Inferring Gene Network Rewiring By Combining Gene Expression And Gene Mutation Data

Gene dependency networks often undergo changes with respect to different dis-ease states.Understanding how these networks rewire between two different disease states is an important task in genomic research.Although many computational methods have been proposed to undertake this task via differential network analy-sis,most of them are designed for a predefined data type.With the development of the high throughput technologies,gene activity measurements can be collected from different aspects(e.g.,mRNA expression and DNA mutation).These different data types might share some common characteristics and include certain unique properties of data type.New methods are needed to explore the similarity and d-ifference between differential networks estimated from different data types.In this study,we develop a new differential network inference model which identifies gene network rewiring by combining gene expression and gene mutation data.Similar-ities and differences between different data types axe learned via a group bridge penalty function.Simulation studies have demonstrated that our method consis-tently outperforms the competing methods.We also apply our method to identify gene network rewiring associated with ovarian cancer platinum resistance from The Cancer Genome Atlas data.There are certain differential edges common to both data types and some differential edges unique to individual data types.Hub genes in the differential networks inferred by our method play important roles in ovarian cancer drug resistance.