bio-pathway-go-enrichment

Perform GO enrichment by providing a gene list to clusterProfiler's enrichGO, which calculates over-representation across BP, MF, and CC ontologies. The analysis requires R, clusterProfiler, and an annotation package like org.Hs.eg.db to output significant biological functions.

Yes, GO over-representation analysis supports Entrez IDs, Ensembl IDs, and Symbols. The analysis processes these multiple gene identifier schemes and can convert them to readable symbols within the enrichment results.

Yes, running enrichGO requires R, the clusterProfiler package, and an organism annotation package like org.Hs.eg.db. These dependencies are necessary to execute the GO over-representation analysis and map gene identifiers.

You can optionally specify a background universe during GO enrichment to refine the over-representation analysis. This restricts the statistical calculation to your specified gene set, yielding more targeted enriched biological functions.

BP, MF, and CC ontologies categorize gene list enrichment into Biological Process, Molecular Function, and Cellular Component groups. Analyzing these distinct categories helps interpret differential expression results by identifying specific functional themes.

Differential expression genes may show no significant GO terms if default p-value and q-value thresholds are too strict. Customizing these thresholds or specifying a correct background universe during GO enrichment can help identify enriched pathways.