNA.
2) can I treat my samples as if they were coming from a population where the only difference among subjects is the treatment? The treatment would then be used as the only covariate, in fact as a single factor with 10+1 levels (such as it is normally done in regression models).
I am very hesitant about this last point because I fear that only genes that exhibit the very same behavior for all treatments will be clustered together, or merged together in a static way, while I would prefer to highlight clusters according to the treatment (and cannot understand if this is possible with WGCNA).
All in all, do you think WGCNA is the appropriate tool for my kind of question?
Thanks in advance.