Statistical aspects of quantitative real-time PCR experiment design
Statistical aspects of quantitative real-time PCR experiment design
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DOI：
10.1016/j.ymeth.2010.01.025
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发表时间：
2010-04-01
期刊：
影响因子：
4.8
通讯作者：
Tichopad, Ales
中科院分区：
文献类型：
--
作者：
Kitchen, Robert R.;Kubista, Mikael;Tichopad, Ales
使用实时定量聚合酶链式反应测试假说的实验受到技术和生物可变性的限制；我们寻求通过优化使用生物和技术复制来最大限度地减少混淆可变性的来源。实验设计的质量通常是通过计算其预期威力来评估的。这种计算依赖于对每组样本测量的预期方差和治疗效果大小的了解；对此的估计往往是不知情和不可靠的。在这里，我们介绍了一种方法，该方法利用小型先导性研究来估计生物和技术差异，以改进后续大型实验的设计。我们在实验设计的几个“水平”上测量方差贡献，并提供一种使用这些信息来预测分析的总方差和预期功率的方法。通过对几种牛组织类型中代表性基因的方差分析，验证了该方法的有效性。我们还讨论了根据测量的感兴趣基因的方差分量对参考基因进行归一化的效果。最后，我们描述了这些方法的一个软件实现，PowerNest，它使用户有机会输入来自先导研究的数据，并交互地修改分析的设计。该软件自动计算较大试验的预期方差、统计量和最优设计。PowerNest使研究人员能够最小化总的混杂方差，并在大型研究的特定最大成本下最大化预期功率。(C)2010 Elsevier Inc.保留所有权利。
Experiments using quantitative real-time PCR to test hypotheses are limited by technical and biological variability; we seek to minimise sources of confounding variability through optimum use of biological and technical replicates. The quality of an experiment design is commonly assessed by calculating its prospective power. Such calculations rely on knowledge of the expected variances of the measurements of each group of samples and the magnitude of the treatment effect; the estimation of which is often uninformed and unreliable. Here we introduce a method that exploits a small pilot study to estimate the biological and technical variances in order to improve the design of a subsequent large experiment. We measure the variance contributions at several 'levels' of the experiment design and provide a means of using this information to predict both the total variance and the prospective power of the assay. A validation of the method is provided through a variance analysis of representative genes in several bovine tissue-types. We also discuss the effect of normalisation to a reference gene in terms of the measured variance components of the gene of interest. Finally, we describe a software implementation of these methods, powerNest, that gives the user the opportunity to input data from a pilot study and interactively modify the design of the assay. The software automatically calculates expected variances, statistical power, and optimal design of the larger experiment. powerNest enables the researcher to minimise the total confounding variance and maximise prospective power for a specified maximum cost for the large study. (C) 2010 Elsevier Inc. All rights reserved.