title: Characterizing Cancer-Specific Networks by Integrating TCGA Data.
abstract: The Cancer Genome Atlas (TCGA) generates comprehensive genomic data for thousands of patients over more than 20 cancer types. TCGA data are typically whole-genome measurements of multiple genomic features, such as DNA copy numbers, DNA methylation, and gene expression, providing unique opportunities for investigating cancer mechanism from multiple molecular and regulatory layers. We propose a Bayesian graphical model to systemically integrate multi-platform TCGA data for inference of the interactions between different genomic features either within a gene or between multiple genes. The presence or absence of edges in the graph indicates the presence or absence of conditional dependence between genomic features. The inference is restricted to genes within a known biological network, but can be extended to any sets of genes. Applying the model to the same genes using patient samples in two different cancer types, we identify network components that are common as well as different between cancer types. The examples and codes are available at https://www.ma.utexas.edu/users/yxu/software.html.
authors.affiliation: Department of Statistics and Data Sciences, The University of Texas at Austin, Austin, TX, USA.
authors.forename: Yanxun
authors.initials: Y
authors.lastname: Xu
authors.affiliation: Northshore University HealthSystem, Evanston, IL, USA.
authors.forename: Yitan
authors.initials: Y
authors.lastname: Zhu
authors.affiliation: Department of Mathematics, The University of Texas at Austin, Austin, TX, USA.
authors.forename: Peter
authors.initials: P
authors.lastname: Müller
authors.affiliation: School of Public Health and Information Sciences, The University of Louisville, Louisville, KY, USA.
authors.forename: Riten
authors.initials: R
authors.lastname: Mitra
authors.affiliation: Northshore University HealthSystem, Evanston, IL, USA. ; Department of Public Health Sciences, The University of Chicago, Chicago, IL, USA.
authors.forename: Yuan
authors.initials: Y
authors.lastname: Ji
keywords: Bayesian graphical model; TCGA; differential networks; genomics; integration; network