tooluniverse-multi-omics-integration

Q: How do I install tooluniverse-multi-omics-integration?

Run npx skills add https://github.com/mims-harvard/tooluniverse --skill tooluniverse-multi-omics-integration in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/tooluniverse-multi-omics-integration/ (personal) or .claude/skills/tooluniverse-multi-omics-integration/ (project).

by mims-harvardEducation & Science

Multi-omics integration — orchestrate per-layer analysis (transcriptomics, proteomics, epigenomics, genomics, metabolomics) then perform cross-omics correlation, multi-omics clustering, and pathway-level integration. Use for integrative systems-biology analysis, multi-modal disease characterization, and cross-omics biomarker discovery.

Installs: 399
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Install

Works with every agent the skills CLI supports.

npx skills add https://github.com/mims-harvard/tooluniverse --skill tooluniverse-multi-omics-integration

01
Install
Run the command in your project. The CLI asks which agents to install for.
02
Describe the task
Ask your agent for something this skill covers. It loads tooluniverse-multi-omics-integration automatically when relevant.
03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.

SKILL.md

The instructions your agent reads when the skill is triggered.

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.

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.

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

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

See: phase_details.md for complete code and implementation details.

Supported Data Types

Omics	Formats	QC Focus
Transcriptomics	CSV/TSV, HDF5, h5ad	Low-count filter, normalize (TPM/DESeq2), log-transform
Proteomics	MaxQuant, Spectronaut, DIA-NN	Missing value imputation, median/quantile normalization
Methylation	IDAT, beta matrices	Failed probes, batch correction, cross-reactive filter
Genomics	VCF, SEG (CNV)	Variant QC, CNV segmentation
Metabolomics	Peak tables	Missing values, normalization

Core Operations

Sample Matching

def match_samples_across_omics(omics_data_dict):
    """Match samples across multiple omics datasets."""
    sample_ids = {k: set(df.columns) for k, df in omics_data_dict.items()}
    common_samples = set.intersection(*sample_ids.values())
    matched_data = {k: df[sorted(common_samples)] for k, df in omics_data_dict.items()}
    return sorted(common_samples), matched_data

Cross-Omics Correlation

from scipy.stats import spearmanr, pearsonr

# RNA vs Protein: expect positive r ~ 0.4-0.6
# Methylation vs Expression: expect negative r (promoter repression)
# CNV vs Expression: expect positive r (dosage effect)

for gene in common_genes:
    r, p = spearmanr(rna[gene], protein[gene])

Pathway Integration

# Score pathway dysregulation using combined evidence from all omics
# Aggregate per-gene evidence, then per-pathway
pathway_score = mean(abs(rna_fc) + abs(protein_fc) + abs(meth_diff) + abs(cnv))

See: phase_details.md for full implementations of each operation.

Multi-Omics Clustering Methods

Method	Description	Best For
MOFA+	Latent factors explaining cross-omics variation	Identifying shared/omics-specific drivers
Joint NMF	Shared decomposition across omics	Patient subtype discovery
SNF	Similarity network fusion	Integrating heterogeneous data types

ToolUniverse Skills Coordination

Skill	Used For	Phase
`tooluniverse-rnaseq-deseq2`	RNA-seq analysis	1, 4
`tooluniverse-epigenomics`	Methylation, ChIP-seq	1, 4
`tooluniverse-variant-analysis`	CNV/SNV processing	1, 3, 4
`tooluniverse-protein-interactions`	Protein network context	6
`tooluniverse-gene-enrichment`	Pathway enrichment	6
`tooluniverse-expression-data-retrieval`	Public data retrieval	1
`tooluniverse-target-research`	Gene/protein annotation	3, 8

Use Cases

Cancer Multi-Omics

Integrate TCGA RNA-seq + proteomics + methylation + CNV to identify patient subtypes, cross-omics driver genes, and multi-omics biomarkers.

eQTL + Expression + Methylation

Identify SNP -> methylation -> expression regulatory chains (mediation analysis).

Drug Response Multi-Omics

Predict drug response using baseline multi-omics profiles; identify resistance/sensitivity pathways.

See: phase_details.md "Use Cases" for detailed step-by-step workflows.

Quantified Minimums

Component	Requirement
Omics types	At least 2 datasets
Common samples	At least 10 across omics
Cross-correlation	Pearson/Spearman computed
Clustering	At least one method (MOFA+, NMF, or SNF)
Pathway integration	Enrichment with multi-omics evidence scores
Report	Summary, correlations, clusters, pathways, biomarkers

Limitations

Sample size: n >= 20 recommended for integration
Missing data: Pairwise integration if not all samples have all omics
Batch effects: Different platforms require careful normalization
Computational: Large datasets may require significant memory
Interpretation: Results require domain expertise for validation

References

MOFA+: https://doi.org/10.1186/s13059-020-02015-1
Similarity Network Fusion: https://doi.org/10.1038/nmeth.2810
Multi-omics review: https://doi.org/10.1038/s41576-019-0093-7
See individual ToolUniverse skill documentation for omics-specific methods

Detailed Reference

phase_details.md - Complete code for all phases, correlation functions, clustering, pathway integration, biomarker discovery, report template, and detailed use cases

FAQ

What does the tooluniverse-multi-omics-integration skill do?

How do I install tooluniverse-multi-omics-integration?

Run npx skills add https://github.com/mims-harvard/tooluniverse --skill tooluniverse-multi-omics-integration in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/tooluniverse-multi-omics-integration/ (personal) or .claude/skills/tooluniverse-multi-omics-integration/ (project).

Which AI agents can use tooluniverse-multi-omics-integration?

Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.

Is tooluniverse-multi-omics-integration safe to install?

No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.

Who maintains tooluniverse-multi-omics-integration?

It is published by mims-harvard in mims-harvard/tooluniverse. It has 399 recorded installs.

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tooluniverse-multi-omics-integration

Install

Install

Describe the task

Review the result

SKILL.md

Multi-Omics Integration

Domain Reasoning

LOOK UP DON'T GUESS

When to Use This Skill

Workflow Overview

Supported Data Types

Core Operations

Sample Matching

Cross-Omics Correlation

Pathway Integration

Multi-Omics Clustering Methods

ToolUniverse Skills Coordination

Use Cases

Cancer Multi-Omics

eQTL + Expression + Methylation

Drug Response Multi-Omics

Quantified Minimums

Limitations

References

Detailed Reference

FAQ

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