Identifying differential networks based on multi-platform gene expression data

Abstract

Exploring how the structure of a gene regulatory network differs between two different disease states is fundamental for understanding the biological mechanisms behind disease development and progression. Recently, with rapid advances in microarray technologies, gene expression profiles of the same patients can be collected from multiple microarray platforms. However, previous differential network analysis methods were usually developed based on a single type of platform, which could not utilize the common information shared across different platforms. In this study, we introduce a multi-view differential network analysis model to infer the differential network between two different patient groups based on gene expression profiles collected from multiple platforms. Unlike previous differential network analysis models that need to analyze each platform separately, our model can draw support from multiple data platforms to jointly estimate the differential networks and produce more accurate and reliable results. Our simulation studies demonstrate that our method consistently outperforms other available differential network analysis methods. We also applied our method to identify network rewiring associated with platinum resistance using TCGA ovarian cancer samples. The experimental results demonstrate that the hub genes in our identified differential networks on the PI3K/AKT/mTOR pathway play an important role in drug resistance.