Two-way sparsity for time-varying networks, with applications in genomics

TL;DR

This paper introduces a new Bayesian model for time-varying networks that promotes two-way sparsity, effectively capturing changes in network structure over time and across variables, with applications in genomics and neural development.

Contribution

The paper presents a novel prior structure for inducing two-way sparsity in dynamic networks, combining ideas from Bayesian lasso and copula models, with an efficient Gibbs sampler implementation.

Findings

Successfully applied to neural development data

Identifies biologically meaningful network changes

Matches current biological knowledge

Abstract

We propose a novel way of modelling time-varying networks, by inducing two-way sparsity on local models of node connectivity. This two-way sparsity separately promotes sparsity across time and sparsity across variables (within time). Separation of these two types of sparsity is achieved through a novel prior structure, which draws on ideas from the Bayesian lasso and from copula modelling. We provide an efficient implementation of the proposed model via a Gibbs sampler, and we apply the model to data from neural development. In doing so, we demonstrate that the proposed model is able to identify changes in genomic network structure that match current biological knowledge. Such changes in genomic network structure can then be used by neuro-biologists to identify potential targets for further experimental investigation.