Lee Jaeeun, Chen Jie
Division of Biostatistics and Data Science, Department of Population Health Sciences, Medical College of Georgia, Augusta University, Augusta, GA 30912, USA.
Stat Appl Genet Mol Biol. 2019 May 30;18(4):sagmb-2018-0001. doi: 10.1515/sagmb-2018-0001.
Modeling the high-throughput next generation sequencing (NGS) data, resulting from experiments with the goal of profiling tumor and control samples for the study of DNA copy number variants (CNVs), remains to be a challenge in various ways. In this application work, we provide an efficient method for detecting multiple CNVs using NGS reads ratio data. This method is based on a multiple statistical change-points model with the penalized regression approach, 1d fused LASSO, that is designed for ordered data in a one-dimensional structure. In addition, since the path algorithm traces the solution as a function of a tuning parameter, the number and locations of potential CNV region boundaries can be estimated simultaneously in an efficient way. For tuning parameter selection, we then propose a new modified Bayesian information criterion, called JMIC, and compare the proposed JMIC with three different Bayes information criteria used in the literature. Simulation results have shown the better performance of JMIC for tuning parameter selection, in comparison with the other three criterion. We applied our approach to the sequencing data of reads ratio between the breast tumor cell lines HCC1954 and its matched normal cell line BL 1954 and the results are in-line with those discovered in the literature.
对以分析肿瘤样本和对照样本的DNA拷贝数变异（CNV）为目的的实验所产生的高通量下一代测序（NGS）数据进行建模，在诸多方面仍然是一项挑战。在本应用工作中，我们提供了一种利用NGS读段比率数据检测多个CNV的有效方法。该方法基于一个带有惩罚回归方法（一维融合套索回归，专为一维结构的有序数据设计）的多重统计变化点模型。此外，由于路径算法将解作为一个调优参数的函数进行追踪，潜在CNV区域边界的数量和位置能够以一种有效的方式同时得到估计。对于调优参数选择，我们随后提出了一种新的改进型贝叶斯信息准则，称为JMIC，并将所提出的JMIC与文献中使用的三种不同贝叶斯信息准则进行比较。模拟结果表明，与其他三种准则相比，JMIC在调优参数选择方面具有更好的性能。我们将我们的方法应用于乳腺癌细胞系HCC1954与其匹配的正常细胞系BL 1954之间的读段比率测序数据，结果与文献中发现的结果一致。
