Stamoulis Catherine, Betensky Rebecca A
IEEE/ACM Trans Comput Biol Bioinform. 2016 May-Jun;13(3):584-91. doi: 10.1109/TCBB.2015.2448077.
We aim to improve the performance of the previously proposed signal decomposition matched filtering (SDMF) method [26] for the detection of copy-number variations (CNV) in the human genome. Through simulations, we show that the modified SDMF is robust even at high noise levels and outperforms the original SDMF method, which indirectly depends on CNV frequency. Simulations are also used to develop a systematic approach for selecting relevant parameter thresholds in order to optimize sensitivity, specificity and computational efficiency. We apply the modified method to array CGH data from normal samples in the cancer genome atlas (TCGA) and compare detected CNVs to those estimated using circular binary segmentation (CBS) [19], a hidden Markov model (HMM)-based approach [11] and a subset of CNVs in the Database of Genomic Variants. We show that a substantial number of previously identified CNVs are detected by the optimized SDMF, which also outperforms the other two methods.
我们旨在改进先前提出的信号分解匹配滤波（SDMF）方法[26]，以用于检测人类基因组中的拷贝数变异（CNV）。通过模拟，我们表明改进后的SDMF即使在高噪声水平下也具有鲁棒性，并且优于原始的SDMF方法，原始方法间接依赖于CNV频率。模拟还用于开发一种系统方法来选择相关参数阈值，以优化灵敏度、特异性和计算效率。我们将改进后的方法应用于癌症基因组图谱（TCGA）中正常样本的阵列比较基因组杂交（array CGH）数据，并将检测到的CNV与使用循环二元分割（CBS）[19]、基于隐马尔可夫模型（HMM）的方法[11]以及基因组变异数据库中的一部分CNV估计值进行比较。我们表明，优化后的SDMF检测到了大量先前鉴定出的CNV，并且也优于其他两种方法。
