KEGG pathway and module enrichment analysis using clusterProfiler enrichKEGG and enrichMKEGG. Use when identifying metabolic and signaling pathways over-represented in a gene list. Supports 4000+ organisms via KEGG online database.
Reference examples tested with: R stats (base), clusterProfiler 4.10+
Before using code patterns, verify installed versions match. If versions differ:
packageVersion('<pkg>') then ?function_name to verify parametersIf code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.
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'
)
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
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
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)
# enrichKEGG does NOT have readable parameter - use setReadable
library(org.Hs.eg.db)
kk_readable <- setReadable(kk, OrgDb = org.Hs.eg.db, keyType = 'ENTREZID')
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
)
| 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')
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
)
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
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')
| 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 |
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)
setReadable() with OrgDb