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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.
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| name | bio-pathway-kegg-pathways |
| description | 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. |
| tool_type | r |
| primary_tool | clusterProfiler |
library(clusterProfiler)
kk <- enrichKEGG(
gene = gene_list, # Character vector of gene IDs
organism = 'hsa', # KEGG organism code
pvalueCutoff = 0.05,
pAdjustMethod = 'BH'
)
library(org.Hs.eg.db)
de_results read.csv
sig_genes de_resultsgene_idde_resultspadj de_resultslog2FoldChange
gene_ids bitrsig_genes fromType toType OrgDb org.Hs.eg.db
gene_list gene_idsENTREZID
# 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
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')
# 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')
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
)
# 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
# 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 |
# 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