Unaccounted uncertainty from qPCR efficiency estimates entails uncontrolled false positive rates

BMC Bioinformatics

Methodology Article

Julie S. Bødker¹  Anders Petersen¹  Malene K. Kjeldsen¹  Steffen Falgreen¹  Karen Dybkær²  Hans E. Johnsen²  Martin Bøgsted²  Anders E. Bilgrau³ 

[1] Department of Haematology, Aalborg University Hospital, Sdr. Skovvej 15, 9000, Aalborg, Denmark;Department of Haematology, Aalborg University Hospital, Sdr. Skovvej 15, 9000, Aalborg, Denmark;Department of Clinical Medicine, Aalborg University Hospital, Sdr. Skovvej 15, 9000, Aalborg, Denmark;Department of Haematology, Aalborg University Hospital, Sdr. Skovvej 15, 9000, Aalborg, Denmark;Department of Mathematical Sciences, Aalborg University, Fredrik Bajers Vej 7G, 9220, Aalborg Ø, Denmark;

关键词: qPCR;    Amplification efficiency;    Delta-delta Cq;    Δ;    Error propagation;    Efficiency adjusted;   

DOI  :  10.1186/s12859-016-0997-6

 received in 2015-09-10, accepted in 2016-03-23,  发布年份 2016

来源: Springer

【 摘 要 】

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.

【 授权许可】

CC BY   
© Bilgrau et al. 2016

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