Digital PCR Partition Classification.

Matthijs Vynck, Yao Chen, David Gleerup, Jo Vandesompele, Wim Trypsteen, Antoon Lievens, Olivier Thas, Ward De Spiegelaere

Journal: Clinical chemistry 2023;69(9):976-990

PMID: 37401391

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.

© American Association for Clinical Chemistry 2023. All rights reserved. For permissions, please e-mail: [email protected].

Address: Digital PCR Consortium, Ghent University, Ghent, Belgium.; Department of Morphology, Imaging, Orthopedics, Rehabilitation and Nutrition, Faculty of Veterinary Medicine, Ghent University, Ghent, Belgium.; Department of Applied Mathematics, Computer Science and Statistics, Faculty of Sciences, Ghent University, Ghent, Belgium.; OncoRNALab, Cancer Research Institute Ghent, Ghent, Belgium.; Department of Biomolecular Medicine, Ghent University, Ghent, Belgium.; Center for Medical Genetics, Ghent University, Ghent, Belgium.; CellCarta, Zwijnaarde, Belgium.; BASF Innovation Center Ghent, Zwijnaarde, Belgium.; Data Science Institute, I-BioStat, Hasselt University, Hasselt, Belgium.; National Institute for Applied Statistics Research Australia, School of Mathematics and Applied Statistics, University of Wollongong, Wollongong, Australia.
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