I am setting up a weighted correlation network analysis (WCNA) in order to identify modules of genes that are co-expressed and correlated or anti-correlated with several quantitative traits. For some traits I have quantifications for the same samples that were profiled (RNAseq and proteomics) but for some other traits we did not have enough tissue to perform both molecular profiling and biochemical determinations from the same animal. Instead, we have repeated measurements in animals of the same genotype than the ones that were profiled.
Would it be possible to assign the mean value per each phenotypic group to the corresponding individual samples? I understand that we would have reduced power to detect significant correlations because we would be unable to model interindividual variability... but I really think it is worth trying. I think that, if present, the correlations should be informative.
An alternative would be averaging replicates and assigning each group the mean value for the different phenotypic traits... but we would be reducing a lot the sample size and we would be then underpowered to detect co-expression modules...
I know it is not the perfect scenario but still, do you think the results would be meaningful?
@Peter Langfelder I would really appreciate your feedback.