bio-pathway-wikipathways

name	bio-pathway-wikipathways
description	WikiPathways enrichment using clusterProfiler and rWikiPathways. Use when analyzing gene lists against community-curated open-source pathways. Performs over-representation analysis and GSEA for 30+ species.
tool_type	r
primary_tool	clusterProfiler

WikiPathways Enrichment

Core Pattern - Over-Representation Analysis

library(clusterProfiler)
library(org.Hs.eg.db)

wp_result <- enrichWP(
    gene = entrez_ids,         # Character vector of Entrez IDs
    organism = 'Homo sapiens', # Full species name
    pvalueCutoff = 0.05,
    pAdjustMethod = 'BH'
)

head(as.data.frame(wp_result))

Prepare Gene List

de_results <- read.csv('de_results.csv')
sig_genes <- de_results[de_results$padj < 0.05 & abs(de_results$log2FoldChange) > 1, 'gene_symbol']

gene_ids <- bitr(sig_genes, fromType = 'SYMBOL', toType = 'ENTREZID', OrgDb = org.Hs.eg.db)
entrez_ids <- gene_ids$ENTREZID

GSEA on WikiPathways

# Create ranked gene list
gene_list <- de_results$log2FoldChange
names(gene_list) <- de_results$entrez_id
gene_list <- sort(gene_list, decreasing = TRUE)

gsea_wp <- gseWP(
    geneList = gene_list,
    organism = 'Homo sapiens',
    pvalueCutoff = 0.05,
    pAdjustMethod = 'BH'
)

head(as.data.frame(gsea_wp))

With Background Universe

all_genes <- de_results$entrez_id

wp_result <- enrichWP(
    gene = entrez_ids,
    universe = all_genes,
    organism = 'Homo sapiens',
    pvalueCutoff = 0.05
)

Make Results Readable

# Convert Entrez IDs to gene symbols
wp_readable <- setReadable(wp_result, OrgDb = org.Hs.eg.db, keyType = 'ENTREZID')

Visualization

library(enrichplot)

# Dot plot
dotplot(wp_result, showCategory = 15)

# Bar plot
barplot(wp_result, showCategory = 15)

# Gene-concept network
cnetplot(wp_readable, categorySize = 'pvalue')

# Enrichment map
wp_result <- pairwise_termsim(wp_result)
emapplot(wp_result)

Using rWikiPathways Directly

library(rWikiPathways)

# List available organisms
listOrganisms()

# Get all pathways for an organism
human_pathways <- listPathways('Homo sapiens')

# Get pathway info
pathway_info <- getPathwayInfo('WP554')  # ACE Inhibitor Pathway

# Get genes in a pathway
pathway_genes <- getXrefList('WP554', 'H')  # HGNC symbols
pathway_entrez <- getXrefList('WP554', 'L')  # Entrez IDs

# Download pathway as GMT for custom analysis
downloadPathwayArchive(organism = 'Homo sapiens', format = 'gmt')

Custom GMT-Based Analysis

# Download WikiPathways GMT
library(rWikiPathways)
downloadPathwayArchive(organism = 'Homo sapiens', format = 'gmt', destpath = '.')

# Read GMT and run enrichment
wp_gmt <- read.gmt('wikipathways-Homo_sapiens.gmt')

wp_custom <- enricher(
    gene = entrez_ids,
    TERM2GENE = wp_gmt,
    pvalueCutoff = 0.05
)

Different Organisms

# Mouse
wp_mouse <- enrichWP(gene = mouse_entrez, organism = 'Mus musculus')

# Rat
wp_rat <- enrichWP(gene = rat_entrez, organism = 'Rattus norvegicus')

# Zebrafish
wp_zfish <- enrichWP(gene = zfish_entrez, organism = 'Danio rerio')

# List all available organisms
library(rWikiPathways)
listOrganisms()

Compare Clusters

gene_clusters <- list(
    upregulated = up_genes,
    downregulated = down_genes
)

compare_wp <- compareCluster(
    geneClusters = gene_clusters,
    fun = 'enrichWP',
    organism = 'Homo sapiens',
    pvalueCutoff = 0.05
)

dotplot(compare_wp)

Export Results

results_df <- as.data.frame(wp_result)
write.csv(results_df, 'wikipathways_enrichment.csv', row.names = FALSE)

Key Parameters

Parameter	Default	Description
gene	required	Vector of Entrez IDs
organism	required	Full species name
pvalueCutoff	0.05	P-value threshold
pAdjustMethod	BH	Adjustment method
universe	NULL	Background genes
minGSSize	10	Min genes per pathway
maxGSSize	500	Max genes per pathway

Common Organisms

Common Name	Scientific Name
Human	Homo sapiens
Mouse	Mus musculus
Rat	Rattus norvegicus
Zebrafish	Danio rerio
Fruit fly	Drosophila melanogaster
C. elegans	Caenorhabditis elegans
Arabidopsis	Arabidopsis thaliana
Yeast	Saccharomyces cerevisiae

WikiPathways vs Other Databases

Feature	WikiPathways	KEGG	Reactome
Curation	Community	Expert	Peer-reviewed
License	Open (CC0)	Commercial	Open
Species	30+	4000+	7
Focus	Disease, drug	Metabolic	Signaling
Updates	Continuous	Ongoing	Quarterly

Related Skills

• go-enrichment - Gene Ontology enrichment
• kegg-pathways - KEGG pathway enrichment
• reactome-pathways - Reactome pathway enrichment
• gsea - Gene Set Enrichment Analysis
• enrichment-visualization - Visualization functions