Bayesian Random Segmentation Models to Identify Shared Copy Number Aberrations for Array CGH Data

• 成果类型: Article
• 署名作者: Baladandayuthapani, Veerabhadran; Ji, Yuan; Talluri, Rajesh; Nieto-Barajas, Luis E.; Morris, Jeffrey S.
• 署名单位: University of Texas System; UTMD Anderson Cancer Center; University of Texas System; UTMD Anderson Cancer Center; Texas A&M University System; Texas A&M University College Station; Instituto Tecnologico Autonomo de Mexico
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1198/jasa.2010.ap09250
• 发表日期: 2010
• 页码: 1358-1375
• 关键词: comparative genomic hybridization; high-resolution analysis; microarray analysis; mass-spectrometry; gene-expression; regression; FRAMEWORK; cell

摘要

Array-based comparative genomic hybridization (aCGH) is a high-resolution, high-throughput technique for studying the genetic basis of cancer. The resulting data consist of log fluorescence ratios as a function of the genomic DNA location and provide a cytogenetic representation of the relative DNA copy number variation. Analysis of such data typically involves estimating the underlying copy number state at each location and segmenting regions of DNA with similar copy number states. Most current methods proceed by modeling a single sample/array at a time, and thus fail to borrow strength across multiple samples to infer shared regions of copy number aberrations. We propose a hierarchical Bayesian random segmentation approach for modeling aCGH data that uses information across arrays from a common population to yield segments of shared copy number changes. These changes characterize the underlying population and allow us to compare different population aCGH profiles to assess which regions of the genome have differential alterations. Our method, which we term Bayesian detection of shared aberrations in aCGH (BDSAScgh), is based on a unified Bayesian hierarchical model that allows us to obtain probabilities of alteration states as well as probabilities of differential alterations that correspond to local false discovery rates for both single and multiple groups. We evaluate the operating characteristics of our method via simulations and an application using a lung cancer aCGH data set. This article has supplementary material online.