Estimating Real-Time qPCR Amplification Efficiency from Single-Reaction Data.

Abstract

Methods for estimating the qPCR amplification efficiency E from data for single reactions are tested on six multireplicate datasets, with emphasis on their performance as a function of the range of cycles n1–n2 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 E(n) with increasing cycle number n 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 E0 that represents E 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 E0 are tested against the LRE and EG models. The recursion method of Carr and Moore also describes a declining E(n) but tacitly assumes E0 = 2 in the baseline region. Two modifications that permit varying E0 are tested, as well as a recursion method that directly fits E(n) to a sigmoidal function. All but the last of these can give E0 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.