| name | scientific-multi-omics |
| description | マルチオミクス統合解析スキル。ゲノム・トランスクリプトーム・プロテオーム・メタボローム
データの統合手法（MOFA/SNF/DIABLO）、オミクス間相関解析、CCA/PLS 統合、
パスウェイレベル統合、ネットワーク統合のテンプレートを提供。
|
Scientific Multi-Omics Integration
複数のオミクスレイヤー（ゲノミクス、トランスクリプトミクス、プロテオミクス、
メタボロミクス）のデータを統合的に解析するためのスキル。各オミクスの個別解析は
それぞれの専門スキル（bioinformatics, metabolomics）に委ねつつ、本スキルは
統合に特化する。
When to Use
- 同一サンプルから得られた複数オミクスデータを統合するとき
- オミクス間の相関構造を解明したいとき
- マルチオミクスバイオマーカー発見が必要なとき
- パスウェイレベルでの統合解析が必要なとき
Quick Start
1. データ整合性チェック
import numpy as np
import pandas as pd
def check_multiomics_alignment(omics_dict, sample_col="Sample_ID"):
"""
マルチオミクスデータの整合性チェック。
Parameters:
omics_dict: {"transcriptomics": df, "proteomics": df, "metabolomics": df}
"""
all_samples = {}
for name, df in omics_dict.items():
all_samples[name] = set(df[sample_col])
common = set.intersection(*all_samples.values())
report = {
"n_omics_layers": len(omics_dict),
"layers": list(omics_dict.keys()),
"samples_per_layer": {k: len(v) for k, v in all_samples.items()},
"common_samples": len(common),
"features_per_layer": {k: df.shape[1] - 1 for k, df in omics_dict.items()},
}
aligned = {}
for name, df in omics_dict.items():
aligned[name] = df[df[sample_col].isin(common)].sort_values(sample_col).reset_index(drop=True)
print(f"=== Multi-Omics Alignment ===")
print(f"Common samples: {len(common)} / {max(report['samples_per_layer'].values())}")
for k, v in report["features_per_layer"].items():
print(f" {k}: {v} features")
return aligned, report
2. オミクス間相関解析
from scipy.stats import spearmanr
def cross_omics_correlation(omics1_df, omics2_df, name1="Omics1", name2="Omics2",
top_n=50, method="spearman"):
"""
2 つのオミクスレイヤー間の特徴量ペアワイズ相関を計算する。
Parameters:
omics1_df: DataFrame (samples × features), no sample_col
omics2_df: DataFrame (samples × features), no sample_col
top_n: 上位の強い相関ペアを返す数
"""
features1 = omics1_df.columns.tolist()
features2 = omics2_df.columns.tolist()
correlations = []
for f1 in features1:
for f2 in features2:
if method == "spearman":
r, p = spearmanr(omics1_df[f1], omics2_df[f2])
else:
from scipy.stats import pearsonr
r, p = pearsonr(omics1_df[f1], omics2_df[f2])
correlations.append({
f"{name1}_feature": f1,
f"{name2}_feature": f2,
"correlation": r,
"p_value": p,
"abs_correlation": abs(r),
})
corr_df = pd.DataFrame(correlations).sort_values("abs_correlation", ascending=False)
return corr_df.head(top_n)
3. CCA (Canonical Correlation Analysis)
from sklearn.cross_decomposition import CCA
def canonical_correlation_analysis(X1, X2, n_components=2):
"""
正準相関分析: 2 つのオミクスデータ間の最大相関方向を見つける。
Returns:
cca_model, scores_X1, scores_X2, canonical_correlations
"""
cca = CCA(n_components=n_components)
scores_X1, scores_X2 = cca.fit_transform(X1, X2)
canonical_corrs = []
for i in range(n_components):
r = np.corrcoef(scores_X1[:, i], scores_X2[:, i])[0, 1]
canonical_corrs.append(r)
return cca, scores_X1, scores_X2, canonical_corrs
def plot_cca_scores(scores_X1, scores_X2, labels, name1="Omics1",
name2="Omics2", figsize=(12, 5)):
"""CCA スコアプロットを描画する。"""
import matplotlib.pyplot as plt
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=figsize)
unique_labels = np.unique(labels)
colors = plt.cm.Set1(np.linspace(0, 0.5, len(unique_labels)))
for color, label in zip(colors, unique_labels):
mask = labels == label
ax1.scatter(scores_X1[mask, 0], scores_X1[mask, 1],
c=[color], label=label, alpha=0.7, edgecolors="black")
ax2.scatter(scores_X2[mask, 0], scores_X2[mask, 1],
c=[color], label=label, alpha=0.7, edgecolors=)
ax1.set_title(, fontweight=)
ax1.set_xlabel(); ax1.set_ylabel()
ax2.set_title(, fontweight=)
ax2.set_xlabel(); ax2.set_ylabel()
ax1.legend(); ax2.legend()
plt.tight_layout()
plt.savefig(, dpi=, bbox_inches=)
plt.close()
4. SNF (Similarity Network Fusion)
def similarity_network_fusion(omics_list, k_neighbors=20, n_iterations=20):
"""
Similarity Network Fusion — 複数オミクスの類似度ネットワークを融合する。
Wang et al., Nature Methods 2014
Parameters:
omics_list: list of np.arrays [(n_samples, p1), (n_samples, p2), ...]
k_neighbors: KNN のK
n_iterations: 融合反復回数
Returns:
fused_similarity: (n_samples, n_samples) 融合類似度行列
"""
from sklearn.metrics import pairwise_distances
n = omics_list[0].shape[0]
n_views = len(omics_list)
similarities = []
for X in omics_list:
D = pairwise_distances(X, metric="euclidean")
mu = np.mean(np.sort(D, axis=1)[:, 1:k_neighbors+1], axis=1)
S = np.exp(-D**2 / (mu[:, None] * mu[None, :] + 1e-10))
np.fill_diagonal(S, 0)
S = S / (S.sum(axis=1, keepdims=True) + 1e-10)
similarities.append(S)
knn_masks = []
for X in omics_list:
D = pairwise_distances(X, metric="euclidean")
mask = np.zeros_like(D, dtype=bool)
for i in range(n):
nn = np.argsort(D[i])[:k_neighbors+1]
mask[i, nn] = True
knn_masks.append(mask)
P = [s.copy() for s similarities]
_ (n_iterations):
P_new = []
v (n_views):
other_avg = np.mean([P[j] j (n_views) j != v], axis=)
S_local = similarities[v] * knn_masks[v]
S_local = S_local / (S_local.(axis=, keepdims=) + )
P_updated = S_local @ other_avg @ S_local.T
P_updated = P_updated / (P_updated.(axis=, keepdims=) + )
P_new.append(P_updated)
P = P_new
fused = np.mean(P, axis=)
fused = (fused + fused.T) /
fused
5. パスウェイレベル統合
def pathway_level_integration(omics_dict, pathway_mapping, pathway_col="Pathway",
feature_col="Feature"):
"""
パスウェイレベルでオミクスデータを統合する。
各パスウェイの活性スコアを PCA 第一主成分で算出。
Parameters:
omics_dict: {"transcriptomics": df, "proteomics": df}
pathway_mapping: DataFrame (Feature, Pathway, Omics_Layer)
"""
from sklearn.decomposition import PCA
pathway_scores = {}
pathways = pathway_mapping[pathway_col].unique()
for pw in pathways:
pw_features = pathway_mapping[pathway_mapping[pathway_col] == pw]
combined_data = []
for omics_name, df in omics_dict.items():
features_in_omics = pw_features[pw_features["Omics_Layer"] == omics_name][feature_col]
available = [f for f in features_in_omics if f in df.columns]
if available:
combined_data.append(df[available].values)
if combined_data:
X = np.hstack(combined_data)
if X.shape[1] >= 2:
pca = PCA(n_components=1)
score = pca.fit_transform(X).ravel()
pathway_scores[pw] = {
"score": score,
"explained_variance": pca.explained_variance_ratio_[0],
"n_features": X.shape[1],
}
return pathway_scores
6. マルチオミクスクラスタリング
from sklearn.cluster import SpectralClustering
def multiomics_clustering(fused_similarity, n_clusters, labels_true=None):
"""融合類似度行列に基づくスペクトラルクラスタリング。"""
from sklearn.metrics import adjusted_rand_score, normalized_mutual_info_score
clustering = SpectralClustering(n_clusters=n_clusters, affinity="precomputed",
random_state=42)
cluster_labels = clustering.fit_predict(fused_similarity)
metrics = {"n_clusters": n_clusters}
if labels_true is not None:
metrics["ARI"] = adjusted_rand_score(labels_true, cluster_labels)
metrics["NMI"] = normalized_mutual_info_score(labels_true, cluster_labels)
return cluster_labels, metrics
References
Output Files
| ファイル | 形式 |
|---|
results/cross_omics_correlation.csv | CSV |
results/canonical_correlations.csv | CSV |
results/pathway_activity_scores.csv | CSV |
results/multiomics_clusters.csv | CSV |
figures/cca_scores.png | PNG |
figures/snf_heatmap.png | PNG |
figures/multiomics_umap.png | PNG |
利用可能ツール
ToolUniverse SMCP 経由で利用可能な外部ツール。
| カテゴリ | 主要ツール | 用途 |
|---|
| HPA | HPA_get_rna_expression_by_source | 組織別 RNA 発現 |
| GEO | geo_search_datasets | オミクスデータセット検索 |
| CELLxGENE | CELLxGENE_get_expression_data | 単一細胞発現データ |
| Reactome | Reactome_map_uniprot_to_pathways | パスウェイマッピング |
| KEGG | kegg_get_pathway_info | パスウェイ情報 |
| UniProt | UniProt_search | タンパク質検索 |
依存パッケージ
scikit-learn>=1.3
scipy>=1.10
