Hi, I'm trying to start a project based on R where I input cancer patient data to find DEG's to ultimately search for possible pharmaceutical targets. My focus is can I can input the same data into GSEA and DEG to confirm each other's conclusions. Right now, I'm only using DEG (voom+limma package in R) to filter/select significant genes.
I know that these two analyses are completely different- GSEA takes in a priori gene sets and gives information relevant to significant gene SETS for each phenotype. DEG will look into individual GENES (not gene sets) and gives us a list of differentially expressed genes for each phenotype.
However, I was wondering if these can work together in harmony so that we can first use GSEA to filter significant gene sets and then use DEG to test individual genes significantly enriched in those gene sets of GSEA. I thought this would help because just performing DEG inherently lacks biological significance. But while GSEA has biological significance, it doesn't have the ability to detect at the level of individual genes. So why not make them work together to complement each other's strengths/weaknesses?
For example, I would run GSEA for two different cancer types (phenotype) A and B, and find gene set X is overexpressed. Then I would look into which group of individual genes are contributing the most to the enrichment score for gene set X. Then I would run a DEG analysis of those individual genes. If I find some genes that are significantly overexpressed for specific types of cancers, that actually itself can be a probable target.
I also do recognize the difficulty of running this together- there are so many different packages that have different methods (ex. normalization methods, etc). But putting these problems aside, I'm just asking that if I could get this right, would this be a good idea?
Thank you for your input :)