I'm analyzing microarray data using wgcna. I have 36 paired samples from 18 persons.Each person has two samples, one before a 6 week endurance training program,and one after the training program. We're investigating the exercise effects on gene expression using wgcna. We want to find modules that are differentially expressed before and after the training program（eg: MEs that have high values before exercise and low values after exercise）.
Here is the question, I thought I might have several ways to do it：
1. Split the whole dataset accroding to exercise status into two 18-sample datasets, then perform wgcna to find consensus modules in the two datasets, compute consensus MEs in each dataset, and find which ME is significantly different in the two datasets using paired-t test.
2. Just handle the 36 samples as a whole, and find MEs that is significantly different before and after exercise using paired-t-test.
3. For each person, compute the gene expression ratio( ratio=value before exercise/value after exercise), and use the ratio as wgcna input.
I would like to know: how to best utilize the power of paired design for co-expression analysis? And I want to figure out the flaws and strength of each kind of design above.
For the second design, to handle 36 samples as a whole, I'm wondering if these paired designs would interfere with the computing of correlation coefficients, and thus lead to unreal outcome.