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