Network Diffusion Promotes the Integrative Analysis of Multiple Omics

Abstract

The development of integrative methods is one of the main challenges in bioinformatics. Network-based methods for the analysis of multiple gene-centered datasets take into account known and/or inferred relations between genes. In the last decades, the mathematical machinery of network diffusion-also referred to as network propagation-has been exploited in several network-based pipelines, thanks to its ability of amplifying association between genes that lie in network proximity. Indeed, network diffusion provides a quantitative estimation of network proximity between genes associated with one or more different data types, from simple binary vectors to real vectors. Therefore, this powerful data transformation method has also been increasingly used in integrative analyses of multiple collections of biological scores and/or one or more interaction networks. We present an overview of the state of the art of bioinformatics pipelines that use network diffusion processes for the integrative analysis of omics data. We discuss the fundamental ways in which network diffusion is exploited, open issues and potential developments in the field. Current trends suggest that network diffusion is a tool of broad utility in omics data analysis. It is reasonable to think that it will continue to be used and further refined as new data types arise (e.g. single cell datasets) and the identification of system-level patterns will be considered more and more important in omics data analysis.

Keywords: biological networks; integrative analysis; network-diffusion; omics data; precision medicine.

Figure 1

Classification of integrative methods. Criteria: (A) Goals; (B) Input data; (C) Network Diffusion (ND) model: Random Walk (RW), Random Walk with Restart (RWR), Insulated Heat Diffusion (IHD), Diffusion Kernel (DK); (D) Molecular network; (E) ND usage.

Figure 2

Ways in which ND enters the integrative analysis pipelines.

Figure 3

Network diffusion methods for the integrative analyses of multiple biological layers. GP, gene prioritization; MD, module detection; FP, function prediction; DS, disease subtyping; SP, survival prediction. We classified methods according to their main use described by the respective authors.