Overview
Multi-omics integration addresses the challenge of combining diverse molecular data types, genome, transcriptome, proteome, and metabolome, to construct a coherent, systems-level picture of a biological system. No single omics layer captures the full complexity of cellular regulation; genomic mutations may not alter transcript levels, transcript abundance often does not correlate with protein abundance due to post-translational regulation, and metabolite levels reflect the integrated output of all upstream layers. Integration strategies aim to bridge these gaps and reveal how perturbations propagate across molecular scales.
Methods
Integration approaches fall into three categories. Concatenation-based methods merge all omics features into a single matrix for joint analysis by clustering or classification. Transformation-based methods convert each omics dataset into an intermediate representation, such as a kernel matrix or a network, before combining them. Model-based methods use probabilistic graphical models or deep learning architectures such as variational autoencoders to learn shared latent representations across data types. Tools like MOFA (Multi-Omics Factor Analysis) and mixOmics identify common and data-type-specific variation patterns. Data preprocessing is critical: batch effects, missing values, and differing dynamic ranges must be addressed before integration.
Applications
Multi-omics integration drives precision medicine by stratifying patients into molecular subtypes based on combined genomic, transcriptomic, and proteomic profiles. In cancer research, integrated analysis links copy-number alterations from DNA microarrays and gene expression data to protein-level changes measured by proteomics and mass spectrometry, and maps these onto disrupted metabolic pathways. Integrative approaches also reveal regulatory mechanisms by correlating epigenetic marks with transcript and protein abundance, providing a truly holistic view of cellular function.
Practical Protocol
For multi-omics integration using MOFA (Multi-Omics Factor Analysis), start with matched data matrices from two or more omics layers measured on the same samples. Preprocess each omics dataset independently: for RNA-seq, use variance-stabilized or TPM-normalized counts; for proteomics, use log2-transformed intensity values; for methylation, use beta values. Remove features with low variance across samples (e.g., keep the top 5000 most variable genes for transcriptomics). In R, create a MOFA object: library(MOFA2); mofa <- create_mofa(list(RNA = rna_matrix, Protein = prot_matrix, Methylation = meth_matrix)). Define model options: model_opts <- get_default_model_options(mofa); model_opts$num_factors <- 10. Train the model: mofa <- run_mofa(mofa, mofa_opts). Inspect variance explained per factor across omics layers with plot_variance_explained(mofa), which shows which factors capture shared versus data-type-specific variation. The factor loadings (weights) indicate which features contribute to each factor: plot_weights(mofa, factor = 1, nfeatures = 10) shows top genes and proteins driving the first factor. For the mixOmics approach using DIABLO, create a design matrix specifying connections between omics layers: design <- matrix(c(0, 0.1, 0.1, 0, 0, 0.1, 0.1, 0, 0), nrow = 3). Tune parameters with perf.diablo and select features with selectVar. Visualize sample separation on factor 1 vs factor 2 score plots colored by phenotype. Interpreting factor loadings: a positive loading for a gene in a given factor means higher expression correlates with higher factor values. Pathway enrichment on the genes with top loadings per factor reveals the biological processes each factor represents. Compare factor values across clinical groups with boxplots and test for significance using ANOVA or Wilcoxon tests.
