Integrative Analysis of DNA Copy Number and Gene Expression Data

• Activity Number: 234
• Type: Contributed
• Date/Time: Monday, August 1, 2011 : 2:00 PM to 3:50 PM
• Sponsor: Section on Bayesian Statistical Science
• Author(s): Runqi Lin*+ and Xinlei "Sherry" Wang and Guanghua "Andy" Xiao
• Companies: Southern Methodist University and Southern Methodist University and The University of Texas Southwestern Medical Center
• Keywords: Bayesian methods ; Comparative genomic hybridization (CGH) ; Single nucleotide polymorphism (SNP) ; Hidden Markov model (HMM)

Abstract

Array comparative genomic hybridization (CGH) and single nucleotide polymorphism (SNP) arrays can be used to detect genomic DNA copy number alterations, which are closely related to the development and progression of cancer. Such alterations, including amplifications and deletions, can result in significant changes in gene expression. A gene is called a tumor driver gene if its copy number variation leads to the change in gene expression, and hence plays a key role in tumor genesis. Integrative analysis of the copy number data (i.e., the array CGH or SNP data) and gene expression data simultaneously can not only improve the statistical power for identifying the tumor driver genes, but also provide a comprehensive picture of biological mechanisms. While a large number of approaches have been proposed to analyze the copy number data alone, there is still lack of statistical methodology for integrative analysis of the copy number and gene expression data. In this paper, we will adopt a Bayesian approach relying on the hidden Markov model (HMM) to incorporate the information from the gene expression and copy number data and identify tumor driver genes.