Department of Statistics
Integrative Directed Cyclic Graphical Models with Heterogeneous Samples
In this talk, I will introduce hierarchical directed cyclic graphical models to infer gene networks by integrating genomic data across platforms and across diseases. The proposed model takes into account tumor heterogeneity. In the case of data that can be naturally divided into known groups, we propose to connect graphs by introducing a hierarchical prior across group-specific graphs, including a correlation on edge strengths across graphs. A novel thresholding prior is applied to induce sparsity of the estimated networks and its connection to spike-and-slab prior and non-local prior will also be discussed. In the case of unknown groups, we cluster subjects into subpopulations and jointly estimate cluster-specific gene networks, again using similar hierarchical priors across clusters. Two applications with multiplatform genomic data for multiple cancers will be presented to illustrate the utility of our model. I will also briefly mention my other work.
Wednesday, 1/24/2018, 11:30 AM, BLOC 457