Vynck Matthijs, Chen Yao, Gleerup David, Vandesompele Jo, Trypsteen Wim, Lievens Antoon, Thas Olivier, De Spiegelaere Ward
Digital PCR Consortium, Ghent University, Ghent, Belgium.
Department of Morphology, Imaging, Orthopedics, Rehabilitation and Nutrition, Faculty of Veterinary Medicine, Ghent University, Ghent, Belgium.
Clin Chem. 2023 Sep 1;69(9):976-990. doi: 10.1093/clinchem/hvad063.
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.
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.
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.
分区分类是数字PCR数据分析流程中的关键步骤。已经开发了一系列分区分类方法，其中许多方法是由特定的实验设置推动的。目前缺乏对这些分区分类方法的概述，并且它们的比较特性往往不明确，这可能会影响这些方法的正确应用。
本综述总结了所有可用的数字PCR分区分类方法以及它们旨在克服的挑战，为希望应用这些方法的数字PCR从业者提供指导。我们还讨论了这些方法的优点和缺点，这可以进一步指导从业者谨慎应用这些现有方法。本综述为方法开发者提供了改进方法或设计新方法的思路。我们对文献中应用空白的识别和讨论进一步激发了后者，目前针对这些空白尚无或仅有很少的方法。
本综述概述了数字PCR分区分类方法、它们的特性和潜在应用。提出了进一步进展的思路，可能会推动方法开发。
