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On determining the power of digital PCR experiments

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Abstract

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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Author information

Authors and Affiliations

  1. Department of Data Analysis and Mathematical Modelling, Ghent University, Coupure Links 653, 9000, Ghent, Belgium

    Matthijs Vynck & Olivier Thas

  2. Center for Medical Genetics, Ghent University, De Pintelaan 185, 9000, Ghent, Belgium

    Jo Vandesompele

  3. Bioinformatics Institute Ghent: from Nucleotides to Networks (BIG N2N), Ghent University, De Pintelaan 185, 9000, Ghent, Belgium

    Jo Vandesompele

  4. Cancer Research Institute Ghent, Ghent University, De Pintelaan 185, 9000, Ghent, Belgium

    Jo Vandesompele

  5. Biogazelle, Technologiepark 3, 9052, Zwijnaarde, Belgium

    Jo Vandesompele

  6. Bioinformatics Institute Ghent: from Nucleotides to Networks (BIG N2N), Ghent University, Coupure Links 653, 9000, Ghent, Belgium

    Olivier Thas

  7. Cancer Research Institute Ghent, Ghent University, Coupure Links 653, 9000, Ghent, Belgium

    Olivier Thas

  8. National Institute for Applied Statistics Research Australia (NIASRA), School of Mathematics and Applied Statistics, University of Wollongong, Wollongong, NSW, 2522, Australia

    Olivier Thas

Authors
  1. Matthijs Vynck
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  2. Jo Vandesompele
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  3. Olivier Thas
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Corresponding author

Correspondence to Matthijs Vynck.

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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.

Additional information

Data availability

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.

Supplementary material

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.

Electronic supplementary material

Below is the link to the electronic supplementary material.

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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

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  • DOI: https://doi.org/10.1007/s00216-018-1212-6

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