Comparing real-time quantitative polymerase chain reaction analysis methods for precision, linearity, and accuracy of estimating amplification efficiency

New methods are used to compare seven qPCR analysis methods for their performance in estimating the quantification cycle (Cq) and amplification efficiency (E) for a large test data set (94 samples for each of 4 dilutions) from a recent study. Precision and linearity are assessed using chi-square (χ2), which is the minimized quantity in least-squares (LS) fitting, equivalent to the variance in unweighted LS, and commonly used to define statistical efficiency. All methods yield Cqs that vary strongly in precision with the starting concentration N0, requiring weighted LS for proper calibration fitting of Cq vs log(N0). Then χ2 for cubic calibration fits compares the inherent precision of the Cqs, while increases in χ2 for quadratic and linear fits show the significance of nonlinearity. Nonlinearity is further manifested in unphysical estimates of E from the same Cq data, results which also challenge a tenet of all qPCR analysis methods — that E is constant throughout the baseline region. Constant-threshold (Ct) methods underperform the other methods when the data vary considerably in scale, as these data do.