--- name: bio-pathway-go-enrichment description: Gene Ontology over-representation analysis using clusterProfiler enrichGO. Use when identifying biological functions enriched in a gene list from differential expression or other analyses. Supports all three ontologies (BP, MF, CC), multiple ID types, and customizable statistical thresholds. tool_type: r primary_tool: clusterProfiler --- ## 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('')` 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. ```r 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. ```r 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$ENTREZID ``` ## ID Conversion with bitr **Goal:** Convert between gene identifier types (Ensembl, Symbol, Entrez) for compatibility with enrichment tools. **Approach:** Use clusterProfiler bitr to map between ID types using organism annotation databases. ```r # Check available key types keytypes(org.Hs.eg.db) # Convert between ID types converted <- bitr(genes, fromType = 'ENSEMBL', toType = 'ENTREZID', OrgDb = org.Hs.eg.db) # Multiple output types converted <- bitr(genes, fromType = 'SYMBOL', toType = c('ENTREZID', 'ENSEMBL'), OrgDb = org.Hs.eg.db) ``` ## With Background Universe **Goal:** Improve enrichment specificity by restricting the background to genes actually tested in the experiment. **Approach:** Pass all expressed genes (not just significant ones) as the universe parameter to enrichGO. ```r # Use all expressed genes as background (recommended) all_genes <- de_results$gene_id universe_ids <- bitr(all_genes, fromType = 'SYMBOL', toType = 'ENTREZID', OrgDb = org.Hs.eg.db) ego <- enrichGO( gene = gene_list, universe = universe_ids$ENTREZID, # Background gene set OrgDb = org.Hs.eg.db, keyType = 'ENTREZID', ont = 'BP', pAdjustMethod = 'BH', pvalueCutoff = 0.05 ) ``` ## All Three Ontologies ```r # Run all ontologies at once ego_all <- enrichGO( gene = gene_list, OrgDb = org.Hs.eg.db, keyType = 'ENTREZID', ont = 'ALL', # BP, MF, and CC combined pAdjustMethod = 'BH', pvalueCutoff = 0.05 ) # Results include ONTOLOGY column head(as.data.frame(ego_all)) ``` ## Make Results Readable ```r # Convert Entrez IDs to gene symbols in results ego_readable <- setReadable(ego, OrgDb = org.Hs.eg.db, keyType = 'ENTREZID') # Or use readable = TRUE directly (only works with ENTREZID input) ego <- enrichGO( gene = gene_list, OrgDb = org.Hs.eg.db, keyType = 'ENTREZID', ont = 'BP', readable = TRUE # Converts to symbols ) ``` ## Extract and Export Results ```r # View top results head(ego) # Convert to data frame results_df <- as.data.frame(ego) # Key columns: ID, Description, GeneRatio, BgRatio, pvalue, p.adjust, qvalue, geneID, Count # Export to CSV write.csv(results_df, 'go_enrichment_results.csv', row.names = FALSE) # Filter for specific criteria sig_terms <- results_df[results_df$p.adjust < 0.01 & results_df$Count >= 5, ] ``` ## Simplify Redundant Terms **Goal:** Remove highly similar GO terms to reduce redundancy in enrichment results. **Approach:** Cluster GO terms by semantic similarity and retain representative terms using the simplify function. ```r # Remove redundant GO terms (keeps representative terms) ego_simplified <- simplify(ego, cutoff = 0.7, by = 'p.adjust', select_fun = min) ``` ## Different Organisms ```r # Mouse library(org.Mm.eg.db) ego_mouse <- enrichGO(gene = genes, OrgDb = org.Mm.eg.db, ont = 'BP') # Zebrafish library(org.Dr.eg.db) ego_zfish <- enrichGO(gene = genes, OrgDb = org.Dr.eg.db, ont = 'BP') # Yeast library(org.Sc.sgd.db) ego_yeast <- enrichGO(gene = genes, OrgDb = org.Sc.sgd.db, ont = 'BP', keyType = 'ORF') ``` ## Group GO Terms by Ancestor **Goal:** Classify genes by broad GO slim categories for a high-level functional overview. **Approach:** Use groupGO to assign genes to GO terms at a specific hierarchy level. ```r # Classify genes by GO slim categories ggo <- groupGO( gene = gene_list, OrgDb = org.Hs.eg.db, ont = 'BP', level = 3, # GO hierarchy level readable = TRUE ) ``` ## Key Parameters | Parameter | Default | Description | |-----------|---------|-------------| | gene | required | Vector of gene IDs | | OrgDb | required | Organism database | | keyType | ENTREZID | Input ID type | | ont | BP | BP, MF, CC, or ALL | | pvalueCutoff | 0.05 | P-value threshold | | qvalueCutoff | 0.2 | Q-value (FDR) threshold | | pAdjustMethod | BH | BH, bonferroni, etc. | | universe | NULL | Background genes | | minGSSize | 10 | Min genes per term | | maxGSSize | 500 | Max genes per term | | readable | FALSE | Convert to symbols | ## Related Skills - kegg-pathways - KEGG pathway enrichment - gsea - Gene Set Enrichment Analysis for GO - enrichment-visualization - Visualize enrichment results - differential-expression - Generate input gene lists

bio-pathway-go-enrichment

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