Let's say, I have two classes, control and disease. So far, I have built WGCNA using the full dataset (both classes together) and identified modules that are related to the trait of interest. However, comparing this to WGCNAs built using only data from each class individually, some modules are not well preserved (by qualitative inspection). In the module preservation paper [Langfelder et al. Is My Network Module Preserved and Reproducible?] and related tutorials, module preservation is usually assessed between different datasets, with modules identified using one dataset assessed in another. In my case, it would mean identifying modules in control group, and assessing their preservation in disease group. However, in this way, I have no means of assessing the relevance of the modules to the trait of interest (except to vaguely conclude that certain modules identified in control is poorly preserved in disease group).
Instead, it makes sense to me to assess how the structure/connectivity within modules differ between classes by using module labels identified from the full dataset, and then calculate the stats, e.g. kME using these labels within each class individually and see how they differ. However, I have not seen it done before. I just wonder if my strategy is valid? Any advice is welcomed! Many thanks.