Direct estimation of differential networks

• 成果类型: Article
• 署名作者: Zhao, Sihai Dave; Cai, T. Tony; Li, Hongzhe
• 署名单位: University of Pennsylvania; University of Pennsylvania
• 刊物名称: BIOMETRIKA
• ISSN/ISSBN: 0006-3444
• DOI: 10.1093/biomet/asu009
• 发表日期: 2014
• 页码: 253268
• 关键词: inverse covariance estimation; sparse signals; ovarian-cancer; RECOVERY; trail; selection; targets; therapy; biology; graphs

摘要

It is often of interest to understand how the structure of a genetic network differs between two conditions. In this paper, each condition-specific network is modelled using the precision matrix of a multivariate normal random vector, and a method is proposed to directly estimate the difference of the precision matrices. In contrast to other approaches, such as separate or joint estimation of the individual matrices, direct estimation does not require those matrices to be sparse, and thus can allow the individual networks to contain hub nodes. Under the assumption that the true differential network is sparse, the direct estimator is shown to be consistent in support recovery and estimation. It is also shown to outperform existing methods in simulations, and its properties are illustrated on gene expression data from late-stage ovarian cancer patients.