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Savitzky-Golay smoothing and differentiation for polymerase chain reaction quantification

机译：Savitzky-Golay平滑和分化聚合酶链式反应量化

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摘要

In quantitative PCR (qPCR), replicates can minimize the impact of intra-assay variation; however, inter-assay variations must be minimized to obtain a robust quantification method. The method proposed in this study uses Savitzky-Golay smoothing and differentiation (SGSD) to identify a derivative-maximum-based cycle of quantification. It does not rely on curve modeling, as is the case with many existing techniques. PCR fluorescence data sets challenged for inter-assay variations (different thermocycler units, different reagents batches, different operators, different standard curves, and different labs) were used for the evaluation. The algorithm was compared with a four-parameter logistic model (4PLM) method, the C(y)0 method, and the threshold method. The SGSD method compared favourably with all methods in terms of inter-assay variation. SGSD was statistically different from the 4PLM (P = 0.03), C(y)0 (P = 0.05), and threshold (P = 0.004) methods on relative error comparison basis. For intra-assay variations, SGSD outperformed the threshold method (P = 0.005) and equalled the 4PLM and C(y)0 methods (P 0.05) on relative error basis. Our results demonstrate that the SGSD method could potentially be an alternative to sigmoid modeling based methods (4PLM and C(y)0) when PCR data are challenged for inter-assay variations.
机译：在定量PCR（QPCR）中，重复可以最小化测定内变化的影响;然而，必须最小化测定间变型以获得鲁棒量化方法。本研究中提出的方法使用Savitzky-Golay平滑和分化（SGSD）来识别基于衍生的定量循环。它不依赖于曲线建模，就像具有许多现有技术的情况一样。用于评估，PCR荧光数据集面临分析间变化（不同的热循环单元，不同的试剂批次，不同的操作员，不同的标准曲线和不同的实验室）进行评估。将算法与四参数逻辑模型（4PLM）方法，C（Y）0方法和阈值方法进行比较。 SGSD方法在测定间变异方面有利地与所有方法相比。 SGSD与4PLM（P = 0.03），C（Y）0（P = 0.05）和相对误差比较的方法统计不同。对于测定内部变化，SGSD优于阈值方法（P = 0.005）并在相对误差的基础上等于4PLM和C（Y）0方法（P＆GT; 0.05）。我们的结果表明，当PCR数据被攻击以进行间间变化时，SGSD方法可能是基于S形的基于方法的替代方法（4PLM和C（Y）0）。

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