I have gene expression values for TEST and CONTROL samples from RNA-seq. I have determined the differentially expressed genes (DEG). Now I need to prepare a co-expression network of only the DEG. I did this by calculating the Pearson Correlation Coefficient (PCC) values using the corr.test() in R and then using a suitable threshold. I need to clarify a few things here. While calculating PCC should I consider gene expression values from both TEST and CONTROL? Is it logical to build separate networks for TEST and CONTROL?
Yes, it makes sense to construct separate networks for TEST and CONTROL and to then compare these [networks]. In fact, I believe this is preferable to just constructing a network on the entire dataset and then doing post-correlations on modules, as is done in WGCNA.
When comparing the networks, you can look at:
- hub scores for each vertex (gene) in each network
- vertex degrees
- betweenness centrality
- edge strengths between vertex pairs
- module / community memberships