I have 6 folders. Each one contains 7 datasets of a specific type of cancer (RNA-Seq) and 7 datasets of normal tissue (healthy control for that type of cancer). A total of 84 datasets.
I want to investigate my gene of interest in each type of cancer.
I believe the best strategy is to merge the FPKM columns and perform correlation analyses of my gene of interest (GOI) versus all other genes in both cancer and normal tissues. As a result, I will have 14 correlation tables, one for each type of cancer and one for each control tissue. Then, I can investigate the pathways related to the genes most correlated with my GOI in each type of cancer and write my biological interpretation.
Am I in the right direction? Is this a good strategy to answer to my question?
I could also compare the control and cancer samples to investigate the significant DEGs (fold change > 1) and the pathways activated by these DEGs, but my gene of interest doesn't have a strong pattern of gene expression, so I'm afraid it won't be in the list of statistically significant DEGs, that's why I'm preferring the correlation analysis approach.
Any other idea?
The controls were collected from the same patient and processed at the same time. I have the gene counts and can use EdgeR, but what should I do if my GOI doesn't appear among the statistically significant DEGs (fold change > 1)? How can I investigate the role of one single gene in a RNA-Seq dataset if it is not differentially expressed? It can have a role in cancer, even if it is not an important DEG. That's why I thought correlation analyses could give me interesting information.