On non-detects in qPCR data.
On non-detects in qPCR data.
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DOI：
10.1093/bioinformatics/btu239
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发表时间：
2014-08-15
期刊：
影响因子：
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通讯作者：
Almudevar A
中科院分区：
文献类型：
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作者：
McCall MN;McMurray HR;Land H;Almudevar A
实时定量PCR（qPCR）是测量基因表达最广泛使用的方法之一。尽管在qPCR实验室方案、标准化和统计分析方面进行了广泛的研究，但很少有人关注qPCR未检测-那些未能产生最小量信号的反应。结果：我们发现，处理qPCR未检出的常见方法会导致有偏倚的推断。此外，我们表明，非检测不代表数据完全随机缺失，可能代表缺失数据发生不随机。我们提出了一个缺失数据机制的模型，并开发了一种方法来直接建模为缺失数据的非检测。最后，我们表明，我们的方法在估计绝对和差异基因表达时，在相当大的偏差减少的结果。可用性和实现：所提出的算法在R包中实现，nondetects。该软件包还包含本文中使用的三个示例数据集的原始数据。该软件包可在http://mnmccall.com/software上免费获得，并作为Bioconductor项目的一部分。联系方式：mccallm@gmail.com
Motivation: Quantitative real-time PCR (qPCR) is one of the most widely used methods to measure gene expression. Despite extensive research in qPCR laboratory protocols, normalization and statistical analysis, little attention has been given to qPCR non-detects—those reactions failing to produce a minimum amount of signal. Results: We show that the common methods of handling qPCR non-detects lead to biased inference. Furthermore, we show that non-detects do not represent data missing completely at random and likely represent missing data occurring not at random. We propose a model of the missing data mechanism and develop a method to directly model non-detects as missing data. Finally, we show that our approach results in a sizeable reduction in bias when estimating both absolute and differential gene expression. Availability and implementation: The proposed algorithm is implemented in the R package, nondetects. This package also contains the raw data for the three example datasets used in this manuscript. The package is freely available at http://mnmccall.com/software and as part of the Bioconductor project. Contact: mccallm@gmail.com