Version Compatibility
Reference examples tested with: R stats (base), clusterProfiler 4.10+
Before using code patterns, verify installed versions match. If versions differ:
- R:
packageVersion('<pkg>') then ?function_name to verify parameters
If code throws ImportError, AttributeError, or TypeError, introspect the installed
package and adapt the example to match the actual API rather than retrying.
KEGG Pathway Enrichment
Core Pattern
Goal: Identify KEGG metabolic and signaling pathways over-represented in a gene list.
Approach: Test for enrichment using the hypergeometric test via clusterProfiler enrichKEGG against the KEGG online database.
"Find enriched KEGG pathways in my gene list" → Test whether KEGG pathway gene sets are over-represented among significant genes.
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
Goal: Extract significant Entrez gene IDs from DE results in the format required by enrichKEGG.
Approach: Filter by significance thresholds and convert gene symbols to Entrez IDs (KEGG requires NCBI Entrez).
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
Goal: Convert between KEGG-specific identifiers and other gene ID formats.
Approach: Use bitr_kegg to map between kegg, ncbi-geneid, ncbi-proteinid, and uniprot ID types.
# 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
Goal: Perform KEGG pathway over-representation analysis with customizable parameters.
Approach: Run enrichKEGG with specified organism, ID type, and statistical thresholds.
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
Goal: Test for enrichment of KEGG modules (smaller functional units than pathways).
Approach: Use enrichMKEGG which tests against KEGG module definitions rather than full pathways.
# 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
Goal: Restrict KEGG enrichment to genes actually measured in the experiment.
Approach: Convert all tested genes to Entrez IDs and pass as the universe parameter.
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
Goal: Save KEGG enrichment results to CSV and extract genes belonging to specific pathways.
Approach: Convert enrichment object to data frame, export, and access pathway gene sets via the geneSets slot.
# 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
Goal: Visualize enriched genes overlaid on KEGG pathway diagrams.
Approach: Use browseKEGG for interactive browser view or pathview to generate annotated pathway images.
# 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
Goal: Compare KEGG pathway enrichment across multiple gene lists (e.g., upregulated vs downregulated).
Approach: Use compareCluster with enrichKEGG to run enrichment per group and visualize with dotplot.
# 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