Estimation of Directed Acyclic Graphs Through Two-Stage Adaptive Lasso for Gene Network Inference
提出一种两阶段自适应Lasso方法（NS-DIST），先通过邻域选择缩小搜索空间，再结合离散禁忌搜索算法估计有向无环图，用于从高维观测数据中推断基因调控网络，模拟和真实数据验证了其有效性和计算效率。
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.