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Digital PCR partition classification

Matthijs Vynck (UGent) , Yao Chen (UGent) , David Gleerup (UGent) , Jo Vandesompele (UGent) , Wim Trypsteen (UGent) , Antoon Lievens (UGent) , Olivier Thas (UGent) and Ward De Spiegelaere (UGent)
(2023) CLINICAL CHEMISTRY. 69(9). p.976-900
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Abstract
Background Partition classification is a critical step in the digital PCR data analysis pipeline. A range of partition classification methods have been developed, many motivated by specific experimental setups. An overview of these partition classification methods is lacking and their comparative properties are often unclear, likely impacting the proper application of these methods. Content This review provides a summary of all available digital PCR partition classification approaches and the challenges they aim to overcome, serving as a guide for the digital PCR practitioner wishing to apply them. We additionally discuss strengths and weaknesses of these methods, which can further guide practitioners in vigilant application of these existing methods. This review provides method developers with ideas for improving methods or designing new ones. The latter is further stimulated by our identification and discussion of application gaps in the literature, for which there are currently no or few methods available. Summary This review provides an overview of digital PCR partition classification methods, their properties, and potential applications. Ideas for further advances are presented and may bolster method development.
Keywords
AMPLIFICATION

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MLA
Vynck, Matthijs, et al. “Digital PCR Partition Classification.” CLINICAL CHEMISTRY, vol. 69, no. 9, Oxford Univ Press Inc, 2023, pp. 976–900, doi:10.1093/clinchem/hvad063.
APA
Vynck, M., Chen, Y., Gleerup, D., Vandesompele, J., Trypsteen, W., Lievens, A., … De Spiegelaere, W. (2023). Digital PCR partition classification. CLINICAL CHEMISTRY, 69(9), 976–900. https://doi.org/10.1093/clinchem/hvad063
Chicago author-date
Vynck, Matthijs, Yao Chen, David Gleerup, Jo Vandesompele, Wim Trypsteen, Antoon Lievens, Olivier Thas, and Ward De Spiegelaere. 2023. “Digital PCR Partition Classification.” CLINICAL CHEMISTRY 69 (9): 976–900. https://doi.org/10.1093/clinchem/hvad063.
Chicago author-date (all authors)
Vynck, Matthijs, Yao Chen, David Gleerup, Jo Vandesompele, Wim Trypsteen, Antoon Lievens, Olivier Thas, and Ward De Spiegelaere. 2023. “Digital PCR Partition Classification.” CLINICAL CHEMISTRY 69 (9): 976–900. doi:10.1093/clinchem/hvad063.
Vancouver
1.
Vynck M, Chen Y, Gleerup D, Vandesompele J, Trypsteen W, Lievens A, et al. Digital PCR partition classification. CLINICAL CHEMISTRY. 2023;69(9):976–900.
IEEE
[1]
M. Vynck et al., “Digital PCR partition classification,” CLINICAL CHEMISTRY, vol. 69, no. 9, pp. 976–900, 2023.
@article{01H4FS4V7P9R0RD9VCG7SRCBFT,
  abstract     = {{Background
Partition classification is a critical step in the digital PCR data analysis pipeline. A range of partition classification methods have been developed, many motivated by specific experimental setups. An overview of these partition classification methods is lacking and their comparative properties are often unclear, likely impacting the proper application of these methods.

Content
This review provides a summary of all available digital PCR partition classification approaches and the challenges they aim to overcome, serving as a guide for the digital PCR practitioner wishing to apply them. We additionally discuss strengths and weaknesses of these methods, which can further guide practitioners in vigilant application of these existing methods. This review provides method developers with ideas for improving methods or designing new ones. The latter is further stimulated by our identification and discussion of application gaps in the literature, for which there are currently no or few methods available.

Summary
This review provides an overview of digital PCR partition classification methods, their properties, and potential applications. Ideas for further advances are presented and may bolster method development.}},
  author       = {{Vynck, Matthijs and Chen, Yao and Gleerup, David and Vandesompele, Jo and Trypsteen, Wim and Lievens, Antoon and Thas, Olivier and De Spiegelaere, Ward}},
  issn         = {{0009-9147}},
  journal      = {{CLINICAL CHEMISTRY}},
  keywords     = {{AMPLIFICATION}},
  language     = {{eng}},
  number       = {{9}},
  pages        = {{976--900}},
  publisher    = {{Oxford Univ Press Inc}},
  title        = {{Digital PCR partition classification}},
  url          = {{http://doi.org/10.1093/clinchem/hvad063}},
  volume       = {{69}},
  year         = {{2023}},
}
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