bio-pathway-wikipathways

FreedomIntelligence/OpenClaw-Medical-SkillsBiology Medicine and BioinformaticsAudit pendingSnapshot 33bc9023d50d

Summary

This source did not publish a separate summary. Review SKILL.md before using the skill.

SKILL.md

Version Compatibility

Reference examples tested with: ReactomePA 1.46+, clusterProfiler 4.10+, rWikiPathways 1.24+

Before using code patterns, verify installed versions match. If versions differ:

R: packageVersion('<pkg>') then ?function_name to verify parameters

If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.

WikiPathways Enrichment

Core Pattern - Over-Representation Analysis

Goal: Identify WikiPathways that are over-represented in a gene list.

Approach: Test for enrichment using enrichWP against community-curated open-source pathway definitions.

"Run pathway enrichment against WikiPathways" → Test whether genes from community-curated WikiPathways are over-represented among significant genes.

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

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))

all_genes <- de_results$entrez_id

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

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

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)

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')

# 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
)

# 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()

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)

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

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 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

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

bio-pathway-wikipathways

Summary

SKILL.md

Version Compatibility

WikiPathways Enrichment

Core Pattern - Over-Representation Analysis

Prepare Gene List

GSEA on WikiPathways

With Background Universe

Make Results Readable

Visualization

Using rWikiPathways Directly

Custom GMT-Based Analysis

Different Organisms

Compare Clusters

Export Results

Key Parameters

Common Organisms

WikiPathways vs Other Databases

Related Skills

Related skills