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The Department of Biostatistics Presents: Yang Ni, University of Texas at Austin
January 9 @ 3:30 pm - 4:30 pm
1690 SPH I
Post Doc Fellow
University of Texas at Austin
“Integrative Directed Cyclic Graphical Models with Heterogeneous Samples”
In this talk, I will introduce novel 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. Thresholding priors are applied to induce sparsity of the estimated networks. 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 discuss my other work and future directions.