Optimization of Signal Decomposition Matched Filtering (SDMF) for Improved Detection of Copy-Number Variations.
Optimization of Signal Decomposition Matched Filtering (SDMF) for Improved Detection of Copy-Number Variations.
复制标题
优化信号分解匹配过滤 (SDMF)，以改进拷贝数变异的检测。
DOI：
10.1109/tcbb.2015.2448077
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发表时间：
2016
期刊：
影响因子：
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通讯作者：
Betensky,RebeccaA
中科院分区：
文献类型：
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作者：
Stamoulis,Catherine;Betensky,RebeccaA
我们的目标是改进先前提出的检测人类基因组中拷贝数变异(CNV)的信号分解匹配滤波(SDMF)方法[26]的性能。仿真结果表明，改进后的SDMF算法在高噪声环境下仍具有较好的鲁棒性，且性能优于间接依赖于CNV频率的原始SDMF方法。为了优化灵敏度、特异度和计算效率，还利用模拟开发了一种选择相关参数阈值的系统方法。我们应用改进的方法来排列癌症基因组图谱(TCGA)中正常样本的CGH数据，并将检测到的CNV与使用循环二进制分割(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CNV的子集估计的CNV进行比较。实验结果表明，优化后的SDMF方法能够检测到大量先前识别出的CNV，其性能也优于其他两种方法。
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.