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基于序列模型选择的分割方法用于检测DNA拷贝数变异。

Sequential model selection-based segmentation to detect DNA copy number variation.

作者信息

Hu Jianhua, Zhang Liwen, Wang Huixia Judy

机构信息

Department of Biostatistics, UT M. D. Anderson Cancer Center, Houston, Texas 77030, U.S.A..

School of Economics, Shanghai University, Shanghai 200444, China.

出版信息

Biometrics. 2016 Sep;72(3):815-26. doi: 10.1111/biom.12478. Epub 2016 Mar 8.

Abstract

Array-based CGH experiments are designed to detect genomic aberrations or regions of DNA copy-number variation that are associated with an outcome, typically a state of disease. Most of the existing statistical methods target on detecting DNA copy number variations in a single sample or array. We focus on the detection of group effect variation, through simultaneous study of multiple samples from multiple groups. Rather than using direct segmentation or smoothing techniques, as commonly seen in existing detection methods, we develop a sequential model selection procedure that is guided by a modified Bayesian information criterion. This approach improves detection accuracy by accumulatively utilizing information across contiguous clones, and has computational advantage over the existing popular detection methods. Our empirical investigation suggests that the performance of the proposed method is superior to that of the existing detection methods, in particular, in detecting small segments or separating neighboring segments with differential degrees of copy-number variation.

摘要

基于芯片的比较基因组杂交(CGH)实验旨在检测与某种结果(通常是疾病状态)相关的基因组畸变或DNA拷贝数变异区域。现有的大多数统计方法旨在检测单个样本或芯片中的DNA拷贝数变异。我们专注于通过同时研究来自多个组的多个样本,检测组效应变异。与现有检测方法中常见的直接分割或平滑技术不同,我们开发了一种由改进的贝叶斯信息准则指导的顺序模型选择程序。这种方法通过累积利用相邻克隆的信息提高了检测准确性,并且在计算上比现有的流行检测方法具有优势。我们的实证研究表明,所提出的方法的性能优于现有检测方法,特别是在检测小片段或分离具有不同拷贝数变异程度的相邻片段方面。

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