tooluniverse-multi-omics-integration

What it does

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

How it works

• Phase 1: Data Loading & QC loads multiple omics datasets and performs per-omics quality control.
• Phase 2: Sample Matching identifies common samples across omics and subsets data accordingly.
• Phase 3: Feature Mapping maps diverse features to a common gene-centric space (genes, proteins to genes; CpG/promoter mappings; CNV-to-gene mappings; metabolites to enzyme genes).
• Phase 4: Cross-Omics Correlation computes relationships across layers, including RNA vs protein, methylation vs expression, CNV vs expression, eQTL vs expression, and metabolite vs enzyme expression.
• Phase 5: Multi-Omics Clustering uses MOFA+ and NMF (and SNF) to identify latent factors and patient subtypes, plus joint clustering across omics.
• Phase 6: Pathway-Level Integration aggregates evidence at the pathway level, scoring dysregulation and leveraging ToolUniverse enrichment tools for pathway analysis.
• Phase 7: Biomarker Discovery performs cross-omics feature selection to derive multi-omics signatures for classification, with validation steps.
• Phase 8: Generate Integrated Report compiles summary statistics, cross-omics results, clusters, dysregulated pathways, and biomarkers into a unified report.

When to use it

• You have multiple omics datasets and need integrative analysis across molecular layers.
• You require cross-omics correlation queries, multi-omics clustering, or biomarker discovery.
• You study disease mechanisms or precision medicine questions needing coordinated analysis of transcriptome, genome, epigenome, proteome, and metabolome data.
• Example triggers include analyses like integrating RNA-seq with proteomics, correlating promoter methylation with expression, performing MOFA+ clustering, or identifying multi-omics pathways driving disease.

What it can touch

• Data inputs: RNA-seq expression matrices, proteomics, methylation data, CNV/SNV data, and metabolomics tables.
• It coordinates Skill workflows for RNA-seq, epigenomics, variant-analysis, protein-interactions, and gene-enrichment tools.
• Tools referenced include MOFA+ (for multi-omics factors), NMF, and enrichment routines via ToolUniverse (e.g., Reactome, KEGG, GO).

Caveats

• The skill relies on the availability and correctness of integrated omics data formats and matching identifiers.
• Detailed outputs (e.g., specific factor interpretations or biomarker lists) depend on data quality and chosen parameters within the included workflows.
• Licensing and environment constraints are per the repository; this summary reflects the stated capabilities and workflows within the skill.