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Analytical and Bioanalytical Chemistry

, Volume 410, Issue 23, pp 5731–5739 | Cite as

On determining the power of digital PCR experiments

  • Matthijs VynckEmail author
  • Jo Vandesompele
  • Olivier Thas
Paper in Forefront

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.

Keywords

Digital PCR Power Design Replicates Variance 

Notes

Compliance with ethical standards

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.

Supplementary material

216_2018_1212_MOESM1_ESM.pdf (773 kb)
(PDF 772 KB)

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Matthijs Vynck
    • 1
    Email author
  • Jo Vandesompele
    • 2
    • 3
    • 4
    • 5
  • Olivier Thas
    • 1
    • 6
    • 7
    • 8
  1. 1.Department of Data Analysis and Mathematical ModellingGhent UniversityGhentBelgium
  2. 2.Center for Medical GeneticsGhent UniversityGhentBelgium
  3. 3.Bioinformatics Institute Ghent: from Nucleotides to Networks (BIG N2N)Ghent UniversityGhentBelgium
  4. 4.Cancer Research Institute GhentGhent UniversityGhentBelgium
  5. 5.BiogazelleZwijnaardeBelgium
  6. 6.Bioinformatics Institute Ghent: from Nucleotides to Networks (BIG N2N)Ghent UniversityGhentBelgium
  7. 7.Cancer Research Institute GhentGhent UniversityGhentBelgium
  8. 8.National Institute for Applied Statistics Research Australia (NIASRA), School of Mathematics and Applied StatisticsUniversity of WollongongWollongongAustralia

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