Describe strategies for integrating multi-omics data (genomics, transcriptomics, proteomics, metabolomics).

Multi-omics integration combines data layers for comprehensive biological understanding. Strategies: 1) Concatenation-based - merge features after normalization and scaling; simple but ignores inter-omic relationships. 2) Transformation-based - project to common latent space using multi-block PCA (MOFA), CCA, or autoencoders. 3) Network-based - build interaction networks across omics layers; integrate with prior knowledge (STRING, Reactome). 4) Bayesian approaches - model relationships probabilistically, propagate information across layers. 5) Pathway-based - aggregate signals at pathway level (PARADIGM, iCluster). Challenges: different scales, missing data, batch effects across platforms, sample mismatch, biological vs technical variation. Best practices: careful experimental design, matched samples, appropriate normalization, validation across cohorts. Tools: MOFA+, mixOmics, SNF, NetICS.