KEGG Pathway Enrichment

Core Pattern

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

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

# 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

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

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

Common Organism Codes

# Find organism codes
search_kegg_organism('mouse')
search_kegg_organism('zebrafish')

With Background Universe

all_genes <- de_results$gene_id
universe_ids <- bitr(all_genes, 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

# 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

# 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

# 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)

Notes

• No readable parameter - use setReadable() with OrgDb
• 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)

Related Skills

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