Here we investigated the regulation change of dysfunction of a TF (TF A) and try to construct the gene regulation network related to it in mouse model.
Our experimental design is to take two samples (two mouse as replicates habiting the dysfunction version of TF A, case sample) every two weeks for RNA-seq and the time spanning is 18 weeks (9 time points). Considering the relative long time spanning and the transcriptional changes due to unrelated events such as growing process and tissue development, we also have a control group which is normal mouse (habit the normal version of TF A, control sample) for RNA-seq and take sample for every two weeks as well.
My question is how to rule out the unrelated events and just see the consequence of dysfunction of my TF? My plan is to get fold change ratio (reads counts for one gene in case/reads counts for one gene in control) as indicator. So I get two gene fold change matrixes for each time point. However, my worry is that this kind of value is not suitable for normal network analysis such as WGCNA. Is it OK? Or do you have any suggestions?
Additionally, someone suggested me the following two ways:
Constructing two separate networks (case and control), then we can compare the topological difference between these two networks.
Combing all samples and constructing one network, then we can analyze and identify module and hub genes in case group.
How do you think about these two suggestions?