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

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.