I’m doing the meta-analysis on bunch of microarray data. There are from patients, with several time points before and after treatment. Actually, I’m interested in the post-treatment data (e.g. Day7), I wonder if reasonable only use normalized Day7 data to conduct the network construction, or in order to get rid of individual variances, should I process the data like Day7/Day0 and then use the processed data to do the WGCNA.
The other question is, it’s meta-analysis, I collect the data from serval different diseases but similar, from disease A, maybe have 8 dataset included in the analysis, but for another diseases, just have 1-4 datasets included. I thought, it may cause the bias to disease A (if I’m lucky to get the very conservative module), how can I consider the bias or include data set weight power to correct the bias?
The similar question is, each data set owns different sample sizes, should I and how to consider the weight of each data set?