Multi-omics Data Integration, Interpretation, and Its Application

Abstract

To study complex biological processes holistically, it is imperative to take an integrative approach that combines multi-omics data to highlight the interrelationships of the involved biomolecules and their functions. With the advent of high-throughput techniques and availability of multi-omics data generated from a large set of samples, several promising tools and methods have been developed for data integration and interpretation. In this review, we collected the tools and methods that adopt integrative approach to analyze multiple omics data and summarized their ability to address applications such as disease subtyping, biomarker prediction, and deriving insights into the data. We provide the methodology, use-cases, and limitations of these tools; brief account of multi-omics data repositories and visualization portals; and challenges associated with multi-omics data integration.

Keywords: biomarker prediction; data integration; data repositories; disease subtyping; multi-omics.

Figure 1.

Overview of multi-omics data integration tools. The tools/methods are grouped based on their approach and are color coded as per their applications. FSMKL indicates feature selection multiple kernel learning; JIVE, joint and individual variation explained; MCIA, multiple co-inertia analysis; MDI, multiple dataset integration; MFA, multiple factor analysis; MOFA, multi-omics factor analysis; NEMO, neighborhood based multi-omics clustering; PFA, pattern fusion analysis; PMA, penalized multivariate analysis; sMBPLS, sparse multi-block partial least squares; SNF, similarity network fusion; NMF, nonnegative matrix factorization; BCC, Bayesian consensus clustering; PSDF, patient-specific data fusion.