Arrays based on single nucleotide polymorphisms (SNPs) have been successful for the large scale discovery of copy number variants (CNVs). However, current CNV calling algorithms still have limitations in detecting CNVs with high specificity and sensitivity, especially in case of small (<100 kb) CNVs. Therefore, this study presents a simple statistical analysis to evaluate CNV calls from SNP arrays in order to improve the noise-robustness of existing CNV calling algorithms. The proposed approach estimates local noise of log R ratios and returns the probability that a certain observation is different from this log R ratio noise level. This probability can be triggered at different thresholds to tailor specificity and/or sensitivity in a flexible way. Moreover, a comparison based on qPCR experiments showed that the proposed noise-robust CNV calls outperformed original ones for multiple threshold values.

中文翻译：

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通过 log R 比率的局部噪声估计对基于 SNP 阵列的 CNV 调用进行抗噪评估

基于单核苷酸多态性 (SNP) 的阵列已成功用于大规模发现拷贝数变体 (CNV)。然而，当前的 CNV 调用算法在检测具有高特异性和灵敏度的 CNV 方面仍然存在局限性，特别是在小的 (<100 kb) CNV 的情况下。因此，本研究提出了一种简单的统计分析来评估来自 SNP 阵列的 CNV 调用，以提高现有 CNV 调用算法的抗噪性。所提出的方法估计对数 R 比率的局部噪声，并返回某个观测值与该对数 R 比率噪声水平不同的概率。这个概率可以在不同的阈值触发，以灵活的方式定制特异性和/或灵敏度。而且，