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

File	Format	Description
rna_data.csv	CSV	Transcriptomics data: Gene ID, expression value, differential analysis results
pro_data.csv	CSV	Proteomics data: Protein ID, abundance value, differential analysis results
met_data.csv	CSV	Metabolomics data: Metabolite ID, concentration value, differential analysis results

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:

Chart Type	Purpose	External Tool Required
Circos Plot	Cross-omics relationship panorama	matplotlib/circlize (user-installed)
Pathway Heatmap	Pathway-level changes	seaborn/complexheatmap (user-installed)
Sankey Diagram	Data flow mapping	plotly (user-installed)
Network Graph	Molecular interaction network	networkx/cytoscape (networkx is included)
Correlation Matrix	Cross-omics correlation	seaborn (user-installed)
Bubble Plot	Integrated enrichment analysis	ggplot2/plotly (user-installed)

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

3. Output Files

File	Description
mapped_ids.json	ID mapping results
pathway_scores.csv	Pathway cross-validation scores
consistency_matrix.csv	Cross-omics consistency matrix
network_edges.csv	Network edge list
report.html	Interactive HTML report

Usage

Basic Usage

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

Advanced Options

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

Risk Indicator	Assessment	Level
Code Execution	Python/R scripts executed locally	Medium
Network Access	No external API calls	Low
File System Access	Read input files, write output files	Medium
Instruction Tampering	Standard prompt guidelines	Low
Data Exposure	Output files saved to workspace	Low

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

Parameter	Type	Default	Description
--rna	str	Required
--pro	str	Required
--met	str	Required
--output	str	'./results'
--databases	str	'KEGG'
--create-sample	str	Required	Create sample data for testing
--format	str	'md