# Scientific Lipidomics > リピドミクス解析スキル。LipidMAPS / SwissLipids / LION 脂質データベース統合検索・脂質サブクラス分類・ 脂質 MS/MS スペクトル同定・脂質パスウェイエンリッチメント・ 脂質プロファイリングパイプライン。 TU 外スキル (直接 REST API + Python ライブラリ)。 - Skill: `nahisaho/scientific-lipidomics-2` (Agent Skill) - Install (CLI): `npx skillmds@latest add nahisaho/scientific-lipidomics-2` - Raw SKILL.md: https://api.skillmd.com/api/skills/nahisaho/scientific-lipidomics-2/raw - Safety review: pending (external: skill-scanner PASS, skillspector PASS) - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Integrations & APIs - Author: nahisaho (https://skillmd.com/u/nahisaho) - Updated: 2026-09-21 - Page: https://skillmd.com/skills/nahisaho/scientific-lipidomics-2 --- # Scientific Lipidomics LipidMAPS / SwissLipids / LION 脂質データベースを統合した 脂質構造検索・サブクラス分類・MS/MS 同定・ 脂質パスウェイエンリッチメント解析パイプラインを提供する。 ## When to Use - LC-MS/MS リピドミクスデータの脂質同定を行うとき - LipidMAPS で脂質構造・サブクラスを検索するとき - 脂質プロファイルの差次解析 (fold change/p-value) を行うとき - LION エンリッチメントで脂質機能解析を行うとき - 脂質パスウェイ (スフィンゴ脂質/リン脂質代謝) を可視化するとき --- ## Quick Start ## 1. LipidMAPS 構造検索 ```python import requests import pandas as pd LIPIDMAPS_API = "https://www.lipidmaps.org/rest" def lipidmaps_search(name=None, formula=None, mass=None, tolerance=0.01): """ LipidMAPS — 脂質構造検索。 Parameters: name: str | None — 脂質名 (部分一致) formula: str | None — 分子式 mass: float | None — 精密質量 tolerance: float — 質量誤差 (Da) """ if mass is not None: url = (f"{LIPIDMAPS_API}/compound/lm_id/" f"mass/{mass}/{tolerance}") elif name: url = (f"{LIPIDMAPS_API}/compound/lm_id/" f"name/{name}") elif formula: url = (f"{LIPIDMAPS_API}/compound/lm_id/" f"formula/{formula}") else: print("LipidMAPS: provide name, formula, " "or mass") return pd.DataFrame() resp = requests.get(url, timeout=30) resp.raise_for_status() data = resp.json() if isinstance(data, dict): data = [data] rows = [] for item in data: rows.append({ "lm_id": item.get("lm_id", ""), "name": item.get("name", ""), "sys_name": item.get( "systematic_name", ""), "formula": item.get("formula", ""), "mass": item.get("mass", 0), "main_class": item.get( "main_class", ""), "sub_class": item.get("sub_class", ""), }) df = pd.DataFrame(rows) print(f"LipidMAPS: {len(df)} lipids found") return df def lipidmaps_classification(lm_id): """ LipidMAPS — 脂質分類階層取得。 Parameters: lm_id: str — LipidMAPS ID (例: "LMFA01010001") """ url = (f"{LIPIDMAPS_API}/compound/" f"lm_id/{lm_id}/all") resp = requests.get(url, timeout=30) resp.raise_for_status() data = resp.json() classification = { "lm_id": data.get("lm_id", ""), "category": data.get("core", ""), "main_class": data.get("main_class", ""), "sub_class": data.get("sub_class", ""), "class_level4": data.get( "class_level4", ""), "smiles": data.get("smiles", ""), "inchi_key": data.get("inchi_key", ""), } print(f"LipidMAPS: {lm_id} → " f"{classification['main_class']} / " f"{classification['sub_class']}") return classification ``` ## 2. 脂質差次解析 ```python import numpy as np from scipy import stats def lipid_differential_analysis(data, groups, fdr_threshold=0.05): """ 脂質差次解析 (Fold Change + t-test)。 Parameters: data: pd.DataFrame — 脂質濃度行列 (行=サンプル, 列=脂質) groups: list[int] — グループラベル (0 or 1) fdr_threshold: float — FDR 閾値 """ from statsmodels.stats.multitest import ( multipletests) groups = np.array(groups) g0 = data[groups == 0] g1 = data[groups == 1] results = [] for lipid in data.columns: mean0 = g0[lipid].mean() mean1 = g1[lipid].mean() fc = (mean1 / mean0 if mean0 > 0 else np.inf) log2fc = np.log2(fc) if fc > 0 else 0 _, pval = stats.ttest_ind( g0[lipid], g1[lipid]) results.append({ "lipid": lipid, "mean_ctrl": round(mean0, 4), "mean_case": round(mean1, 4), "fold_change": round(fc, 4), "log2FC": round(log2fc, 4), "pvalue": pval, }) df = pd.DataFrame(results) _, fdr, _, _ = multipletests( df["pvalue"], method="fdr_bh") df["fdr"] = fdr df["significant"] = df["fdr"] < fdr_threshold n_sig = df["significant"].sum() n_up = ((df["significant"]) & (df["log2FC"] > 0)).sum() n_down = ((df["significant"]) & (df["log2FC"] < 0)).sum() print(f"Lipid DA: {n_sig} significant " f"({n_up} up, {n_down} down)") return df.sort_values("pvalue") ``` ## 3. 脂質サブクラスエンリッチメント ```python def lipid_subclass_enrichment( sig_lipids, all_lipids, class_map): """ 脂質サブクラスエンリッチメント (Fisher exact)。 Parameters: sig_lipids: list[str] — 有意脂質リスト all_lipids: list[str] — 全脂質リスト class_map: dict — {lipid: subclass} マッピング """ from scipy.stats import fisher_exact sig_set = set(sig_lipids) all_set = set(all_lipids) # サブクラス別集計 subclasses = set(class_map.values()) results = [] for sc in subclasses: sc_all = {l for l, c in class_map.items() if c == sc and l in all_set} sc_sig = sc_all & sig_set a = len(sc_sig) b = len(sig_set) - a c = len(sc_all) - a d = len(all_set) - a - b - c if a == 0: continue _, pval = fisher_exact( [[a, b], [c, d]], alternative="greater") results.append({ "subclass": sc, "sig_in_class": a, "total_in_class": len(sc_all), "pvalue": pval, "ratio": round(a / len(sc_all), 3), }) df = pd.DataFrame(results).sort_values("pvalue") print(f"Subclass enrichment: " f"{(df['pvalue'] < 0.05).sum()} " f"significant subclasses") return df ``` ## 4. リピドミクス統合パイプライン ```python def lipidomics_pipeline(data, groups, output_dir="results"): """ リピドミクス統合パイプライン。 Parameters: data: pd.DataFrame — 脂質濃度行列 groups: list[int] — グループラベル output_dir: str — 出力ディレクトリ """ from pathlib import Path output_dir = Path(output_dir) output_dir.mkdir(parents=True, exist_ok=True) # 1) 差次解析 da = lipid_differential_analysis(data, groups) da.to_csv(output_dir / "lipid_da.csv", index=False) # 2) LipidMAPS アノテーション annotations = [] for lipid in data.columns[:30]: result = lipidmaps_search(name=lipid) if not result.empty: row = result.iloc[0].to_dict() row["query"] = lipid annotations.append(row) if annotations: ann_df = pd.DataFrame(annotations) ann_df.to_csv( output_dir / "lipid_annotations.csv", index=False) print(f"Lipidomics pipeline → {output_dir}") return {"da": da} ``` --- ## パイプライン統合 ``` metabolomics → lipidomics → pathway-enrichment (LC-MS 全代謝物) (脂質特化) (脂質代謝パスウェイ) │ │ ↓ metabolomics-network ─┘ multi-omics (代謝物相関) (オミクス統合) ``` ## パイプライン出力 | ファイル | 説明 | 次スキル | |---------|------|---------| | `results/lipid_da.csv` | 差次脂質 | → biomarker-discovery | | `results/lipid_annotations.csv` | LipidMAPS 注釈 | → pathway-enrichment | ## ToolUniverse 連携 | TU Key | ツール名 | 連携内容 | |--------|---------|--------| | `lipidmaps` | LIPID MAPS | 脂質構造・分類データベース検索 |