Key Takeaways
- Multi-omics integration combines data from multiple omics layers to find correlations invisible in single-omics analysis
- OmicsClaw provides automated pipelines for: single-omics preprocessing → cross-omics alignment → integration analysis → visualization
- Supported integration methods: MOFA (Multi-Omics Factor Analysis), correlation analysis, network-based integration
- Input: preprocessed count matrices or raw FASTQ files
- Output: integrated factor plots, cross-omics correlation heatmaps, pathway enrichment results
Why Multi-Omics Integration?
Single-omics analysis tells you what changed. Multi-omics integration tells you why it changed — by revealing regulatory relationships between DNA, RNA, protein, and metabolite layers.
Core challenge: Different omics types have different scales, noise profiles, and measurement biases. Integration methods must account for these differences.
Integration Workflow with OmicsClaw
Step 1: Individual omics preprocessing
├── RNA-seq → DESeq2 normalization → differential expression
└── Proteomics → MaxQuant → protein quantification
Step 2: Cross-omics alignment
└── Match samples, align feature identifiers (gene → protein mapping)
Step 3: Integration analysis
├── MOFA: Find shared latent factors across omics
├── Correlation: Gene-protein expression correlation
└── Network: Build regulatory networks
Step 4: Visualization and interpretation
├── Factor plots showing shared variation sources
├── Cross-omics correlation heatmaps
└── Pathway enrichment on integrated resultsSupported Integration Methods
| Method | Input | Output | Best For |
|---|---|---|---|
| MOFA | 2+ omics matrices | Latent factors, variance decomposition | Finding shared biological signals |
| Correlation | Paired gene-protein data | Correlation coefficients, scatter plots | Validating transcriptional regulation |
| Network | Multi-omics features | Regulatory networks | Understanding causal relationships |
| Pathway | Integrated feature lists | Enriched pathways across omics | Biological interpretation |
FAQ
Q1: How many samples do I need for multi-omics integration?
MOFA works best with 15+ samples per group. Correlation analysis requires paired samples (same individual, both omics measured). More samples improve statistical power.
Q2: Can I integrate more than 2 omics types?
Yes. MOFA supports arbitrary numbers of omics views. OmicsClaw can integrate genomics + transcriptomics + proteomics + metabolomics simultaneously.
Summary
Multi-omics integration reveals regulatory relationships invisible in single-omics analysis. OmicsClaw automates the full workflow — from individual preprocessing through cross-omics alignment to integration analysis and visualization. MOFA is the recommended method for exploratory analysis; correlation analysis for targeted validation.