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.
Keywords: Bayesian graphical model; TCGA; differential networks; genomics; integration; network.