Comparative analyses of seven algorithms for copy number variant identification from single nucleotide polymorphism arrays.
Comparative analyses of seven algorithms for copy number variant identification from single nucleotide polymorphism arrays.
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DOI：
10.1093/nar/gkq040
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发表时间：
2010-05
影响因子：
14.9
通讯作者：
Li YJ
中科院分区：
文献类型：
--
作者：
Dellinger AE;Saw SM;Goh LK;Seielstad M;Young TL;Li YJ
在全基因组单核苷酸多态性阵列中推断的拷贝数变异（CNV）的确定在遗传变异疾病关联中显示出越来越多的实用性。几种CNV检测方法是可用的，但CNV调用阈值和特征存在差异。我们评估了七种方法的相对性能：循环二进制分割，CNVEGFR2，CNvPartition，DNA的增益和损失，Nexus算法，PennCNV和QuantiSNP。测试数据包括来自近视风险因素新加坡队列研究（SCORM）的真实的和模拟的Illumina HumHap 550数据，以及来自Affyoung 6.0和平台独立分布的模拟数据。在进行全面分析之前，提出了归一化单例比（NSR）作为参数优化的度量。我们使用10个SCORM样本优化每个方法的参数设置，然后使用100个SCORM样本在最佳参数下评估方法性能。通过模拟研究评估统计功效、假阳性率和受试者工作特征（ROC）曲线残差。由NSR和ROC曲线残差确定的最佳参数在数据集之间是一致的。QuantiSNP在大多数数据集上优于基于ROC曲线残差的其他方法。Nexus Rank和SNPRank具有低特异性和高功效。Nexus Rank调用超大CNV。PennCNV检测到的CNV数量是最少的。
Determination of copy number variants (CNVs) inferred in genome wide single nucleotide polymorphism arrays has shown increasing utility in genetic variant disease associations. Several CNV detection methods are available, but differences in CNV call thresholds and characteristics exist. We evaluated the relative performance of seven methods: circular binary segmentation, CNVFinder, cnvPartition, gain and loss of DNA, Nexus algorithms, PennCNV and QuantiSNP. Tested data included real and simulated Illumina HumHap 550 data from the Singapore cohort study of the risk factors for Myopia (SCORM) and simulated data from Affymetrix 6.0 and platform-independent distributions. The normalized singleton ratio (NSR) is proposed as a metric for parameter optimization before enacting full analysis. We used 10 SCORM samples for optimizing parameter settings for each method and then evaluated method performance at optimal parameters using 100 SCORM samples. The statistical power, false positive rates, and receiver operating characteristic (ROC) curve residuals were evaluated by simulation studies. Optimal parameters, as determined by NSR and ROC curve residuals, were consistent across datasets. QuantiSNP outperformed other methods based on ROC curve residuals over most datasets. Nexus Rank and SNPRank have low specificity and high power. Nexus Rank calls oversized CNVs. PennCNV detects one of the fewest numbers of CNVs.
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影响因子：
30.8
作者：
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通讯作者：
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影响因子：
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通讯作者：
Lander, Eric S.