School of Physics, University of Edinburgh, 10 Crichton Street, Edinburgh EH8 9AB, UK.
Methods. 2010 Apr;50(4):231-6. doi: 10.1016/j.ymeth.2010.01.025. Epub 2010 Jan 28.
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.
使用定量实时 PCR 进行假设检验的实验受到技术和生物学变异性的限制；我们通过最优地使用生物学和技术重复来尽量减少混杂变异性的来源。实验设计的质量通常通过计算其预期效力来评估。这种计算依赖于对每组样本测量的预期方差和处理效果大小的知识；这些估计往往是没有根据的和不可靠的。在这里，我们介绍了一种方法，该方法利用小规模初步研究来估计生物学和技术方差，以改进随后的大型实验设计。我们在实验设计的几个“水平”上测量方差贡献，并提供了一种使用该信息来预测总方差和分析物的预期效力的方法。通过对几种牛组织类型中的代表性基因进行方差分析，对该方法进行了验证。我们还讨论了归一化到参考基因对感兴趣基因的测量方差分量的影响。最后，我们描述了这些方法的软件实现 powerNest，它为用户提供了从初步研究中输入数据并交互修改分析物设计的机会。该软件自动计算预期方差、统计效力和大型实验的最佳设计。powerNest 使研究人员能够最小化总混杂方差并为大型研究指定的最大成本最大化预期效力。
