How do you integrate multi-omics data to understand biological systems?

Multi-omics integration combines genomics, transcriptomics, proteomics, metabolomics, and epigenomics to provide holistic understanding. Strategies include: late integration (analyze separately, combine at interpretation), intermediate integration (correlation networks, pathway enrichment across omics), and early integration (matrix factorization, machine learning on combined data). Methods include canonical correlation analysis (CCA), partial least squares (PLS), MOFA, DIABLO, and deep learning approaches. Network-based integration maps omics data onto interaction networks (STRING, Reactome). Challenges include data heterogeneity, missing values, different scales and distributions, and causal inference. Applications span disease subtyping, biomarker discovery, and understanding regulatory mechanisms. Validation requires functional experiments.