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.
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Conflict of interest
Biogazelle provided support in the form of salaries for JV, but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. MV and OT have no conflict of interest to declare.
All data used is publicly available and referenced to in the main text. All code needed to reproduce our analyses is available at https://github.com/CenterForStatistics-UGent/dPowerCalcR.
Electronic Supplementary Material (ESM) contains additional details on the derivation of the power equations, the sources of variation, the contribution of different sources of variation, additional figures of power curves for some specific designs, and the R/Shiny application.
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Vynck, M., Vandesompele, J. & Thas, O. On determining the power of digital PCR experiments. Anal Bioanal Chem 410, 5731–5739 (2018). https://doi.org/10.1007/s00216-018-1212-6
- Digital PCR