qPCR data analysis: Better results through iconoclasm

The standard approach for quantitative estimation of genetic materials with qPCR is calibration with known concentrations for the target substance, in which estimates of the quantification cycle (C(q)) are fitted to a straight-line function of log(N(0)), where N(0) is the initial number of target molecules. The location of C(q) for the unknown on this line then yields its N(0). The most widely used definition for C(q) is an absolute threshold that falls in the early growth cycles. This usage is flawed as commonly implemented: threshold set very close to the baseline level, which is estimated separately, from designated "baseline cycles." The absolute threshold is especially poor for dealing with the scale variability often observed for growth profiles. Scale-independent markers, like the first derivative maximum (FDM) and a relative threshold (C(r)) avoid this problem. We describe improved methods for estimating these and other C(q) markers and their standard errors, from a nonlinear algorithm that fits growth profiles to a 4-parameter log-logistic function plus a baseline function. By examining six multidilution, multireplicate qPCR data sets, we find that nonlinear expressions are often preferred statistically for the dependence of C(q) on log(N(0)). This means that the amplification efficiency E depends on N(0), in violation of another tenet of qPCR analysis. Neglect of calibration nonlinearity leads to biased estimates of the unknown. By logic, E estimates from calibration fitting pertain to the earliest baseline cycles, not the early growth cycles used to estimate E from growth profiles for single reactions. This raises concern about the use of the latter in lengthy extrapolations to estimate N(0). Finally, we observe that replicate ensemble standard deviations greatly exceed predictions, implying that much better results can be achieved from qPCR through better experimental procedures, which likely include reducing pipette volume uncertainty.