title: Direct estimation of differential networks.
abstract: 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 modeled 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.
authors.affiliation: Department of Biostatistics and Epidemiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania 19104, USA.
authors.forename: Sihai Dave
authors.initials: SD
authors.lastname: Zhao
authors.affiliation: Department of Statistics, The Wharton School, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA.
authors.forename: T Tony
authors.initials: TT
authors.lastname: Cai
authors.affiliation: Department of Biostatistics and Epidemiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania 19104, USA.
authors.forename: Hongzhe
authors.initials: H
authors.lastname: Li
keywords: Differential network; Graphical model; High dimensionality; Precision matrix