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Analytical and Bioanalytical Chemistry

, Volume 409, Issue 25, pp 5919–5931 | Cite as

Quality control of digital PCR assays and platforms

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

Abstract

Digital polymerase chain reaction (digital PCR, dPCR) is a direct nucleic acid quantification method, thus requiring no standard curves unlike quantitative real-time PCR (qPCR). Nevertheless, evaluation of the linear dynamic range, accuracy, and precision of an assay or platform is recommended, as there are several potential causes of important non-linearity, bias, and imprecision. Ignoring these quality issues may lead to erroneous quantification. This necessitates an approach akin to the construction of standard curves. We study the pitfalls associated with the evaluation of such an experiment, and provide guidelines for the assessment of linearity, accuracy, and precision in dPCR experiments. We present simulation results and a case study supporting the importance of a thorough evaluation. Further, typically presented plots and statistics may not reveal problems with linearity, accuracy, or precision. We find that a robust weighted least-squares approach is highly advisable, yet may also suffer from an inflated false-positive rate. The proposed assessments are also applicable to other analyses, such as the comparison of results obtained from qPCR and dPCR. A web tool for quality evaluation, dPCalibRate, is available.

Keywords

Digital PCR Quality control Linearity Accuracy Precision 

Notes

Compliance with Ethical Standards

Conflict of interests

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 conflicts of interest to declare.

Supplementary material

216_2017_538_MOESM1_ESM.pdf (495 kb)
(PDF 495 KB)

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Copyright information

© Springer-Verlag GmbH Germany 2017

Authors and Affiliations

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

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