Agent skill
bio-pathway-go-enrichment
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.
Install this agent skill to your Project
npx add-skill https://github.com/majiayu000/claude-skill-registry/tree/main/skills/other/go-enrichment
SKILL.md
GO Over-Representation Analysis
Core Pattern
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
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$ENTREZID
ID Conversion with bitr
# Check available key types
keytypes(org.Hs.eg.db)
# Convert between ID types
converted <- bitr(genes, fromType = 'ENSEMBL', toType = 'ENTREZID', OrgDb = org.Hs.eg.db)
# Multiple output types
converted <- bitr(genes, fromType = 'SYMBOL', toType = c('ENTREZID', 'ENSEMBL'), OrgDb = org.Hs.eg.db)
With Background Universe
# Use all expressed genes as background (recommended)
all_genes <- de_results$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
# 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
# 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
Recommended Agent Skills
Expand your agent's capabilities with these related and highly-rated skills.
majiayu000/claude-skill-registry
agent-ops-spec
Manage specification documents in .agent/specs/. Use when user provides requirements, acceptance criteria, or feature descriptions that need to be tracked and validated against implementation.
majiayu000/claude-skill-registry
agent-ops-state
Maintain .agent state files. Use at session start, after meaningful steps, and before concluding: read/update constitution/memory/focus/issues/baseline consistently.
majiayu000/claude-skill-registry
agent-ops-spec
Manage specification documents in .agent/specs/. Use when user provides requirements, acceptance criteria, or feature descriptions that need to be tracked and validated against implementation.
majiayu000/claude-skill-registry
agent-ops-testing
Test strategy, execution, and coverage analysis. Use when designing tests, running test suites, or analyzing test results beyond baseline checks.
majiayu000/claude-skill-registry
agent-ops-testing
Test strategy, execution, and coverage analysis. Use when designing tests, running test suites, or analyzing test results beyond baseline checks.
majiayu000/claude-skill-registry
agent-ops-state
Maintain .agent state files. Use at session start, after meaningful steps, and before concluding: read/update constitution/memory/focus/issues/baseline consistently.
Didn't find tool you were looking for?