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 (C_q) 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 C_qs that vary strongly in precision with the starting concentration N₀, requiring weighted LS for proper calibration fitting of C_q vs log(N₀). Then χ² for cubic calibration fits compares the inherent precision of the C_qs, 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 C_q data, results which also challenge a tenet of all qPCR analysis methods — that E is constant throughout the baseline region. Constant-threshold (C_t) methods underperform the other methods when the data vary considerably in scale, as these data do.