tooluniverse-multi-omics-integration

Multi-Omics Integration

Coordinate and integrate multiple omics datasets for comprehensive systems biology analysis. Orchestrates specialized ToolUniverse skills to perform cross-omics correlation, multi-omics clustering, pathway-level integration, and unified interpretation.

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Domain Reasoning

Multi-omics integration asks whether different molecular layers tell a concordant story. If a gene is upregulated in RNA-seq AND its protein is elevated in proteomics, that is concordant evidence of true biological change. Discordance — high mRNA but low protein, or elevated protein without matching mRNA — may indicate post-transcriptional regulation (miRNA silencing, protein degradation, translational control) and is itself a meaningful finding worth reporting. Not every discordance is noise; some are the most interesting biology.

LOOK UP DON'T GUESS

• Expected RNA-protein correlation ranges: compute Spearman r from the actual data; the typical range (0.4-0.6) is a guide, not a guarantee.
• Pathway enrichment results: run ReactomeAnalysis_pathway_enrichment or gseapy on the actual gene lists; never list enriched pathways from memory.
• eQTL associations: query GTEx or eQTL databases for the specific variant and tissue; do not assume regulatory relationships.
• Methylation-expression directionality at specific loci: retrieve experimental data; promoter repression is the canonical model but exceptions exist.

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When to Use This Skill

• User has multiple omics datasets (RNA-seq + proteomics, methylation + expression, etc.)
• Cross-omics correlation queries (e.g., "How does methylation affect expression?")
• Multi-omics biomarker discovery or patient subtyping
• Systems biology questions requiring multiple molecular layers
• Precision medicine applications with multi-omics patient data

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Workflow Overview

Phase 1: Data Loading & QC
  Load each omics type, format-specific QC, normalize
  Supported: RNA-seq, proteomics, methylation, CNV/SNV, metabolomics

Phase 2: Sample Matching
  Harmonize sample IDs, find common samples, handle missing omics

Phase 3: Feature Mapping
  Map features to common gene-level identifiers
  CpG->gene (promoter), CNV->gene, metabolite->enzyme

Phase 4: Cross-Omics Correlation
  RNA vs Protein (translation efficiency)
  Methylation vs Expression (epigenetic regulation)
  CNV vs Expression (dosage effect)
  eQTL variants vs Expression (genetic regulation)

Phase 5: Multi-Omics Clustering
  MOFA+, NMF, SNF for patient subtyping

Phase 6: Pathway-Level Integration
  Aggregate omics evidence at pathway level
  Score pathway dysregulation with combined evidence

Phase 7: Biomarker Discovery
  Feature selection across omics, multi-omics classification

Phase 8: Integrated Report
  Summary, correlations, clusters, pathways, biomarkers