Section 8 Pathway Analysis
In Section 7, we covered analysis at the individual feature level (protein, peptide, phosphoprotein, etc.). While this is useful, it is not without its own set of shortcomings. For instance, there may be no features that pass the significance threshold after correcting for multiple hypothesis testing. Alternatively, there may be many features that are statistically significant, and interpreting this list can be tedious and “prone to investigator bias toward a hypothesis of interest” (Maleki et al., 2020). Another issue is that differential analysis fails to detect subtle, yet coordinated changes in groups of related features (Subramanian et al., 2005).
In order to address these, and other, issues, pathway analysis instead examines a priori defined gene sets—groups of genes that participate in the same biological pathway, share the same cellular location, etc. In this section, we will explore some common annotation databases, as well as two pathway analysis methods: Over-Representation Analysis (ORA) and Gene Set Enrichment Analysis (GSEA).