WikiPathways Enrichment
Core Pattern - Over-Representation Analysis
r
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
r
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$ENTREZID
GSEA on WikiPathways
r
# Create ranked gene list
gene_list <- de_results$log2FoldChange
names(gene_list) <- de_results$entrez_id
gene_list <- sort(gene_list, decreasing = TRUE)
gsea_wp <- gseWP(
geneList = gene_list,
organism = 'Homo sapiens',
pvalueCutoff = 0.05,
pAdjustMethod = 'BH'
)
head(as.data.frame(gsea_wp))
With Background Universe
r
all_genes <- de_results$entrez_id
wp_result <- enrichWP(
gene = entrez_ids,
universe = all_genes,
organism = 'Homo sapiens',
pvalueCutoff = 0.05
)
Make Results Readable
r
# Convert Entrez IDs to gene symbols wp_readable <- setReadable(wp_result, OrgDb = org.Hs.eg.db, keyType = 'ENTREZID')
Visualization
r
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
r
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
r
# 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
r
# 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
r
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
r
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