--- 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 | 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 | | --- Source attribution: GeorgeDoors888 Original project: https://github.com/GeorgeDoors888/GB-Power-Market-JJ/blob/main/openclaw-skills/skills/aipoch-ai/multi-omics-integration-strategist/SKILL.md License: MIT License information: https://opensource.org/license/mit Original upstream instructions; downloaded from Mega AI Hub. ## Upstream license notice MIT License Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. # Mega AI Hub resource metadata Creator / publisher: GeorgeDoors888 Original project: https://github.com/GeorgeDoors888/GB-Power-Market-JJ/blob/main/openclaw-skills/skills/aipoch-ai/multi-omics-integration-strategist/SKILL.md Attribution basis: Source repository record Fields of work: Data & Analytics, Education & Research, Product Management Roles: Business Intelligence Analyst, Data Scientist, Market Research Analyst, Product Manager, Researcher Specialties: Specialty not specified Tools: Csv, Python Country applicability: Country not specified Classification basis: Source catalog labels Instruction SHA-256: 0043a2525b0f6c999cbbe6ccced8370e2f80ec0c854e375993938b887a57d92f