The experimental design that will be carried out to evaluate a nucleic acid quantification hypothesis determines the cost and feasibility of digital polymerase chain reaction (digital PCR) studies. Experiment design involves the calculation of the number of technical measurement replicates and the determination of the characteristics of those replicates, and this in accordance with the capabilities of the available digital PCR platform. Available digital PCR power analyses suffer from one or more of the following limitations: narrow scope, unrealistic assumptions, no sufficient detail for replication, lack of source code and user-friendly software. Here, we discuss the nature of six parameters that affect the statistical power, i.e., desired effect size, total number of partitions, fraction of positive partitions, number of replicate measurements, between-replicate variance, and significance level. We also show to what extent these parameters affect power, and argue that careful design of experiments is needed to achieve the desired power. A web tool, dPowerCalcR, that allows interactive calculation of statistical power and optimization of the experimental design is available.

评估核酸定量假设的实验设计决定了数字聚合酶链反应（数字PCR）研究的成本和可行性。实验设计包括计算技术测量重复样本的数量以及确定这些重复样本的特征，这要与可用的数字PCR平台的功能相一致。可用的数字PCR功率分析受到以下一个或多个限制：范围狭窄，不切实际的假设，没有足够的详细信息可复制，缺少源代码和用户友好的软件。在这里，我们讨论了影响统计功效的六个参数的性质，即所需的效果大小，分区总数，正分区分数，重复测量次数，重复之间的差异和显着性水平。我们还显示了这些参数在多大程度上影响功率，并指出需要仔细设计实验才能获得所需的功率。可以使用网络工具dPowerCalcR，该工具允许交互式计算统计功效并优化实验设计。