Flexible methods for uncertainty estimation of digital PCR data

Abstract

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.