multi-omics-integration-strategist

# 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) ```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) ```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) ```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`) ```markdown # 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 ```bash python scripts/main.py \ --rna rna_data.csv \ --pro pro_data.csv \ --met met_data.csv \ --output ./results ``` ### Advanced Options ```bash 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 ```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 ```bash # 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 | |