Are there generally accepted workflows for strongly ontology/pathway-driven analyses of RNA-seq data?
Specifically, I have a dataset of ~159,000 unique transcripts mapping to ENST identifiers that I've performed differential expression analysis on (healthy tissue vs. diseased). The PI I am currently working with is only interested in a specific class of glycoproteins and known pathways related to his disease process. I had a member of his group generate a list of relevant GO terms for both the glycoproteins and known disease pathways. I parsed this to the level of a unique gene list derived from all the GO terms (via biomaRt) and then used this list to filter my DE transcripts.
Now I have a list of transcripts related to the PI's molecules/disease of interest sorted by p-value as give by DE analysis (using edgeR). Simply stated, I have no idea what to do with this.
Intuition tells me I should attempt to integrate log2 fold change data somehow. The PI has suggested to just dump the top 1,000 ontology-filtered DE genes (by p-value) into the Cytoscape ReactomeFI plugin, run gene set analysis, and call it a day. At best, this seems uninformative and, at worst, a tautology since we've already highly preselected the genes to be used as input.
Has anyone else encountered a similar situation? Are there better ways of analyzing RNA-seq data when there are strong prior assumptions about what genes/transcripts/pathways will be considered?