Bayesian Nonparametric Variable Selection as an Exploratory Tool for Discovering Differentially Expressed Genes

• Activity Number: 181
• Type: Contributed
• Date/Time: Monday, July 30, 2012 : 10:30 AM to 12:20 PM
• Sponsor: Section on Bayesian Statistical Science
• Author(s): Babak Shahbaba*+ and Wesley Johnson
• Companies: University of California at Irvine and University of California at Irvine
• Keywords: Bayesian Inference ; Gene Expression ; Dirichlet Process Mixtures ; Large-scale Scientific Studies

Abstract

High-throughput scientific studies involving no clear a'priori hypothesis are common. For example, a large-scale genomic study of a disease may examine thousands of genes without hypothesizing that any specific gene is responsible for the disease. In these studies, the objective is to explore a large number of possible factors (e.g. genes) in order to identify a small number that will be considered in follow-up studies that tend to be more thorough and on smaller scales. For large-scale studies, we propose a nonparametric Bayesian approach based on random partition models. Our model thus divides the set of candidate factors into several subgroups according to their degrees of relevance, or potential effect, in relation to the outcome of interest. The model allows for a latent rank to be assigned to each factor according to the overall potential importance of its group. The posterior expectation or mode of these ranks is used to set up a threshold for selecting such factors. Using simulated data, we demonstrate that our approach could be quite effective in finding relevant genes compared to several alternative methods. We apply our model to two large-scale studies.