Skip to main content
scientific-pathway-enrichment パスウェイ・Gene Ontology 富化解析スキル。KEGG パスウェイ検索・マッピング、
Reactome パスウェイ階層解析、Gene Ontology (BP/MF/CC) アノテーション、
WikiPathways コミュニティパスウェイ、Pathway Commons 統合相互作用を横断的に
利用した ORA (Over-Representation Analysis)、GSEA (Gene Set Enrichment Analysis)、
トポロジーベース富化解析パイプライン。34+ の ToolUniverse SMCP ツールと連携。
Jump to install Skills Marketplace Discover and explore AI skills built by the community.
Install with Codex or Claude Copy this prompt, paste it into Codex, Claude, or another assistant, and let it review the skill page and install it for you.
Show prompt details A direct command skips the review prompt. Inspect the source before running it.
npx skills add https://github.com/nahisaho/satori --skill scientific-pathway-enrichmentThe command stays on one line. Scroll horizontally to inspect it before copying.
Prefer a local copy? Download the files currently available to SkillsMP.
Download Zip Downloading... More from this repository name scientific-pathway-enrichment description パスウェイ・Gene Ontology 富化解析スキル。KEGG パスウェイ検索・マッピング、
Reactome パスウェイ階層解析、Gene Ontology (BP/MF/CC) アノテーション、
WikiPathways コミュニティパスウェイ、Pathway Commons 統合相互作用を横断的に
利用した ORA (Over-Representation Analysis)、GSEA (Gene Set Enrichment Analysis)、
トポロジーベース富化解析パイプライン。34+ の ToolUniverse SMCP ツールと連携。
Scientific Pathway & Enrichment Analysis
遺伝子リスト・ランク付きプロファイルに対して
KEGG / Reactome / GO / WikiPathways / Pathway Commons の
5 大パスウェイ DB を横断する統合富化解析パイプラインを提供する。
When to Use
DEG リスト (差次発現遺伝子) の機能注釈・パスウェイ富化を行うとき
GSEA で遺伝子ランキングに基づくパスウェイ活性を評価するとき
KEGG / Reactome パスウェイの詳細マッピングが必要なとき
GO (BP/MF/CC) を用いた機能的カテゴリ分類が必要なとき
複数 DB 間のパスウェイ結果を統合比較するとき
Quick Start
1. ORA (Over-Representation Analysis)
import pandas as pd
import numpy as np
from scipy import stats
def over_representation_analysis (gene_list, background_genes,
gene_sets, min_size=10 , max_size=500 ,
fdr_method="fdr_bh" ):
"""
超幾何検定ベースの Over-Representation Analysis (ORA)。
Parameters:
gene_list: list — DEG などの注目遺伝子リスト
background_genes: list — 背景遺伝子集合 (全発現遺伝子)
gene_sets: dict — {pathway_name: [genes]} 辞書
min_size / max_size: パスウェイサイズフィルタ
fdr_method: 多重検定補正 ("fdr_bh", "bonferroni")
"""
from statsmodels.stats.multitest import multipletests
query = set
set
len
len
for
in
set
len
len
if
or
continue
1
if
0
and
0
else
0
"pathway"
"overlap"
"pathway_size"
"background_size"
"query_size"
"fold_enrichment"
round
2
"p_value"
if
len
0
"p_value"
"fdr"
"fdr"
True
print
f"ORA complete: {len (gene_list)} query genes, "
f"{len (gene_sets)} pathways tested, "
f"{(df['fdr' ] < 0.05 ).sum ()} significant (FDR < 0.05)"
return
2. GSEA (Gene Set Enrichment Analysis) def gsea_analysis (ranked_genes, gene_sets, permutations=1000 ,
min_size=15 , max_size=500 , seed=42 ):
"""
GSEA (Subramanian et al., 2005) の Python 実装。
Parameters:
ranked_genes: pd.Series — gene_symbol → fold_change or -log10(p)*sign(FC)
gene_sets: dict — {pathway_name: [genes]}
permutations: 順列検定回数
"""
np.random.seed(seed)
ranked = ranked_genes.sort_values(ascending=False )
gene_names = ranked.index.tolist()
scores = ranked.values
N = len (gene_names)
results = []
for pw_name, pw_genes in gene_sets.items():
hits = set (pw_genes) & set (gene_names)
if len (hits) < min_size or len (hits) > max_size:
continue
hit_mask = np.array([g in hits for g in gene_names])
hit_scores = np.abs (scores) * hit_mask
hit_sum = hit_scores.sum ()
miss_count = N - hit_mask.sum ()
if hit_sum == 0 or miss_count == 0 :
continue
running_sum = np.cumsum(
np.where(hit_mask, np.abs (scores) / hit_sum,
-1.0 / miss_count)
)
es = running_sum[np.argmax(np.abs (running_sum))]
null_es = []
for _ in range (permutations):
perm_idx = np.random.permutation(N)
perm_hit = hit_mask[perm_idx]
perm_scores = np.abs (scores) * perm_hit
ps = perm_scores.sum ()
if ps == 0 :
continue
perm_rs = np.cumsum(
np.where(perm_hit, np.abs (scores) / ps,
-1.0 / miss_count)
)
null_es.append(perm_rs[np.argmax(np.abs (perm_rs))])
null_es = np.array(null_es)
if es >= 0 :
pval = (null_es >= es).mean()
else :
pval = (null_es <= es).mean()
nes = es / np.abs (null_es).mean() if np.abs (null_es).mean() > 0 else 0
results.append({
"pathway" : pw_name,
"es" : round (es, 4 ),
"nes" : round (nes, 4 ),
"p_value" : pval,
"hit_count" : len (hits),
"leading_edge" : [g for g in gene_names if g in hits][:10 ],
})
df = pd.DataFrame(results).sort_values("p_value" ).reset_index(drop=True )
from statsmodels.stats.multitest import multipletests
if len (df) > 0 :
_, fdr, _, _ = multipletests(df["p_value" ], method="fdr_bh" )
df["fdr" ] = fdr
print (f"GSEA complete: {len (ranked_genes)} ranked genes, "
f"{len (results)} pathways, "
f"{(df['fdr' ] < 0.05 ).sum () if 'fdr' in df.columns else 0 } significant" )
return df
3. KEGG パスウェイマッピング def kegg_pathway_analysis (gene_list, organism="hsa" ):
"""
KEGG REST API によるパスウェイマッピング。
Parameters:
gene_list: list — 遺伝子シンボルまたは KEGG gene ID リスト
organism: str — KEGG 生物種コード (hsa, mmu, etc.)
"""
import requests
base_url = "https://rest.kegg.jp"
gene_pathway_map = {}
for gene in gene_list:
url = f"{base_url} /link/pathway/{organism} :{gene} "
resp = requests.get(url)
if resp.status_code == 200 and resp.text.strip():
for line in resp.text.strip().split("\n" ):
parts = line.strip().split("\t" )
if len (parts) == 2 :
pw = parts[1 ].replace("path:" , "" )
gene_pathway_map.setdefault(pw, []).append(gene)
results = []
for pw_id, genes in gene_pathway_map.items():
info_url = f"{base_url} /get/{pw_id} "
resp = requests.get(info_url)
name = pw_id
if resp.status_code == 200 :
for line in resp.text.split("\n" ):
if line.startswith("NAME" ):
name = line.replace("NAME" , "" ).strip()
break
results.append({
"pathway_id" : pw_id,
"pathway_name" : name,
"gene_count" : len (genes),
"genes" : genes,
})
df = pd.DataFrame(results).sort_values("gene_count" , ascending=False )
print (f"KEGG mapping: {len (gene_list)} genes → {len (df)} pathways" )
return df
4. Reactome パスウェイ階層解析 def reactome_enrichment (gene_list, species="Homo sapiens" ,
p_cutoff=0.05 , include_interactors=False ):
"""
Reactome REST API による富化解析。
Parameters:
gene_list: list — UniProt ID または遺伝子シンボル
species: 種名
p_cutoff: 有意水準
"""
import requests
url = "https://reactome.org/AnalysisService/identifiers/"
payload = "\n" .join(gene_list)
headers = {"Content-Type" : "text/plain" }
params = {
"interactors" : str (include_interactors).lower(),
"species" : species,
"sortBy" : "ENTITIES_PVALUE" ,
"order" : "ASC" ,
"resource" : "TOTAL" ,
}
resp = requests.post(url, data=payload, headers=headers, params=params)
data = resp.json()
pathways = data.get("pathways" , [])
results = []
for pw in pathways:
entities = pw.get("entities" , {})
results.append({
"pathway_id" : pw.get("stId" , "" ),
"pathway_name" : pw.get("name" , "" ),
"found" : entities.get("found" , 0 ),
"total" : entities.get("total" , 0 ),
"ratio" : round (entities.get("ratio" , 0 ), 4 ),
"p_value" : entities.get("pValue" , 1.0 ),
"fdr" : entities.get("fdr" , 1.0 ),
"species" : pw.get("species" , {}).get("name" , "" ),
})
df = pd.DataFrame(results)
sig = df[df["fdr" ] < p_cutoff] if len (df) > 0 else df
print (f"Reactome enrichment: {len (gene_list)} genes → "
f"{len (sig)} significant pathways (FDR < {p_cutoff} )" )
return df
5. Gene Ontology アノテーション・富化 def go_enrichment (gene_list, background_genes=None ,
ontology="BP" , organism="human" ,
method="fisher" , fdr_cutoff=0.05 ):
"""
Gene Ontology 富化解析 (goatools / gseapy 代替)。
Parameters:
gene_list: list — 注目遺伝子リスト
ontology: "BP" (Biological Process), "MF" (Molecular Function),
"CC" (Cellular Component)
method: "fisher" or "chi2"
"""
import requests
results = []
url = "https://www.ebi.ac.uk/QuickGO/services/annotation/search"
batch_size = 100
for i in range (0 , len (gene_list), batch_size):
batch = gene_list[i:i + batch_size]
params = {
"geneProductId" : "," .join(batch),
"aspect" : ontology,
"taxonId" : "9606" if organism == "human" else organism,
"limit" : 200 ,
}
resp = requests.get(url, params=params, headers={"Accept" : "application/json" })
if resp.status_code == 200 :
data = resp.json()
for result in data.get("results" , []):
results.append({
"gene" : result.get("geneProductId" , "" ),
"go_id" : result.get("goId" , "" ),
"go_name" : result.get("goName" , "" ),
"evidence" : result.get("goEvidence" , "" ),
"aspect" : result.get("goAspect" , "" ),
})
df = pd.DataFrame(results)
if len (df) > 0 :
term_counts = df.groupby(["go_id" , "go_name" ]).size().reset_index(name="count" )
term_counts = term_counts.sort_values("count" , ascending=False )
print (f"GO {ontology} enrichment: {len (gene_list)} genes → "
f"{len (term_counts)} unique GO terms" )
return term_counts
return df
6. 統合富化ヒートマップ def integrated_enrichment_heatmap (ora_results_dict, top_n=20 ,
fdr_cutoff=0.05 ,
output="figures/enrichment_heatmap.png" ):
"""
複数 DB の富化結果を統合ヒートマップで可視化。
Parameters:
ora_results_dict: dict — {"KEGG": df, "Reactome": df, "GO_BP": df}
top_n: 表示パスウェイ数
fdr_cutoff: 有意水準
"""
import matplotlib.pyplot as plt
import os
os.makedirs(os.path.dirname(output), exist_ok=True )
fig, axes = plt.subplots(1 , len (ora_results_dict),
figsize=(6 * len (ora_results_dict), 10 ))
if len (ora_results_dict) == 1 :
axes = [axes]
for ax, (db_name, df) in zip (axes, ora_results_dict.items()):
sig = df[df["fdr" ] < fdr_cutoff].head(top_n)
if len (sig) == 0 :
ax.set_title(f"{db_name} \n(no significant)" )
continue
neg_log_fdr = -np.log10(sig["fdr" ].clip(lower=1e-50 ))
ax.barh(range (len (sig)), neg_log_fdr, color="steelblue" )
ax.set_yticks(range (len (sig)))
ax.set_yticklabels(sig["pathway" ].str [:50 ], fontsize=8 )
ax.set_xlabel("-log10(FDR)" )
ax.set_title(f"{db_name} (top {len (sig)} )" )
ax.axvline(-np.log10(fdr_cutoff), color="red" , ls="--" , alpha=0.5 )
ax.invert_yaxis()
plt.tight_layout()
plt.savefig(output, dpi=300 , bbox_inches="tight" )
plt.close()
print (f"Saved: {output} " )
return output
References
Output Files ファイル 形式 results/ora_results.csvCSV results/gsea_results.csvCSV results/kegg_pathways.csvCSV results/reactome_enrichment.csvCSV results/go_enrichment.csvCSV figures/enrichment_heatmap.pngPNG figures/gsea_running_sum.pngPNG
利用可能ツール カテゴリ 主要ツール 用途 KEGG kegg_search_pathwayパスウェイキーワード検索 KEGG kegg_get_pathway_infoパスウェイ詳細情報取得 KEGG kegg_find_genes遺伝子検索 KEGG kegg_get_gene_info遺伝子詳細情報 KEGG kegg_list_organisms対応生物種一覧 Reactome Reactome_get_pathwayパスウェイ詳細取得 Reactome Reactome_get_pathway_hierarchyパスウェイ階層構造 Reactome Reactome_get_pathway_reactionsパスウェイ内反応一覧 Reactome Reactome_map_uniprot_to_pathwaysUniProt→パスウェイ変換 Reactome Reactome_map_uniprot_to_reactionsUniProt→反応変換 Reactome Reactome_get_pathways_low_entity低レベルパスウェイ Reactome Reactome_list_top_pathwaysトップレベルパスウェイ一覧 Reactome Reactome_list_species対応種一覧 Reactome Reactome_get_event_ancestorsイベント祖先取得 Reactome Reactome_get_events_hierarchyイベント階層 Reactome Reactome_get_participant_reference_entities参加エンティティ Reactome Reactome_get_participants参加要素取得 Reactome Reactome_get_complex複合体情報 Reactome Reactome_get_diseases疾患関連パスウェイ Reactome Reactome_get_interactor相互作用パートナー Reactome Reactome_query_by_idsバッチ ID クエリ Reactome Reactome_get_reaction反応詳細 Reactome Reactome_get_entity_compartmentエンティティ局在 Reactome Reactome_get_entity_eventsエンティティ関連イベント Reactome Reactome_get_database_versionDB バージョン確認 GO GO_search_termsGO 用語検索 GO GO_get_term_by_idGO ID → 用語情報 GO GO_get_term_details用語詳細 (定義/関係) GO GO_get_annotations_for_gene遺伝子 GO アノテーション GO GO_get_genes_for_termGO term → 遺伝子リスト WikiPathways WikiPathways_searchコミュニティパスウェイ検索 WikiPathways WikiPathways_get_pathwayパスウェイ詳細取得 Pathway Commons pc_search_pathways統合パスウェイ検索 Pathway Commons pc_get_interactions分子間相互作用取得
参照スキル スキル 関連 scientific-gene-expression-transcriptomicsDEG リスト → ORA/GSEA scientific-proteomics-mass-spectrometry差次タンパク質 → パスウェイ scientific-metabolomics代謝物 → パスウェイマッピング scientific-single-cell-genomicsscRNA-seq マーカー → GO 富化 scientific-multi-omicsマルチオミクス富化統合 scientific-network-analysisパスウェイ → ネットワーク scientific-systems-biologyFBA/GRN → パスウェイ統合
依存パッケージ scipy, statsmodels, pandas, numpy, matplotlib, requests, gseapy (optional)
Related occupations SOC
Based on SOC occupation classification
