Integrative Prescreening in Analysis of Multiple Cancer Genomic Studies

• Activity Number: 2
• Type: Invited
• Date/Time: Sunday, July 31, 2011 : 2:00 PM to 3:50 PM
• Sponsor: IMS
• Author(s): Rui Song*+ and Jian Huang and Shuangge Ma
• Companies: Colorado State University and University of Iowa and Yale University

Abstract

In high throughput cancer genomic studies, results from analysis of single datasets often suffer from a lack of reproducibility because of small sample sizes. Integrative analysis can effectively pool and analyze multiple datasets and provides a cost effective way to improve reproducibility. In integrative analysis, simultaneously analyzing all genes profiled may incur high computational cost. A computationally affordable remedy is prescreening, which fits marginal models, can be conducted in a parallel manner, and has low computational cost. In this article, we investigate integrative prescreening with multiple cancer genomic datasets. Theoretical properties are established. Simulation shows that the proposed integrative prescreening has better finite sample performance than alternatives including prescreening with individual datasets, an intensity approach, and meta-analysis. We apply the proposed approach to microarray gene profiling studies on liver and pancreatic cancers