DiffGRN: differential gene regulatory network analysis

Abstract

Identification of differential gene regulators with significant changes under disparate conditions is essential to understand complex biological mechanism in a disease. Differential Network Analysis (DiNA) examines different biological processes based on gene regulatory networks that represent regulatory interactions between genes with a graph model. While most studies in DiNA have considered correlation-based inference to construct gene regulatory networks from gene expression data due to its intuitive representation and simple implementation, the approach lacks in the representation of causal effects and multivariate effects between genes. In this paper, we propose an approach named Differential Gene Regulatory Network (DiffGRN) that infers differential gene regulation between two groups. We infer gene regulatory networks of two groups using Random LASSO, and then we identify differential gene regulations by the proposed significance test. The advantages of DiffGRN are to capture multivariate effects of genes that regulate a gene simultaneously, to identify causality of gene regulations, and to discover differential gene regulators between regression-based gene regulatory networks. We assessed DiffGRN by simulation experiments and showed its outstanding performance than the current state-of-the-art correlation-based method, DINGO. DiffGRN is applied to gene expression data in asthma. The DiNA with asthma data showed a number of gene regulations, such as ADAM12 and RELB, reported in biological literature.