Chen Yao, De Spiegelaere Ward, Vynck Matthijs, Trypsteen Wim, Gleerup David, Vandesompele Jo, Thas Olivier
Department of Applied Mathematics, Computer Science and Statistics, Ghent University, 9000 Ghent, Belgium.
Digital PCR Center (DIGPCR), Ghent University, 9000 Ghent, Belgium.
iScience. 2025 Jan 8;28(3):111772. doi: 10.1016/j.isci.2025.111772. eCollection 2025 Mar 21.
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.
数字PCR（dPCR）是一种用于核酸定量的精确技术，但由于许多现有方法所依据的假设不成立，方差估计仍然是一个挑战。为了解决这个问题，我们提出了两种通用方法，即非参数方差（NonPVar）和二项式方差（BinomVar），用于计算dPCR数据中的方差。这些方法使用模拟数据和经验数据进行评估，并纳入了常见的变异性来源。与传统方法不同，我们的方法具有灵活性，适用于分区计数的复杂函数，如拷贝数变异（CNV）、丰度分数和DNA完整性。我们提供了一个R Shiny应用程序，以方便方法的选择和实施。我们的研究结果表明，这些方法提高了准确性和适应性，为dPCR实验中的不确定性估计提供了强大的工具。
