Network approaches to systems biology analysis of complex disease: integrative methods for multi-omics data

Abstract

In the past decade, significant progress has been made in complex disease research across multiple omics layers from genome, transcriptome and proteome to metabolome. There is an increasing awareness of the importance of biological interconnections, and much success has been achieved using systems biology approaches. However, because of the typical focus on one single omics layer at a time, existing systems biology findings explain only a modest portion of complex disease. Recent advances in multi-omics data collection and sharing present us new opportunities for studying complex diseases in a more comprehensive fashion, and yet simultaneously create new challenges considering the unprecedented data dimensionality and diversity. Here, our goal is to review extant and emerging network approaches that can be applied across multiple biological layers to facilitate a more comprehensive and integrative multilayered omics analysis of complex diseases.

Figure 1

Networks and approaches in omics layers. (A) There are primarily three categories of network approaches across omics layers, where networks are generated as outcomes, used as priors or analyzed as features, respectively. (B) The ultimate goal of integrative omics is to illuminate the interplay across biological layers that potentially drives the progress of complex diseases.

Figure 2

Common pipelines for pathway enrichment and network module identification. (A) Demonstration of three types of pathway enrichment analysis. In ORA and rank-based methods (following the dark blue line), the pathway-level significance is based on node counts (ORA) or the significance of individual nodes (rank-based); in topology-based methods (following the dark blue line), the topology of pathways, such as reactant and product in a single biochemical reaction, is taken into account. (B) Demonstration of two types of network analyses. The first one (following the dark blue line) identifies the module based on the weighted network after mapping gene/protein significance onto the node; the second one (following the light blue line) predefines the module based on the original network topology.