Estimation of Directed Acyclic Graphs Through Two-Stage Adaptive Lasso for Gene Network Inference

• 成果类型: Article
• 署名作者: Han, Sung Won; Chen, Gong; Cheon, Myun-Seok; Zhong, Hua
• 署名单位: Korea University; University System of Georgia; Georgia Institute of Technology; New York University
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2016.1142880
• 发表日期: 2016
• 页码: 1004-1019
• 关键词: learning bayesian networks; variable selection; regulatory networks; PENALIZED LIKELIHOOD; regression; MODEL; expression; combination

摘要

Graphical models are a popular approach to find dependence and conditional independence relationships between gene expressions. Directed acyclic graphs (DAGs) are a special class of directed, graphical models, where all the edges are directed edges and contain. no directed cycles. The DAGs are well known models for discovering causal relationships between genes in gene regulatory networks. However, estimating DAGs without assuming known ordering is challenging due to high dimensionality, the acyclic constraints, and the presence of equivalence class from observational data. To overcome these challenges, we propose a two stage adaptive Lasso approach, called NS-DIST, which performs neighborhood selection (NS) in stage 1, and then estimates DAGs by the discrete improving search with Tabu (DIST) algorithm within the selected neighborhood. Simulation studies are presented to demonstrate the effectiveness of the method and its computational efficiency. Two real data examples are used to demonstrate the practical usage of our method for gene regulatory network inference. Supplementary materials for this article are available online.