[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6DATDSAVKV797CWGBTKHTYA":3,"skill-same:01M6DATDSAVKV797CWGBTKHTYA":70},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":16,"categories":17,"keywords":18,"publishedAt":39,"linkOnly":40,"markdownContent":41,"markdownBody":42,"frontMatter":43,"refName":44,"commitSha":45,"skillDirectory":46,"forksCount":14,"repositoryHtmlUrl":47,"repositoryHomepageUrl":6,"ownerHtmlUrl":48,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":49,"install":50,"resources":63},"01M6DATDSAVKV797CWGBTKHTYA","tooluniverse-multi-omics-integration",null,"Integrate and analyze multiple omics datasets (transcriptomics, proteomics, epigenomics, genomics, metabolomics) for systems biology and precision medicine. Performs cross-omics correlation, multi-omics clustering (MOFA+, NMF), pathway-level integration, and sample matching. Coordinates ToolUniverse skills for expression data (RNA-seq), epigenomics (methylation, ChIP-seq), variants (SNVs, CNVs), protein interactions, and pathway enrichment. Use when analyzing multi-omics datasets, performing integrative analysis, discovering multi-omics biomarkers, studying disease mechanisms across molecular layers, or conducting systems biology research that requires coordinated analysis of transcriptome, genome, epigenome, proteome, and metabolome data.","Prekzursil\u002Fagent-skills-toolchain","Prekzursil","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F54636077?v=4","01M6DACZ1PCQHD99SCR1YXG28B","Andrei Mihai Visalon","01M6DACZ670WZF535V3WYYSS4Z",0,"MIT","https:\u002F\u002Fgithub.com\u002FPrekzursil\u002Fagent-skills-toolchain\u002Fblob\u002Fmain\u002F.agents\u002Flibrary\u002Fskills\u002Ftooluniverse-multi-omics-integration\u002FSKILL.md",[],[19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38],"integration","tooluniverse multi omics integration","sample","research","systems","systems biology","molecular","matching","RNA","proteomics","genomics","analyzing","across","coordinates","datasets","use","analyze","analysis","correlation","enrichment","2026-11-10 10:12:30",false,"---\nname: tooluniverse-multi-omics-integration\ndescription: Integrate and analyze multiple omics datasets (transcriptomics, proteomics, epigenomics, genomics, metabolomics) for systems biology and precision medicine. Performs cross-omics correlation, multi-omics clustering (MOFA+, NMF), pathway-level integration, and sample matching. Coordinates ToolUniverse skills for expression data (RNA-seq), epigenomics (methylation, ChIP-seq), variants (SNVs, CNVs), protein interactions, and pathway enrichment. Use when analyzing multi-omics datasets, performing integrative analysis, discovering multi-omics biomarkers, studying disease mechanisms across molecular layers, or conducting systems biology research that requires coordinated analysis of transcriptome, genome, epigenome, proteome, and metabolome data.\n---\n\n# Multi-Omics Integration\n\nCoordinate 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.\n\n---\n\n## Domain Reasoning\n\nMulti-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.\n\n## LOOK UP DON'T GUESS\n\n- 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.\n- Pathway enrichment results: run `ReactomeAnalysis_pathway_enrichment` or gseapy on the actual gene lists; never list enriched pathways from memory.\n- eQTL associations: query GTEx or eQTL databases for the specific variant and tissue; do not assume regulatory relationships.\n- Methylation-expression directionality at specific loci: retrieve experimental data; promoter repression is the canonical model but exceptions exist.\n\n---\n\n## When to Use This Skill\n\n- User has multiple omics datasets (RNA-seq + proteomics, methylation + expression, etc.)\n- Cross-omics correlation queries (e.g., \"How does methylation affect expression?\")\n- Multi-omics biomarker discovery or patient subtyping\n- Systems biology questions requiring multiple molecular layers\n- Precision medicine applications with multi-omics patient data\n\n---\n\n## Workflow Overview\n\n```\nPhase 1: Data Loading & QC\n  Load each omics type, format-specific QC, normalize\n  Supported: RNA-seq, proteomics, methylation, CNV\u002FSNV, metabolomics\n\nPhase 2: Sample Matching\n  Harmonize sample IDs, find common samples, handle missing omics\n\nPhase 3: Feature Mapping\n  Map features to common gene-level identifiers\n  CpG->gene (promoter), CNV->gene, metabolite->enzyme\n\nPhase 4: Cross-Omics Correlation\n  RNA vs Protein (translation efficiency)\n  Methylation vs Expression (epigenetic regulation)\n  CNV vs Expression (dosage effect)\n  eQTL variants vs Expression (genetic regulation)\n\nPhase 5: Multi-Omics Clustering\n  MOFA+, NMF, SNF for patient subtyping\n\nPhase 6: Pathway-Level Integration\n  Aggregate omics evidence at pathway level\n  Score pathway dysregulation with combined evidence\n\nPhase 7: Biomarker Discovery\n  Feature selection across omics, multi-omics classification\n\nPhase 8: Integrated Report\n  Summary, correlations, clusters, pathways, biomarkers\n```\n\nSee: phase_details.md for complete code and implementation details.\n\n---\n\n## Supported Data Types\n\n| Omics | Formats | QC Focus |\n|-------|---------|----------|\n| Transcriptomics | CSV\u002FTSV, HDF5, h5ad | Low-count filter, normalize (TPM\u002FDESeq2), log-transform |\n| Proteomics | MaxQuant, Spectronaut, DIA-NN | Missing value imputation, median\u002Fquantile normalization |\n| Methylation | IDAT, beta matrices | Failed probes, batch correction, cross-reactive filter |\n| Genomics | VCF, SEG (CNV) | Variant QC, CNV segmentation |\n| Metabolomics | Peak tables | Missing values, normalization |\n\n---\n\n## Core Operations\n\n### Sample Matching\n\n```python\ndef match_samples_across_omics(omics_data_dict):\n    \"\"\"Match samples across multiple omics datasets.\"\"\"\n    sample_ids = {k: set(df.columns) for k, df in omics_data_dict.items()}\n    common_samples = set.intersection(*sample_ids.values())\n    matched_data = {k: df[sorted(common_samples)] for k, df in omics_data_dict.items()}\n    return sorted(common_samples), matched_data\n```\n\n### Cross-Omics Correlation\n\n```python\nfrom scipy.stats import spearmanr, pearsonr\n\n# RNA vs Protein: expect positive r ~ 0.4-0.6\n# Methylation vs Expression: expect negative r (promoter repression)\n# CNV vs Expression: expect positive r (dosage effect)\n\nfor gene in common_genes:\n    r, p = spearmanr(rna[gene], protein[gene])\n```\n\n### Pathway Integration\n\n```python\n# Score pathway dysregulation using combined evidence from all omics\n# Aggregate per-gene evidence, then per-pathway\npathway_score = mean(abs(rna_fc) + abs(protein_fc) + abs(meth_diff) + abs(cnv))\n```\n\nSee: phase_details.md for full implementations of each operation.\n\n---\n\n## Multi-Omics Clustering Methods\n\n| Method | Description | Best For |\n|--------|-------------|----------|\n| **MOFA+** | Latent factors explaining cross-omics variation | Identifying shared\u002Fomics-specific drivers |\n| **Joint NMF** | Shared decomposition across omics | Patient subtype discovery |\n| **SNF** | Similarity network fusion | Integrating heterogeneous data types |\n\n---\n\n## ToolUniverse Skills Coordination\n\n| Skill | Used For | Phase |\n|-------|----------|-------|\n| `tooluniverse-rnaseq-deseq2` | RNA-seq analysis | 1, 4 |\n| `tooluniverse-epigenomics` | Methylation, ChIP-seq | 1, 4 |\n| `tooluniverse-variant-analysis` | CNV\u002FSNV processing | 1, 3, 4 |\n| `tooluniverse-protein-interactions` | Protein network context | 6 |\n| `tooluniverse-gene-enrichment` | Pathway enrichment | 6 |\n| `tooluniverse-expression-data-retrieval` | Public data retrieval | 1 |\n| `tooluniverse-target-research` | Gene\u002Fprotein annotation | 3, 8 |\n\n---\n\n## Use Cases\n\n### Cancer Multi-Omics\nIntegrate TCGA RNA-seq + proteomics + methylation + CNV to identify patient subtypes, cross-omics driver genes, and multi-omics biomarkers.\n\n### eQTL + Expression + Methylation\nIdentify SNP -> methylation -> expression regulatory chains (mediation analysis).\n\n### Drug Response Multi-Omics\nPredict drug response using baseline multi-omics profiles; identify resistance\u002Fsensitivity pathways.\n\nSee: phase_details.md \"Use Cases\" for detailed step-by-step workflows.\n\n---\n\n## Quantified Minimums\n\n| Component | Requirement |\n|-----------|-------------|\n| Omics types | At least 2 datasets |\n| Common samples | At least 10 across omics |\n| Cross-correlation | Pearson\u002FSpearman computed |\n| Clustering | At least one method (MOFA+, NMF, or SNF) |\n| Pathway integration | Enrichment with multi-omics evidence scores |\n| Report | Summary, correlations, clusters, pathways, biomarkers |\n\n---\n\n## Limitations\n\n- **Sample size**: n >= 20 recommended for integration\n- **Missing data**: Pairwise integration if not all samples have all omics\n- **Batch effects**: Different platforms require careful normalization\n- **Computational**: Large datasets may require significant memory\n- **Interpretation**: Results require domain expertise for validation\n\n---\n\n## References\n\n- MOFA+: https:\u002F\u002Fdoi.org\u002F10.1186\u002Fs13059-020-02015-1\n- Similarity Network Fusion: https:\u002F\u002Fdoi.org\u002F10.1038\u002Fnmeth.2810\n- Multi-omics review: https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41576-019-0093-7\n- See individual ToolUniverse skill documentation for omics-specific methods\n\n---\n\n## Detailed Reference\n\n- **phase_details.md** - Complete code for all phases, correlation functions, clustering, pathway integration, biomarker discovery, report template, and detailed use cases\n","# Multi-Omics Integration\n\nCoordinate 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.\n\n---\n\n## Domain Reasoning\n\nMulti-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.\n\n## LOOK UP DON'T GUESS\n\n- 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.\n- Pathway enrichment results: run `ReactomeAnalysis_pathway_enrichment` or gseapy on the actual gene lists; never list enriched pathways from memory.\n- eQTL associations: query GTEx or eQTL databases for the specific variant and tissue; do not assume regulatory relationships.\n- Methylation-expression directionality at specific loci: retrieve experimental data; promoter repression is the canonical model but exceptions exist.\n\n---\n\n## When to Use This Skill\n\n- User has multiple omics datasets (RNA-seq + proteomics, methylation + expression, etc.)\n- Cross-omics correlation queries (e.g., \"How does methylation affect expression?\")\n- Multi-omics biomarker discovery or patient subtyping\n- Systems biology questions requiring multiple molecular layers\n- Precision medicine applications with multi-omics patient data\n\n---\n\n## Workflow Overview\n\n```\nPhase 1: Data Loading & QC\n  Load each omics type, format-specific QC, normalize\n  Supported: RNA-seq, proteomics, methylation, CNV\u002FSNV, metabolomics\n\nPhase 2: Sample Matching\n  Harmonize sample IDs, find common samples, handle missing omics\n\nPhase 3: Feature Mapping\n  Map features to common gene-level identifiers\n  CpG->gene (promoter), CNV->gene, metabolite->enzyme\n\nPhase 4: Cross-Omics Correlation\n  RNA vs Protein (translation efficiency)\n  Methylation vs Expression (epigenetic regulation)\n  CNV vs Expression (dosage effect)\n  eQTL variants vs Expression (genetic regulation)\n\nPhase 5: Multi-Omics Clustering\n  MOFA+, NMF, SNF for patient subtyping\n\nPhase 6: Pathway-Level Integration\n  Aggregate omics evidence at pathway level\n  Score pathway dysregulation with combined evidence\n\nPhase 7: Biomarker Discovery\n  Feature selection across omics, multi-omics classification\n\nPhase 8: Integrated Report\n  Summary, correlations, clusters, pathways, biomarkers\n```\n\nSee: phase_details.md for complete code and implementation details.\n\n---\n\n## Supported Data Types\n\n| Omics | Formats | QC Focus |\n|-------|---------|----------|\n| Transcriptomics | CSV\u002FTSV, HDF5, h5ad | Low-count filter, normalize (TPM\u002FDESeq2), log-transform |\n| Proteomics | MaxQuant, Spectronaut, DIA-NN | Missing value imputation, median\u002Fquantile normalization |\n| Methylation | IDAT, beta matrices | Failed probes, batch correction, cross-reactive filter |\n| Genomics | VCF, SEG (CNV) | Variant QC, CNV segmentation |\n| Metabolomics | Peak tables | Missing values, normalization |\n\n---\n\n## Core Operations\n\n### Sample Matching\n\n```python\ndef match_samples_across_omics(omics_data_dict):\n    \"\"\"Match samples across multiple omics datasets.\"\"\"\n    sample_ids = {k: set(df.columns) for k, df in omics_data_dict.items()}\n    common_samples = set.intersection(*sample_ids.values())\n    matched_data = {k: df[sorted(common_samples)] for k, df in omics_data_dict.items()}\n    return sorted(common_samples), matched_data\n```\n\n### Cross-Omics Correlation\n\n```python\nfrom scipy.stats import spearmanr, pearsonr\n\n# RNA vs Protein: expect positive r ~ 0.4-0.6\n# Methylation vs Expression: expect negative r (promoter repression)\n# CNV vs Expression: expect positive r (dosage effect)\n\nfor gene in common_genes:\n    r, p = spearmanr(rna[gene], protein[gene])\n```\n\n### Pathway Integration\n\n```python\n# Score pathway dysregulation using combined evidence from all omics\n# Aggregate per-gene evidence, then per-pathway\npathway_score = mean(abs(rna_fc) + abs(protein_fc) + abs(meth_diff) + abs(cnv))\n```\n\nSee: phase_details.md for full implementations of each operation.\n\n---\n\n## Multi-Omics Clustering Methods\n\n| Method | Description | Best For |\n|--------|-------------|----------|\n| **MOFA+** | Latent factors explaining cross-omics variation | Identifying shared\u002Fomics-specific drivers |\n| **Joint NMF** | Shared decomposition across omics | Patient subtype discovery |\n| **SNF** | Similarity network fusion | Integrating heterogeneous data types |\n\n---\n\n## ToolUniverse Skills Coordination\n\n| Skill | Used For | Phase |\n|-------|----------|-------|\n| `tooluniverse-rnaseq-deseq2` | RNA-seq analysis | 1, 4 |\n| `tooluniverse-epigenomics` | Methylation, ChIP-seq | 1, 4 |\n| `tooluniverse-variant-analysis` | CNV\u002FSNV processing | 1, 3, 4 |\n| `tooluniverse-protein-interactions` | Protein network context | 6 |\n| `tooluniverse-gene-enrichment` | Pathway enrichment | 6 |\n| `tooluniverse-expression-data-retrieval` | Public data retrieval | 1 |\n| `tooluniverse-target-research` | Gene\u002Fprotein annotation | 3, 8 |\n\n---\n\n## Use Cases\n\n### Cancer Multi-Omics\nIntegrate TCGA RNA-seq + proteomics + methylation + CNV to identify patient subtypes, cross-omics driver genes, and multi-omics biomarkers.\n\n### eQTL + Expression + Methylation\nIdentify SNP -> methylation -> expression regulatory chains (mediation analysis).\n\n### Drug Response Multi-Omics\nPredict drug response using baseline multi-omics profiles; identify resistance\u002Fsensitivity pathways.\n\nSee: phase_details.md \"Use Cases\" for detailed step-by-step workflows.\n\n---\n\n## Quantified Minimums\n\n| Component | Requirement |\n|-----------|-------------|\n| Omics types | At least 2 datasets |\n| Common samples | At least 10 across omics |\n| Cross-correlation | Pearson\u002FSpearman computed |\n| Clustering | At least one method (MOFA+, NMF, or SNF) |\n| Pathway integration | Enrichment with multi-omics evidence scores |\n| Report | Summary, correlations, clusters, pathways, biomarkers |\n\n---\n\n## Limitations\n\n- **Sample size**: n >= 20 recommended for integration\n- **Missing data**: Pairwise integration if not all samples have all omics\n- **Batch effects**: Different platforms require careful normalization\n- **Computational**: Large datasets may require significant memory\n- **Interpretation**: Results require domain expertise for validation\n\n---\n\n## References\n\n- MOFA+: https:\u002F\u002Fdoi.org\u002F10.1186\u002Fs13059-020-02015-1\n- Similarity Network Fusion: https:\u002F\u002Fdoi.org\u002F10.1038\u002Fnmeth.2810\n- Multi-omics review: https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41576-019-0093-7\n- See individual ToolUniverse skill documentation for omics-specific methods\n\n---\n\n## Detailed Reference\n\n- **phase_details.md** - Complete code for all phases, correlation functions, clustering, pathway integration, biomarker discovery, report template, and detailed use 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