Hierarchy‐assisted gene expression regulatory network analysis

Abstract

Gene expressions have been extensively studied in biomedical research. With gene expression, network analysis, which takes a system perspective and examines the interconnections among genes, has been established as highly important and meaningful. In the construction of gene expression networks, a commonly adopted technique is high‐dimensional regularized regression. Network construction can be unadjusted (which focuses on gene expressions only) and adjusted (which also incorporates regulators of gene expressions), and the two types of construction have different implications and can be equally important. In this article, we propose a variable selection hierarchy to connect the unadjusted regression‐based network construction with the adjusted construction that incorporates two or more types of regulators. This hierarchy is sensible and amounts to additional information for both constructions, thus having the potential of improving variable selection and estimation. An effective computational algorithm is developed, and extensive simulation demonstrates the superiority of the proposed construction over multiple closely relevant alternatives. The analysis of TCGA data further demonstrates the practical utility of the proposed approach.