Bio Pathway Wikipathways

Prepare Gene List

Goal: Extract significant Entrez gene IDs from DE results for WikiPathways enrichment.

Approach: Filter by significance thresholds and convert gene symbols to Entrez IDs with bitr.

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

Goal: Detect coordinated expression changes in WikiPathways using a ranked gene list.

Approach: Sort genes by fold change and run gseWP for rank-based enrichment testing.

# 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

Goal: Create summary plots of WikiPathways enrichment results.

Approach: Use enrichplot functions (dotplot, barplot, cnetplot, emapplot) on the enrichment result object.

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

Goal: Query the WikiPathways database directly for pathway metadata, gene lists, and GMT files.

Approach: Use rWikiPathways API functions to list organisms, retrieve pathway info, and download gene set definitions.

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

Goal: Run enrichment using a downloaded WikiPathways GMT file for offline or custom analysis.

Approach: Download the GMT archive via rWikiPathways, read it with read.gmt, and run enricher.

# 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

Goal: Compare WikiPathways enrichment across multiple gene lists (e.g., upregulated vs downregulated).

Approach: Use compareCluster with enrichWP to run enrichment per group and visualize with dotplot.

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

Common Organisms

WikiPathways vs Other Databases

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