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.

中文翻译：

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使用测序数据进行 DNA 拷贝数研究的惩罚回归方法

为研究 DNA 拷贝数变异 (CNV) 的肿瘤和对照样本进行分析的实验所产生的高通量下一代测序 (NGS) 数据在各个方面仍然是一个挑战。在这项应用工作中，我们提供了一种使用 NGS 读取比率数据检测多个 CNV 的有效方法。该方法基于具有惩罚回归方法的多统计变化点模型，即一维融合 LASSO，该模型专为一维结构中的有序数据而设计。此外，由于路径算法根据调整参数跟踪解，因此可以有效地同时估计潜在 CNV 区域边界的数量和位置。为了调整参数选择，我们提出了一种新的改进的贝叶斯信息准则，称为 JMIC，并将提议的 JMIC 与文献中使用的三种不同的贝叶斯信息标准进行比较。仿真结果表明，与其他三个标准相比，JMIC 在调整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肿瘤细胞系 HCC1954 与其匹配的正常细胞系 BL 1954 之间的读取比的测序数据，结果与文献中发现的结果一致。