Human diseases are multi-factorial, affecting multiple aspects of a homeostatic system. Recent advances in high-throughput technology have allowed the generation of various omics datasets from large cohorts at affordable costs and hence made it possible to study the complex dynamical systems perturbed in human diseases. Studying the complex perturbed systems offers a mechanistic understanding to identify druggable targets and offers new avenues for individualised medical intervention. Mechanisms driving complex human diseases cannot be explored merely by single omics-focused studies. In addition, the heterogeneity among the human populations adds additional complexity and limits the possibility for inferring the regulatory mechanisms underlying these diseases. Examining the disease or phenotype of interest through the lens of multiple omics layers may allow the dissection of the perturbed biological processes associated with the disease. Studying a complex disease through multiple omics layers providing vast information is quite a challenging task and, therefore, requires statistical frameworks to achieve integrative multi-omics analysis. In this chapter, we first summarise key characteristics of each of the omics layers and the various considerations important for the implementation of statistical methods. We then shed light on the most common statistical methods used for multi-omics integration studies and highlight various published examples showing the use of these methods for addressing key biological questions. For this, we show integration examples focused on at least two prime omics layers. We next focus on methods and examples showing multi-omics integration to study dynamical systems in large cohort studies. Finally, we discuss some of the multi-omics approaches and examples from single-cell multi-omics datasets.
Kumar et al. (Thu,) studied this question.