TWO-WAY SPARSITY FOR TIME-VARYING NETWORKS WITH APPLICATIONS IN GENOMICS

• 成果类型: Article
• 署名作者: Bartlett, Thomas E.; Kosmidis, Ioannis; Silva, Ricardo
• 署名单位: University of London; University College London; University of Warwick; Alan Turing Institute
• 刊物名称: ANNALS OF APPLIED STATISTICS
• ISSN/ISSBN: 1932-6157
• DOI: 10.1214/20-AOAS1416
• 发表日期: 2021
• 页码: 856-879
• 关键词: cell-differentiation; regression; Lasso

摘要

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 neurobiologists to identify potential targets for further experimental investigation.