Bias and imprecision in analysis of real-time quantitative polymerase chain reaction data

Monte Carlo simulations are used to examine the bias and loss of precision that result from experimental error and analysis procedures in real-time quantitative polymerase chain reaction (PCR). In the limit of small copy numbers (N0), Poisson statistics govern the dispersion in estimates of the quantification cycle (Cq) for replicate experiments, permitting the estimation of N0 from the Cq variance, which is inversely proportional to N0. We derive corrections to expressions given previously for this determination. With increasing N0, the Poisson contribution decreases and other effects, like pipet volume uncertainty (typically >3%), dominate. Cycle-to-cycle variability in the amplification efficiency E produces scale dispersion similar to that for variability in the sensitivity of fluorescence detection. When this E variability is proportional to just the amplification (E - 1), there is insignificant effect on Cq if scale-independent definitions are used for this marker. Single-reaction analysis methods based on the exponential growth equation are inherently low-biased in E and high-biased in N0, and these biases can amount to factor-of-4 or greater error in N0. For estimating Cq, their greatest limitation is use of a constant absolute threshold, making them inefficient for data that exhibit scale variability.