Multi-omics integration in biomedical research - A metabolomics-centric review

Abstract

Recent advances in high-throughput technologies have enabled the profiling of multiple layers of a biological system, including DNA sequence data (genomics), RNA expression levels (transcriptomics), and metabolite levels (metabolomics). This has led to the generation of vast amounts of biological data that can be integrated in so-called multi-omics studies to examine the complex molecular underpinnings of health and disease. Integrative analysis of such datasets is not straightforward and is particularly complicated by the high dimensionality and heterogeneity of the data and by the lack of universal analysis protocols. Previous reviews have discussed various strategies to address the challenges of data integration, elaborating on specific aspects, such as network inference or feature selection techniques. Thereby, the main focus has been on the integration of two omics layers in their relation to a phenotype of interest. In this review we provide an overview over a typical multi-omics workflow, focusing on integration methods that have the potential to combine metabolomics data with two or more omics. We discuss multiple integration concepts including data-driven, knowledge-based, simultaneous and step-wise approaches. We highlight the application of these methods in recent multi-omics studies, including large-scale integration efforts aiming at a global depiction of the complex relationships within and between different biological layers without focusing on a particular phenotype.

Keywords: Data integration; Lipidomics; Metabolomics; Multi-omics; Systems biology.

Figure 1.. Multi-omics workflow.

A typical multi-omics analysis can generally be broken down into 4 steps. (i) Data generation. Study design, sample preparation and subsequent data acquisition through high-throughput analytical platforms lead to different data scenarios. (ii) Data preprocessing and dimensionality reduction. Raw data collected on different omics layers is preprocessed appropriately and dimensionality reduction can be applied to reduce the number of variables (measured biological entities). (iii) Data integration. Data from different omics layers are analyzed and integrated using data-driven, knowledge-based or hybrid integration approaches. The choice of method depends on the input data and research question of interest. (iv) Data interpretation. Post-integration visualization and analysis of the integration results (e.g., statistical model or network) can identify novel biomarker candidates, generate testable hypothesis or reveal meaningful biological relationships.

Figure 2.. Multi-omics integration through composite networks.

A. Different layers of a biological system that can be profiled using high-throughput technologies and are frequently integrated in multi-omics studies. B. Simultaneous integration. Correlation structures within and across omics datasets are analyzed using statistical methods. C. QTL-based integration. Using the genome as an anchor, quantitative trait loci (QTLs) identified in genome wide association studies (GWASs) are overlaid to establish links between different omics layers. D Knowledge integration. External information from metabolic databases or scientific literature is used to establish relationships between biological entities. E. Composite networks. By merging the networks inferred in (B-D) on common entities, comprehensive multi-omics catalogues can be constructed. These heterogenous networks can be mined in post-integration analysis using established graph algorithms.