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Quality control of digital PCR assays and platforms

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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.

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Author information

Authors and Affiliations

  1. Department of Mathematical Modelling, Statistics and Bioinformatics, 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. Biogazelle, Technologiepark 3, 9052, Zwijnaarde, Belgium

    Jo Vandesompele

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

    Olivier Thas

  6. National Institute for Applied Statistics Research Australia (NIASRA), School of Mathematics and Applied Statistics, University of 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 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.

Additional information

Supplementary material

Electronic Supplementary Material 1 (ESM1) contains additional details on data simulation, comparison of different linear model fitting procedures and full analysis results.

Data availability

All data and code needed to reproduce our analyses is available at https://github.com/CenterForStatistics-UGent/dPCalibRate.

Funding

Unfunded.

Electronic supplementary material

Below is the link to the electronic supplementary material.

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Vynck, M., Vandesompele, J. & Thas, O. Quality control of digital PCR assays and platforms. Anal Bioanal Chem 409, 5919–5931 (2017). https://doi.org/10.1007/s00216-017-0538-9

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  • DOI: https://doi.org/10.1007/s00216-017-0538-9

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