version: "1.0.0" name: bio-pathway-kegg-pathways description: KEGG pathway and module enrichment analysis using clusterProfiler enrichKEGG and enrichMKEGG. Use when identifying metabolic and signaling pathways over-represented in a gene list. Supports 4000+ organisms via KEGG online database. 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('<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.

KEGG Pathway Enrichment

Core Pattern

Goal: Identify KEGG metabolic and signaling pathways over-represented in a gene list.

Approach: Test for enrichment using the hypergeometric test via clusterProfiler enrichKEGG against the KEGG online database.

"Find enriched KEGG pathways in my gene list" → Test whether KEGG pathway gene sets are over-represented among significant genes.

library(clusterProfiler)
 
kk <- enrichKEGG(
    gene = gene_list,           # Character vector of gene IDs
    organism = 'hsa',           # KEGG organism code
    pvalueCutoff = 0.05,
    pAdjustMethod = 'BH'
)

Prepare Gene List

Goal: Extract significant Entrez gene IDs from DE results in the format required by enrichKEGG.

Approach: Filter by significance thresholds and convert gene symbols to Entrez IDs (KEGG requires NCBI Entrez).

library(org.Hs.eg.db)
 
de_results <- read.csv('de_results.csv')
sig_genes <- de_results$gene_id[de_results$padj < 0.05 & abs(de_results$log2FoldChange) > 1]
 
# KEGG requires NCBI Entrez gene IDs (kegg, ncbi-geneid)
gene_ids <- bitr(sig_genes, fromType = 'SYMBOL', toType = 'ENTREZID', OrgDb = org.Hs.eg.db)
gene_list <- gene_ids$ENTREZID

KEGG ID Conversion

Goal: Convert between KEGG-specific identifiers and other gene ID formats.

Approach: Use bitr_kegg to map between kegg, ncbi-geneid, ncbi-proteinid, and uniprot ID types.

# Convert between KEGG and other IDs
kegg_ids <- bitr_kegg(gene_list, fromType = 'ncbi-geneid', toType = 'kegg', organism = 'hsa')
 
# Available types: kegg, ncbi-geneid, ncbi-proteinid, uniprot

Run KEGG Pathway Enrichment

Goal: Perform KEGG pathway over-representation analysis with customizable parameters.

Approach: Run enrichKEGG with specified organism, ID type, and statistical thresholds.

kk <- enrichKEGG(
    gene = gene_list,
    organism = 'hsa',
    keyType = 'ncbi-geneid',    # or 'kegg'
    pvalueCutoff = 0.05,
    pAdjustMethod = 'BH',
    minGSSize = 10,
    maxGSSize = 500
)
 
# View results
head(kk)
results <- as.data.frame(kk)

Make Results Readable

# enrichKEGG does NOT have readable parameter - use setReadable
library(org.Hs.eg.db)
kk_readable <- setReadable(kk, OrgDb = org.Hs.eg.db, keyType = 'ENTREZID')

KEGG Module Enrichment

Goal: Test for enrichment of KEGG modules (smaller functional units than pathways).

Approach: Use enrichMKEGG which tests against KEGG module definitions rather than full pathways.

# KEGG modules are smaller functional units than pathways
mkk <- enrichMKEGG(
    gene = gene_list,
    organism = 'hsa',
    pvalueCutoff = 0.05
)

Common Organism Codes

KEGG covers 8,000+ organisms. Always verify the code for the specific strain:

search_kegg_organism('Pseudomonas', by = 'scientific_name')
search_kegg_organism('aeruginosa', by = 'scientific_name')

Background Universe (Critical)

Goal: Restrict KEGG enrichment to genes actually measured in the experiment.

Approach: Convert all tested genes to Entrez IDs and pass as the universe parameter.

Without specifying the universe, enrichKEGG uses all KEGG-annotated genes as background. This inflates significance for tissue-specific pathways (e.g., liver-expressed pathways in a liver RNA-seq experiment will appear enriched simply because liver genes are expressed and brain genes are not).

# Background = all tested genes (non-NA pvalue from DE analysis)
all_tested <- de_results$gene_id[!is.na(de_results$pvalue)]
universe_ids <- bitr(all_tested, fromType = 'SYMBOL', toType = 'ENTREZID', OrgDb = org.Hs.eg.db)
 
kk <- enrichKEGG(
    gene = gene_list,
    universe = universe_ids$ENTREZID,
    organism = 'hsa',
    pvalueCutoff = 0.05
)

Extract and Export Results

Goal: Save KEGG enrichment results to CSV and extract genes belonging to specific pathways.

Approach: Convert enrichment object to data frame, export, and access pathway gene sets via the geneSets slot.

# Convert to data frame
results_df <- as.data.frame(kk)
 
# Key columns: ID (pathway), Description, GeneRatio, BgRatio, pvalue, p.adjust, geneID, Count
 
# Export
write.csv(results_df, 'kegg_enrichment_results.csv', row.names = FALSE)
 
# Get genes in a specific pathway
pathway_genes <- kk@geneSets[['hsa04110']]  # Cell cycle

Browse KEGG Pathways

Goal: Visualize enriched genes overlaid on KEGG pathway diagrams.

Approach: Use browseKEGG for interactive browser view or pathview to generate annotated pathway images.

# View pathway in browser (opens KEGG website)
browseKEGG(kk, 'hsa04110')
 
# Download pathway image
library(pathview)
pathview(gene.data = gene_list, pathway.id = 'hsa04110', species = 'hsa')

Key Parameters

Compare Multiple Gene Lists

Goal: Compare KEGG pathway enrichment across multiple gene lists (e.g., upregulated vs downregulated).

Approach: Use compareCluster with enrichKEGG to run enrichment per group and visualize with dotplot.

# Compare KEGG enrichment across groups
gene_lists <- list(
    up = up_genes,
    down = down_genes
)
 
ck <- compareCluster(
    geneClusters = gene_lists,
    fun = 'enrichKEGG',
    organism = 'hsa'
)
 
dotplot(ck)

Prokaryotic / Non-Model Organism KEGG

Bacteria and non-model organisms do NOT use org.*.eg.db packages or bitr(). Bacterial genes use locus tags (e.g., PA0001 for P. aeruginosa, b0001 for E. coli) that map directly as KEGG gene IDs.

# Bacterial KEGG ORA -- no bitr() or OrgDb needed
# Gene IDs should be locus tags matching the KEGG genome
kegg_bac <- enrichKEGG(
    gene = sig_locus_tags,       # e.g., c('PA0001', 'PA0612', 'PA3476')
    organism = 'pae',            # P. aeruginosa PAO1
    keyType = 'kegg',            # use locus tags directly
    pvalueCutoff = 0.05,
    pAdjustMethod = 'BH'
)
 
# Note: setReadable() requires an OrgDb which does not exist for most bacteria
# Instead, map gene IDs manually or use KEGG gene names from the result

For organisms without KEGG strain-specific annotation, use KEGG Orthology (KO) with organism = 'ko'. Map genes to KO IDs via eggNOG-mapper or BlastKOALA first.

Multi-Condition Comparison

Goal: Find shared and condition-specific enriched pathways across experimental conditions.

Approach: Run enrichKEGG per condition, then use set operations on significant pathway IDs. Do NOT compare p-values across conditions (they depend on sample size and DE gene count).

# Run enrichment per condition
kk_A <- enrichKEGG(gene = sig_genes_A, organism = 'hsa', pvalueCutoff = 0.05)
kk_B <- enrichKEGG(gene = sig_genes_B, organism = 'hsa', pvalueCutoff = 0.05)
 
# Set operations on enriched pathway IDs
paths_A <- as.data.frame(kk_A)$ID
paths_B <- as.data.frame(kk_B)$ID
shared <- intersect(paths_A, paths_B)
only_A <- setdiff(paths_A, paths_B)
only_B <- setdiff(paths_B, paths_A)
 
# Or use compareCluster for side-by-side visualization
gene_clusters <- list(ConditionA = sig_genes_A, ConditionB = sig_genes_B)
ck <- compareCluster(geneClusters = gene_clusters, fun = 'enrichKEGG', organism = 'hsa')
dotplot(ck, showCategory = 10)

For proper multi-contrast enrichment that avoids p-value comparison pitfalls, use the mitch package (rank-MANOVA approach).

Notes

• No readable parameter - use setReadable() with OrgDb (eukaryotes only)
• Requires internet - queries KEGG database online
• use_internal_data - set TRUE to use cached KEGG data (may be outdated)
• Pathway IDs - format is organism code + 5 digits (e.g., hsa04110)
• Licensing - KEGG data is free for academic web browsing but bulk downloads and commercial use require a license; for reproducibility-critical work, consider Reactome or WikiPathways (fully open)
• Background universe - always specify; default uses all KEGG-annotated genes which inflates significance

Related Skills

• go-enrichment - Gene Ontology enrichment analysis
• gsea - GSEA using KEGG pathways (gseKEGG)
• enrichment-visualization - Visualize KEGG results