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
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