KEGG Pathway Enrichment
Core Pattern
r
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
r
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
r
# 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
r
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
r
# 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
r
# 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 |
r
# Find organism codes
search_kegg_organism('mouse')
search_kegg_organism('zebrafish')
With Background Universe
r
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
r
# 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
r
# 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
r
# 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