title: Node-based differential network analysis in genomics.
abstract: Gene dependency networks often undergo changes in response to different conditions. Understanding how these networks change across two conditions is an important task in genomics research. Most previous differential network analysis approaches assume that the difference between two condition-specific networks is driven by individual edges. Thus, they may fail in detecting key players which might represent important genes whose mutations drive the change of network. In this work, we develop a node-based differential network analysis (N-DNA) model to directly estimate the differential network that is driven by certain hub nodes. We model each condition-specific gene network as a precision matrix and the differential network as the difference between two precision matrices. Then we formulate a convex optimization problem to infer the differential network by combing a D-trace loss function and a row-column overlap norm penalty function. Simulation studies demonstrate that N-DNA provides more accurate estimate of the differential network than previous competing approaches. We apply N-DNA to ovarian cancer and breast cancer gene expression data. The model rediscovers known cancer-related genes and contains interesting predictions.
authors.affiliation: School of Mathematics and Statistics & Hubei Key Laboratory of Mathematical Sciences, Central China Normal University, Wuhan, China; Department of Electronic Engineering, City University of Hong Kong, Hong Kong, China.
authors.forename: Xiao-Fei
authors.initials: XF
authors.lastname: Zhang
authors.affiliation: College of Information Engineering, Shenzhen University, Shenzhen, 518060, China. Electronic address: szuouyl@gmail.com.
authors.forename: Le
authors.initials: L
authors.lastname: Ou-Yang
authors.affiliation: Department of Electronic Engineering, City University of Hong Kong, Hong Kong, China.
authors.forename: Hong
authors.initials: H
authors.lastname: Yan
mesh_terms: D064413:3-Phosphoinositide-Dependent Protein Kinases / Q000235:genetics; D001943:Breast Neoplasms / Q000235:genetics; D005260:Female; D053263:Gene Regulatory Networks; D023281:Genomics / Q000379:methods*; D006801:Humans; D055504:Insulin Receptor Substrate Proteins / Q000235:genetics; D000069550:Machine Learning; D010051:Ovarian Neoplasms / Q000235:genetics; D019009:Proto-Oncogene Proteins c-kit / Q000235:genetics; D051881:Receptor, Notch1 / Q000235:genetics; D055765:Shc Signaling Adaptor Proteins / Q000235:genetics
chemical_list: C526292:IRS1 protein, human; D055504:Insulin Receptor Substrate Proteins; C497761:NOTCH1 protein, human; D051881:Receptor, Notch1; C519335:SHC4 protein, human; D055765:Shc Signaling Adaptor Proteins; D019009:Proto-Oncogene Proteins c-kit; D064413:3-Phosphoinositide-Dependent Protein Kinases; C578562:PDPK1 protein, human
keywords: Differential network analysis; Gaussian graphical model; Gene dependency network; Graphical lasso; Hub nodes