bio-pathway-go-enrichment

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

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.

# 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.

# 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

# 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

# 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

# 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.

# Remove redundant GO terms (keeps representative terms)
ego_simplified <- simplify(ego, cutoff = 0.7, by = 'p.adjust', select_fun = min)

Different Organisms

# 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.

# 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