Identifying Gene Network Rewiring by Integrating Gene Expression and Gene Network Data

Exploring the rewiring pattern of gene regulatory networks between different pathological states is an important task in bioinformatics. Although a number of computational approaches have been developed to infer differential networks from high-throughput data, most of them only focus on gene expression data. The valuable static gene regulatory network data accumulated in recent biomedical researches are neglected. In this study, we propose a new Gaussian graphical model-based method to infer differential networks by integrating gene expression and static gene regulatory network data. We first evaluate the empirical performance of our method by comparing with the state-of-the-art methods using simulation data. We also apply our method to The Cancer Genome Atlas data to identify gene network rewiring between ovarian cancers with different platinum responses, and rewiring between breast cancers of luminal A subtype and basal-like subtype. Hub genes in the estimated differential networks rediscover known genes associated with platinum resistance in ovarian cancer and signatures of the breast cancer intrinsic subtypes.