Date: 2012-11-23

Time: 14:30-15:30

Location: BURN 107

Abstract:

We propose a nonparametric Bayesian local clustering (NoB-LoC) approach for heterogeneous data. Using genomics data as an example, the NoB-LoC clusters genes into gene sets and simultaneously creates multiple partitions of samples, one for each gene set. In other words, the sample partitions are nested within the gene sets. Inference is guided by a joint probability model on all random elements. Biologically, the model formalizes the notion that biological samples cluster differently with respect to different genetic processes, and that each process is related to only a small subset of genes. These local features are importantly different from global clustering approaches such as hierarchical clustering, which create one partition of samples that applies for all genes in the data set. Furthermore, the NoB-LoC includes a special cluster of genes that do not give rise to any meaningful partition of samples. These genes could be irrelevant to the disease conditions under investigation. Similarly, for a given gene set, the NoB-LoC includes a subset of samples that do not co-cluster with other samples. The samples in this special cluster could, for example, be those whose disease subtype is not characterized by the particular gene set.

This is joint work with Juhee Lee and Yuan Ji.

Speaker

Peter Mueller (http://www.math.utexas.edu/users/pmueller/) is Professor, Department of Mathematics, University of Texas at Austin. His research interests include theory and applications of Bayesian nonparametric inference, with applications in genomics, medicine and health sciences.