Hello everybody, this is a general question based on a practical problem: can something meaningful be derived from any kind of correlation between two separate WGCNA analyses of the same data (samples)?

To illustrate: we have sequenced small and long RNA of patients and controls (separately from the same blood samples) and performed WGCNA analyses on each of those sets. As a supplement to targeting- and network-level analyses, we were wondering if we can get a meaningful statement from some module specific correlate between the small and long RNA WGCNA modules, e.g., can one compare eigengene vectors (or even more principal components) from the two sets, and which statistical and ranking processes would be appropriate in those cases?

Thanks in advance, Sebastian

Yes, you can correlate module eigengenes from different analyses (across common samples), just as you could correlate the expression levels of single short and long mRNAs. I would not compare subleading principal components without a good rationale to believe that they represent something meaningful and reliable since the subleading components tend to be much less stable than the 1st PC (eigengene).

Standard correlation tests should be applicable. You can always rank directly by the value (or absolute value) of the correlation; for Pearson and biweight mid-correlation, standard Student t-test significance is usually appropriate, with the standard disclaimer that the test assumes normally distributed errors.