A Bayesian Graphical Model for Integrative Analysis of TCGA Data.

Abstract

We integrate three TCGA data sets including measurements on matched DNA copy numbers (C), DNA methylation (M), and mRNA expression (E) over 500+ ovarian cancer samples. The integrative analysis is based on a Bayesian graphical model treating the three types of measurements as three vertices in a network. The graph is used as a convenient way to parameterize and display the dependence structure. Edges connecting vertices infer specific types of regulatory relationships. For example, an edge between M and E and a lack of edge between C and E implies methylation-controlled transcription, which is robust to copy number changes. In other words, the mRNA expression is sensitive to methylational variation but not copy number variation. We apply the graphical model to each of the genes in the TCGA data independently and provide a comprehensive list of inferred profiles. Examples are provided based on simulated data as well.