What is the purpose of Gene Ontology (GO) enrichment analysis in the context of a differential expression experiment?
ATo identify which genes are most highly expressed
BTo determine the chromosomal location of differentially expressed genes
CTo identify biological processes, molecular functions, or cellular components that are over-represented among differentially expressed genes
DTo predict the three-dimensional structure of the proteins encoded by differentially expressed genes
GO enrichment tests whether genes associated with a particular biological function appear more often in your differentially expressed gene list than expected by chance. If 30 out of 500 DE genes are involved in 'inflammatory response' but only 5 would be expected by chance from a genome of that size, the enrichment is statistically significant. This transforms an uninterpretable list of hundreds of gene names into a concise summary of which biological processes are affected, providing biological meaning to the statistical results.
Question 2 True / False
Combining transcriptomics and proteomics data always shows the same biological signal because proteins are translated from mRNA.
TTrue
FFalse
Answer: False
Transcript and protein levels correlate only moderately (r ~ 0.4-0.6) because post-transcriptional regulation, translation efficiency, and protein stability create divergence. A gene may be transcriptionally upregulated but its protein rapidly degraded, or an mRNA may be translationally repressed. Integrating both layers reveals biology that neither alone captures: transcriptomics shows regulatory intent, proteomics shows functional outcome, and the discrepancies between them reveal post-transcriptional regulatory mechanisms.
Question 3 Short Answer
Explain why network-based analysis can identify important biology that single-gene analysis misses.
Think about your answer, then reveal below.
Model answer: Many biological processes involve coordinated changes across multiple interacting genes, each with individually modest effects that may not reach statistical significance in a single-gene test. Network analysis aggregates these small effects across connected genes: if many members of a protein complex or pathway show modest changes in the same direction, the pathway-level signal can be strong even when no individual gene is significant. This 'guilt by association' approach also helps interpret genes of unknown function — if an uncharacterized gene is co-expressed with known immune genes, it likely has an immune-related function.
The classic example is GSEA (Gene Set Enrichment Analysis), which detects coordinated shifts in gene sets without requiring individual genes to pass a significance threshold. Network propagation methods extend this further by spreading signal through interaction networks, identifying modules of functionally related genes that are collectively perturbed.