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WikiPathways Enrichment · FreedomIntelligence | DecimalAI

Bio Pathway Wikipathways

SkillScore4Maintained

bio-pathway-wikipathways

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.

scienceby FreedomIntelligence

Links this skill to your agents and keeps it current with the author’s updates.

Automatic

Research pack

1 · One-time — install the CLI (docs ↗)

pip install decimalai

2 · Get the skill — writes .claude/skills/bio-pathway-wikipathways/SKILL.md

decimalai skills pull bio-pathway-wikipathways --out .claude/skills/

No CLI? Get the same file — curl · raw file

curl -fsSL --create-dirs -o .claude/skills/bio-pathway-wikipathways/SKILL.md https://app.decimal.ai/s/bio-pathway-wikipathways/SKILL.md

Raw file: /s/bio-pathway-wikipathways/SKILL.md ↗ · machine-readable summary at /s/bio-pathway-wikipathways.json ↗

In productionGlobal

how this skill behaves on real traffic · last 30 days

No production data for this skill yet — usage numbers appear here once agents start running it.

Anonymized aggregate across every org using this skill — proof from real production use, not a demo.

Does this skill help? · Benchmark: with vs. withoutsign in to run this on your agents

23 test cases from the publisher’s eval.yaml · each run twice on gemini-3.6-flash — with the skill injected, then without · last run 57d ago

Yes — +4% lift · at higher cost+114% tokens0% turns4% of whole cases pass with skill (1/23)

Test case	Without → With	Effect	Δ tokens	Δ turns
case-17	✗→✓	▲ Improved	—	—
case-05	✗→✗	＝ Same ✗	—	—
case-13	✗→✗	＝ Same ✗	—	—
case-20	✗→✗	＝ Same ✗	—	—
case-01	✗→✗	＝ Same ✗	—	—

Showing 5 of 23 cases

SkillSafety

✓ 3 of 3 passed

Code scan15 of 15 checks clean

0 critical · 0 warning · 0 info — scanner v4 — 7/30/2026

live_secretremote_code_executionreverse_shelldata_exfiltrationssrf_metadataagent_snoopinganti_refusaldestructive_commandsobfuscated_unicodeunicode_homoglyphopaque_blobsuspicious_urltool_overreachtrigger_abuseprompt_injection_phrasing

IntentSafe

LLM judge · does the skill do what it claims

ContentClean

LLM judge · policy & harmful-content review

Source

Imported

FreedomIntelligence / OpenClaw-Medical-Skills

skills/bio-pathway-wikipathways/SKILL.md

View on GitHub ↗

Imported 8/1/2026Version v1

Skill contents

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.

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

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.

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

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.

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

Goal: Create summary plots of WikiPathways enrichment results.

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

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

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.

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

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.

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

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.

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

Illustrative Want to see what bio-pathway-wikipathways would put in front of your model? Preview the rendered body and check its trigger phrases — no LLM call, no signup.

Latest benchmark · publisher runVerified

7/26/2026, 09:12 UTC · 23 cases

Lift

+4%

4% vs 0%

Agent turns

1.0 vs 1.0

Tokens

+114%

2,605.83 vs 1,217

fail → pass · 1fail → fail · 22

Per-case results (23)

Case	Status	Duration (ms)			Turns			Tokens			Tool calls
Case	Status	Without	With	Δ	Without	With	Δ	Without	With	Δ	Without	With	Δ
case-05 I have a WikiPathways enrichment object `wp_result` from `enrichWP`. I tried running `emapplot(wp_result)` directly in R but received an error that term similarity matrix was missing. How should I prepare and generate the enrichment map plot?	fail→fail	—	—	—	—	—	—	—	—	—	—	—	—
case-13 I want to perform WikiPathways over-representation analysis on zebrafish DE genes (`zfish_entrez`). What full scientific name string must be supplied to the organism parameter in `enrichWP`?	fail→fail	—	—	—	—	—	—	—	—	—	—	—	—
case-20 When calling `enrichWP` for human pathway analysis, I want to explicitly specify the Benjamini-Hochberg false discovery rate adjustment method. What string value should be assigned to `pAdjustMethod`?	fail→fail	—	—	—	—	—	—	—	—	—	—	—	—
case-01 I have RNA-seq differential expression results containing Entrez IDs and log2 fold changes. Could you provide R code to perform Gene Set Enrichment Analysis (GSEA) specifically against WikiPathways for human samples? I want to rank genes by fold change and save the resulting pathway table as a CSV file.	fail→fail	—	—	—	—	—	—	—	—	—	—	—	—
case-22 I want to perform pathway enrichment analysis specifically against the Reactome database using human Entrez gene IDs in R. What function from the ReactomePA package should be called?	fail→fail	—	—	—	—	—	—	—	—	—	—	—	—
case-23 I want to test for Gene Ontology (GO) Biological Process over-representation among a set of human Entrez gene IDs in R using clusterProfiler. What function should be called and what subontology parameter should be set?	fail→fail	—	—	—	—	—	—	—	—	—	—	—	—
case-17 I have a readable WikiPathways enrichment object `wp_readable`. I want to plot a gene-concept network where node sizes reflect p-values using enrichplot. What plotting function should be used?	fail→pass	—	—	—	—	—	—	—	—	—	—	—	—
case-18 I completed a WikiPathways enrichment analysis with `enrichWP` and stored the output in `wp_result`. I need to convert this S4 enrichment result object into a standard R data frame and write it to 'wikipathways_enrichment.csv'. Show the standard R code.	fail→fail	—	—	—	—	—	—	—	—	—	—	—	—
case-21 I have a list of human Entrez gene IDs `entrez_ids` from a microarray study. I want to perform KEGG pathway over-representation analysis using clusterProfiler in R. Which function should I use and what organism code format does KEGG expect?	fail→fail	—	—	—	—	—	—	—	—	—	—	—	—
case-07 I downloaded a custom WikiPathways GMT file named `wikipathways-Homo_sapiens.gmt`. I read it into R using `read.gmt`. Now I want to test my `entrez_ids` list against this custom GMT data frame `wp_gmt`. Should I use `enrichWP` or a generic enrichment function?	fail→fail	—	—	—	—	—	—	—	—	—	—	—	—
case-06 I need to download the latest human WikiPathways definitions in GMT format directly to my local R workspace so I can analyze them offline. I'm tempted to manually scrape the WikiPathways website or use `download.file` with a static URL. What is the standard programmatic function from the rWikiPathways R package for archiving pathway GMT files?	fail→fail	—	—	—	—	—	—	—	—	—	—	—	—
case-03 I have a vector of DE gene symbols `sig_symbols` from a human study. I want to convert these to Entrez IDs in R using standard clusterProfiler utilities before running WikiPathways enrichment. Show the conversion code.	fail→fail	—	—	—	—	—	—	—	—	—	—	—	—
case-04 After running `enrichWP` in clusterProfiler, my result object `wp_res` displays Entrez IDs in the GeneID column. I want to convert these Entrez IDs back to human gene symbols directly on the enrichment result object before plotting or exporting. How should I do this in R?	fail→fail	—	—	—	—	—	—	—	—	—	—	—	—
case-12 I ran a differential expression experiment where 12,000 total genes were tested (`all_entrez`), and 450 were significant (`sig_entrez`). I want to run `enrichWP` on human genes taking into account the 12,000 tested genes as the background universe to avoid selection bias. How do I supply this?	fail→fail	—	—	—	—	—	—	—	—	—	—	—	—
case-02 Write R code to perform over-representation analysis against WikiPathways for a vector of human Entrez gene IDs named `my_entrez_genes`. I usually use short organism codes like 'hsa' or 'Hs' in Bioconductor tools—is that what I should pass for the organism parameter?	fail→fail	—	—	—	—	—	—	—	—	—	—	—	—
case-15 I need to programmatically fetch pathway metadata (such as name, species, and revision) for pathway ID 'WP554' using R. Which function in `rWikiPathways` handles pathway info retrieval?	fail→fail	—	—	—	—	—	—	—	—	—	—	—	—
case-11 I want to retrieve HGNC gene symbols for human pathway 'WP554' using `rWikiPathways`. What system code should be passed to `getXrefList` to get gene symbols rather than Entrez IDs?	fail→fail	—	—	—	—	—	—	—	—	—	—	—	—
case-08 I have two gene vectors, `up_genes` and `down_genes` (Entrez IDs). I want to perform comparative WikiPathways over-representation analysis across both clusters simultaneously in R and plot a dotplot comparing them. What clusterProfiler function and setup should I use?	fail→fail	—	—	—	—	—	—	—	—	—	—	—	—
case-09 I am running WikiPathways over-representation analysis with `enrichWP` on a vector of mouse Entrez IDs `mouse_genes`. Should I set `organism = 'mm'` or `organism = 'Mus musculus'`?	fail→fail	—	—	—	—	—	—	—	—	—	—	—	—
case-14 I want to check whether Arabidopsis thaliana is supported in the WikiPathways database programmatically from R before running analysis. What function in the `rWikiPathways` library retrieves all available species?	fail→fail	—	—	—	—	—	—	—	—	—	—	—	—
case-16 What is the exact scientific organism name string to use when running `enrichWP` on rat Entrez gene IDs?	fail→fail	—	—	—	—	—	—	—	—	—	—	—	—
case-10 Using the `rWikiPathways` package in R, I want to retrieve the list of Entrez gene IDs for the human ACE Inhibitor Pathway (id: 'WP554'). I know `getXrefList` takes a system code parameter—should I pass code 'H' or code 'L' to get Entrez gene IDs?	fail→fail	—	—	—	—	—	—	—	—	—	—	—	—
case-19 Given an R data frame `de_results` containing columns `padj`, `log2FoldChange`, and `gene_symbol`, write the R filtering logic to extract gene symbols that have adjusted p-value under 0.05 and absolute log2 fold change greater than 1.	fail→fail	—	—	—	—	—	—	—	—	—	—	—	—

These cases were also screened by the skill’s intent / safety pass.See the Trust review ↗Reproduce this benchmark in Colab ↗

How this skill was measured

DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 23 cases were attempted. The headline lift of +4 percentage points is the difference between those two pass rates over the 23 comparable cases.

Model measured: gemini-3.6-flash
Graded by: gemini-3.6-flash
Grading method: judged
Verification: verified
Pass rate without the skill: 0%
Pass rate with the skill: 4.4%
Cases attempted: 23
Cases counted toward lift: 23
Cases that regressed: 0
Skill version measured: v1
Run completed: 7/26/2026, 09:12 UTC

The per-case answers from this run were removed by the retention sweep, so the case table below shows the verdicts without the text either arm produced. The counts above were recorded at the time and are unaffected. Answers are now kept for 180 days.

SkillScore blends 4 signals1 of 4 complete

BenchmarkPass rate WITH skill on a verified run4

Live evalNeeds ≥10 evaluated traces in 30 days

AI ratingNeeds ≥10 rated traces

AdoptionNeeds ≥10 serves across ≥2 orgs in 30 days

Backed by 1 of 4 signals — each added signal makes the score more robust.

Verified on gemini-3.6-flash · re-tested 56 days ago

Other measured skills in the registry, with their headline benchmark lift.