什么是 多组学集成策略家？

多组学集成策略家是 Openclaw Skills 库中的一个复杂分析组件，专为研究人员和生物信息学家设计。它有助于设计 RNA、蛋白质和代谢物数据的联合分析方案，从而提供生物系统的全面视图。通过在研究中使用 Openclaw Skills，您可以自动化跨组学验证和通路级集成的复杂过程。

该技能擅长在 KEGG 和 Reactome 等各种数据库中映射不同的数据类型，为理解疾病机制和生物通路提供统一策略。它充当原始表达数据与高级系统生物学洞察之间的桥梁，确保您的多组学发现具有统计稳健性和生物学相关性。

下载入口:https://github.com/openclaw/skills/tree/main/skills/aipoch-ai/multi-omics-integration-strategist

安装与下载

1. ClawHub CLI

从源直接安装技能的最快方式。

2. 手动安装

将技能文件夹复制到以下位置之一

优先级：工作区 > 本地 > 内置

3. 提示词安装

将此提示词复制到 OpenClaw 即可自动安装。

多组学集成策略家 应用场景

• 用于研究复杂人类疾病的系统生物学机制研究
• 临床应用中多组学标志物的发现与验证
• 通过集成通路验证识别药物靶点
• 不同组学数据集之间的质量评估和一致性分析

1. 摄取包含表达和差异结果的 RNA、蛋白质组学和代谢组学 CSV 文件。
2. 执行 ID 映射层，链接 Gene Symbols、UniProt IDs 和 KEGG 代谢物标识符。
3. 针对包括 KEGG、Reactome 和 WikiPathways 在内的标准数据库进行通路映射。
4. 涉及方向一致性、相关性分析和富集一致性的多维交叉验证。
5. 生成综合集成报告和用于下游可视化的网络边列表。

多组学集成策略家 配置指南

要开始使用此技能，请确保已安装 Python 3.8+，然后使用以下命令设置环境：

# Install required dependencies
pip install pandas numpy scipy scikit-learn networkx matplotlib seaborn gseapy

# Run the integration strategist
python scripts/main.py --rna rna_data.csv --pro pro_data.csv --met met_data.csv --output ./results

多组学集成策略家 数据架构与分类体系

该技能需要结构化的 CSV 输入并生成多个分析输出。通过 Openclaw Skills 进行的这种集成确保了标准化的数据分类：

主要输出：

• mapped_ids.json: 不同组学 ID 之间的全面映射。
• pathway_scores.csv: 生物通路的定量交叉验证得分。
• report.html: 集成分析结果的交互式摘要。

name: multi-omics-integration-strategist
description: Design multi-omics integration strategies for transcriptomics, proteomics,
  and metabolomics data analysis
version: 1.0.0
category: Bioinfo
tags: []
author: AIPOCH
license: MIT
status: Draft
risk_level: Medium
skill_type: Tool/Script
owner: AIPOCH
reviewer: ''
last_updated: '2026-02-06'

Skill: Multi-Omics Integration Strategist (ID: 204)

Overview

Designs multi-omics (transcriptomics RNA, proteomics Pro, metabolomics Met) joint analysis schemes, performs cross-validation at the pathway level, and provides systems biology-level integrated analysis strategies.

Use Cases

• Systems biology mechanism research for complex diseases
• Biomarker discovery and validation
• Drug target identification and pathway validation
• Multi-omics data quality assessment and consistency analysis

Directory Structure

.
├── SKILL.md                 # This file - Skill documentation
├── config/
│   └── pathways.json        # Pathway database configuration
├── scripts/
│   └── main.py             # Main analysis script
├── templates/
│   └── report_template.md   # Analysis report template
└── examples/
    └── sample_data/         # Sample datasets

Input

Required Files

Input Format Specifications

RNA Data (rna_data.csv)

gene_id,gene_name,log2fc,pvalue,padj,sample_A,sample_B,...
ENSG00000139618,BRCA1,1.23,0.001,0.005,12.5,13.2,...

Protein Data (pro_data.csv)

protein_id,gene_name,log2fc,pvalue,padj,sample_A,sample_B,...
P38398,BRCA1,0.85,0.002,0.008,2450,2890,...

Metabolite Data (met_data.csv)

metabolite_id,metabolite_name,kegg_id,log2fc,pvalue,padj,...
C00187,Cholesterol,C00187,-1.45,0.003,0.012,...

Integration Strategy

1. ID Mapping Layer

• RNA → Protein: Mapping through Gene Symbol / UniProt ID
• Protein → Metabolite: Association through KEGG/Reactome enzyme-reaction-metabolite
• RNA → Metabolite: Indirect association through KEGG pathway

2. Pathway Mapping

Supported databases:

• KEGG (Kyoto Encyclopedia of Genes and Genomes)
• Reactome
• WikiPathways
• GO (Gene Ontology) - Biological Process

3. Cross-Validation Methods

3.1 Directional Consistency Validation

• Whether the change direction of genes/proteins/metabolites in the same pathway is consistent
• Score: +1 (consistent), -1 (opposite), 0 (no data)

3.2 Correlation Validation

• Pearson/Spearman correlation analysis
• Cross-omics expression profile clustering

3.3 Pathway Enrichment Concordance

• Independent enrichment analysis for each omics
• Common enriched pathway identification

3.4 Network Topology Validation

• Construct cross-omics regulatory network
• Identify key nodes (Hub genes/proteins/metabolites)

Output

1. Integration Report (integration_report.md)

# Multi-Omics Integration Analysis Report

## Executive Summary
- Sample count: RNA=30, Pro=28, Met=25
- Mapping success rate: RNA-Pro=85%, Pro-Met=62%
- Pathway coverage: 342 KEGG pathways

## Cross-Validation Results
### Highly Consistent Pathways (Score > 0.8)
1. Glycolysis/Gluconeogenesis (Score=0.92)
2. Citrate cycle (TCA cycle) (Score=0.88)

### Conflicting Pathways (Score < -0.3)
1. Fatty acid biosynthesis (Score=-0.45)

## Recommendations
- Focus on: Energy metabolism-related pathways
- Needs verification: Lipid metabolism pathway data quality

2. External Visualization Tools (Not Included)

This tool generates analysis results that can be visualized using external tools. Users may export results to:

Note: This skill focuses on data integration and analysis. Visualization requires separate installation of plotting libraries by the user.

3. Output Files

Usage

Basic Usage

python scripts/main.py r
  --rna rna_data.csv r
  --pro pro_data.csv r
  --met met_data.csv r
  --output ./results

Advanced Options

python scripts/main.py r
  --rna rna_data.csv r
  --pro pro_data.csv r
  --met met_data.csv r
  --pathway-db KEGG,Reactome r
  --id-mapping config/mapping.json r
  --method correlation+enrichment+network r
  --output ./results r
  --format html,csv,json

Configuration

config/pathways.json

{
  "databases": {
    "KEGG": {
      "enabled": true,
      "organism": "hsa",
      "min_genes": 3
    },
    "Reactome": {
      "enabled": true,
      "min_genes": 5
    }
  },
  "mapping": {
    "rna_to_protein": "gene_symbol",
    "protein_to_metabolite": "enzyme_commission"
  }
}

Dependencies

• Python >= 3.8
• pandas >= 1.3.0
• numpy >= 1.21.0
• scipy >= 1.7.0
• scikit-learn >= 1.0.0
• networkx >= 2.6.0
• matplotlib >= 3.4.0
• seaborn >= 0.11.0
• gseapy >= 1.0.0 (Pathway enrichment analysis)

References

1. Subramanian et al. (2005) PNAS - GSEA method
2. Kamburov et al. (2011) NAR - ConsensusPathDB
3. Chin et al. (2018) Nature Communications - Multi-omics integration methods review

Version

• Version: 1.0.0
• Last Updated: 2026-02-06
• Author: OpenClaw Bioinformatics Team

Risk Assessment

Security Checklist

• No hardcoded credentials or API keys
• No unauthorized file system access (../)
• Output does not expose sensitive information
• Prompt injection protections in place
• Input file paths validated (no ../ traversal)
• Output directory restricted to workspace
• Script execution in sandboxed environment
• Error messages sanitized (no stack traces exposed)
• Dependencies audited

Prerequisites

# Python dependencies
pip install -r requirements.txt

Evaluation Criteria

Success Metrics

• Successfully executes main functionality
• Output meets quality standards
• Handles edge cases gracefully
• Performance is acceptable

Test Cases

1. Basic Functionality: Standard input → Expected output
2. Edge Case: Invalid input → Graceful error handling
3. Performance: Large dataset → Acceptable processing time

Lifecycle Status

• Current Stage: Draft
• Next Review Date: 2026-03-06
• Known Issues: None
• Planned Improvements:
  • Performance optimization
  • Additional feature support

Parameters