Tellinghuisen Joel
Department of Chemistry, Vanderbilt University, Nashville, TN 37235, USA.
Life (Basel). 2021 Jul 14;11(7):693. doi: 10.3390/life11070693.
Methods for estimating the qPCR amplification efficiency from data for single reactions are tested on six multireplicate datasets, with emphasis on their performance as a function of the range of cycles - included in the analysis. The two-parameter exponential growth (EG) model that has been relied upon almost exclusively does not allow for the decline of () with increasing cycle number through the growth region and accordingly gives low-biased estimates. Further, the standard procedure of "baselining"-separately estimating and subtracting a baseline before analysis-leads to reduced precision. The three-parameter logistic model (LRE) does allow for such decline and includes a parameter that represents through the baseline region. Several four-parameter extensions of this model that accommodate some asymmetry in the growth profiles but still retain the significance of are tested against the LRE and EG models. The recursion method of Carr and Moore also describes a declining () but tacitly assumes = 2 in the baseline region. Two modifications that permit varying are tested, as well as a recursion method that directly fits () to a sigmoidal function. All but the last of these can give estimates that agree fairly well with calibration-based estimates but perform best when the calculations are extended to only about one cycle below the first-derivative maximum (FDM). The LRE model performs as well as any of the four-parameter forms and is easier to use. Its proper implementation requires fitting to it plus a suitable baseline function, which typically requires four-six adjustable parameters in a nonlinear least-squares fit.
从单反应数据估计定量聚合酶链反应（qPCR）扩增效率的方法在六个多重复数据集中进行了测试，重点关注其作为分析中所包含循环范围函数的性能。几乎完全依赖的双参数指数增长（EG）模型不允许在增长区域内随着循环数增加而下降，因此给出的估计值存在低偏差。此外，“基线确定”的标准程序——在分析前单独估计并减去基线——会导致精度降低。三参数逻辑模型（LRE）确实允许这种下降，并且包括一个在基线区域代表的参数。该模型的几个四参数扩展形式，虽然能适应增长曲线中的一些不对称性，但仍保留的重要性，与LRE和EG模型进行了对比测试。卡尔和摩尔的递归方法也描述了下降的情况，但在基线区域隐含地假设 = 2。测试了两种允许变化的修改方法，以及一种直接将拟合到S形函数的递归方法。除了最后一种方法外，所有这些方法给出的估计值与基于校准的估计值相当吻合，但当计算扩展到仅比一阶导数最大值（FDM）低约一个循环时性能最佳。LRE模型的性能与任何四参数形式一样好，并且更易于使用。其正确实施需要将其与合适的基线函数进行拟合，这通常在非线性最小二乘拟合中需要四到六个可调参数。
