Version Compatibility

Reference examples tested with: R stats (base), clusterProfiler 4.10+

Before using code patterns, verify installed versions match. If versions differ:

• R: packageVersion('<pkg>') then ?function_name to verify parameters

If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.

GO Over-Representation Analysis

Core Pattern

Goal: Identify enriched Gene Ontology terms in a gene list from differential expression or similar analyses.

Approach: Test for over-representation of GO terms using the hypergeometric test via clusterProfiler enrichGO.

"Run GO enrichment on my gene list" → Test whether biological process, molecular function, or cellular component terms are over-represented among significant genes.

library(clusterProfiler)
library(org.Hs.eg.db)  # Human - change for other organisms

ego <- enrichGO(
    gene = gene_list,           # Character vector of gene IDs
    OrgDb = org.Hs.eg.db,       # Organism annotation database
    keyType = 'ENTREZID',       # ID type: ENSEMBL, SYMBOL, ENTREZID, etc.
    ont = 'BP',                 # BP, MF, CC, or ALL
    pAdjustMethod = 'BH',       # p-value adjustment method
    pvalueCutoff = 0.05,
    qvalueCutoff = 0.2
)

Prepare Gene List from DE Results

Goal: Extract significant gene IDs from differential expression results and convert to the format required by enrichGO.

Approach: Filter DE results by adjusted p-value and fold change, then convert gene symbols to Entrez IDs using bitr.

library(dplyr)

de_results <- read.csv('de_results.csv')

sig_genes <- de_results %>%
    filter(padj < 0.05, abs(log2FoldChange) > 1) %>%
    pull(gene_id)

# If using gene symbols, convert to Entrez IDs
gene_ids <- bitr(sig_genes, fromType = 'SYMBOL', toType = 'ENTREZID', OrgDb = org.Hs.eg.db)
gene_list <- gene_ids$ENTREZID