Abstract
BackgroundAccurate adjustment for the amplification efficiency (AE) is an important part of real-time quantitative polymerase chain reaction (qPCR) experiments. The most commonly used correction strategy is to estimate the AE by dilution experiments and use this as a plug-in when efficiency correcting the ΔΔCq. Currently, it is recommended to determine the AE with high precision as this plug-in approach does not account for the AE uncertainty, implicitly assuming an infinitely precise AE estimate. Determining the AE with such precision, however, requires tedious laboratory work and vast amounts of biological material. Violation of the assumption leads to overly optimistic standard errors of the ΔΔCq, confidence intervals, and p-values which ultimately increase the type I error rate beyond the expected significance level. As qPCR is often used for validation it should be a high priority to account for the uncertainty of the AE estimate and thereby properly bounding the type I error rate and achieve the desired significance level.ResultsWe suggest and benchmark different methods to obtain the standard error of the efficiency adjusted ΔΔCq using the statistical delta method, Monte Carlo integration, or bootstrapping. Our suggested methods are founded in a linear mixed effects model (LMM) framework, but the problem and ideas apply in all qPCR experiments. The methods and impact of the AE uncertainty are illustrated in three qPCR applications and a simulation study. In addition, we validate findings suggesting that MGST1 is differentially expressed between high and low abundance culture initiating cells in multiple myeloma and that microRNA-127 is differentially expressed between testicular and nodal lymphomas.ConclusionsWe conclude, that the commonly used efficiency corrected quantities disregard the uncertainty of the AE, which can drastically impact the standard error and lead to increased false positive rates. Our suggestions show that it is possible to easily perform statistical inference of ΔΔCq, whilst properly accounting for the AE uncertainty and better controlling the false positive rate.
Highlights
Accurate adjustment for the amplification efficiency (AE) is an important part of real-time quantitative polymerase chain reaction experiments
culture initiating cells (CICs) study The Cq-values and dilution curves for the CIC study are depicted in Fig. 1 panels a–b, respectively
We used four different methods to estimate and perform inference: (1) efficiency corrected (EC): Efficiency corrected linear mixed effects model (LMM) estimate ignoring the uncertainty of the efficiency estimates
Summary
Accurate adjustment for the amplification efficiency (AE) is an important part of real-time quantitative polymerase chain reaction (qPCR) experiments. As qPCR is often used for validation it should be a high priority to account for the uncertainty of the AE estimate and thereby properly bounding the type I error rate and achieve the desired significance level. Despite being an aging technique, real-time quantitative polymerase chain reaction (qPCR)—arguably one of the most significant biotech discoveries of all time—is still heavily used in molecular biology [1]. Important statistical details are often omitted resulting in a failure to obtain the desired type I error probability. Validation without such an ability cannot be considered very meaningful and conservative approaches should be taken
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