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,

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 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 Cross Omics Correlation Pathway Integration 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

tooluniverse-multi-omics-integration