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

Organism	Code	Common Name
hsa	Human	Homo sapiens
mmu	Mouse	Mus musculus
rno	Rat	Rattus norvegicus
dre	Zebrafish	Danio rerio
dme	Fruit fly	Drosophila melanogaster
cel	Worm	C. elegans
sce	Yeast	S. cerevisiae
ath	Arabidopsis	A. thaliana
eco	E. coli K-12

# 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

Parameter	Default	Description
gene	required	Vector of gene IDs
organism	hsa	KEGG organism code
keyType	kegg	Input ID type
pvalueCutoff	0.05	P-value threshold
qvalueCutoff	0.2	Q-value threshold
pAdjustMethod	BH	Adjustment method
universe	NULL	Background genes
minGSSize	10	Min genes per pathway
maxGSSize	500	Max genes per pathway
use_internal_data	FALSE	Use local KEGG data

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