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

---

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

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

2. Pathway Mapping

Supported databases:

3. Cross-Validation Methods

#### 3.1 Directional Consistency Validation

#### 3.2 Correlation Validation

#### 3.3 Pathway Enrichment Concordance

#### 3.4 Network Topology Validation

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

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

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

Prerequisites

# Python dependencies
pip install -r requirements.txt

Evaluation Criteria

Success Metrics

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

- 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 | |