Flexible methods for uncertainty estimation of digital PCR data
Yao Chen,
Ward De Spiegelaere,
Matthijs Vynck,
Wim Trypsteen,
David Gleerup,
Jo Vandesompele,
Olivier Thas
Affiliations
Yao Chen
Department of Applied Mathematics, Computer Science and Statistics, Ghent University, 9000 Ghent, Belgium; Digital PCR Center (DIGPCR), Ghent University, 9000 Ghent, Belgium; Department of Morphology, Medical Imaging, Orthopaedics, Physiotherapy and Nutrition, Ghent University, 9820 Merelbeke, Belgium
Ward De Spiegelaere
Digital PCR Center (DIGPCR), Ghent University, 9000 Ghent, Belgium; Department of Morphology, Medical Imaging, Orthopaedics, Physiotherapy and Nutrition, Ghent University, 9820 Merelbeke, Belgium
Matthijs Vynck
Digital PCR Center (DIGPCR), Ghent University, 9000 Ghent, Belgium; Department of Morphology, Medical Imaging, Orthopaedics, Physiotherapy and Nutrition, Ghent University, 9820 Merelbeke, Belgium
Wim Trypsteen
Digital PCR Center (DIGPCR), Ghent University, 9000 Ghent, Belgium; Department of Internal Medicine, Ghent University and University Hospital, 9000 Ghent, Belgium
David Gleerup
Digital PCR Center (DIGPCR), Ghent University, 9000 Ghent, Belgium; Department of Morphology, Medical Imaging, Orthopaedics, Physiotherapy and Nutrition, Ghent University, 9820 Merelbeke, Belgium
Jo Vandesompele
Digital PCR Center (DIGPCR), Ghent University, 9000 Ghent, Belgium; OncoRNALab, Center for Medical Genetics, Department of Biomolecular Medicine, Ghent University, 9000 Ghent, Belgium; Cancer Research Institute Ghent (CRIG), 9000 Ghent, Belgium; pxlence, 9000 Ghent, Belgium
Olivier Thas
Department of Applied Mathematics, Computer Science and Statistics, Ghent University, 9000 Ghent, Belgium; Digital PCR Center (DIGPCR), Ghent University, 9000 Ghent, Belgium; Data Science Institute, I-BioStat, Hasselt University, 3590 Diepenbeek, Belgium; National Institute for Applied Statistics Research Australia (NIASRA), University of Wollongong, Wollongong, NSW 2522, Australia; Corresponding author
Summary: Digital PCR (dPCR) is an accurate technique for quantifying nucleic acids, but variance estimation remains a challenge due to violations of the assumptions underlying many existing methods. To address this, we propose two generic approaches, NonPVar and BinomVar, for calculating variance in dPCR data. These methods are evaluated using simulated and empirical data, incorporating common sources of variability. Unlike classical methods, our approaches are flexible and applicable to complex functions of partition counts like copy number variation (CNV), fractional abundance, and DNA integrity. An R Shiny app is provided to facilitate method selection and implementation. Our findings demonstrate that these methods improve accuracy and adaptability, offering robust tools for uncertainty estimation in dPCR experiments.
