Statistical uncertainty and its propagation in the analysis of quantitative polymerase chain reaction data: Comparison of methods

• Published in: Analytical biochemistry Vol. 464; pp. 94 - 102
• Main Authors: Tellinghuisen, Joel, Spiess, Andrej-Nikolai
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.11.2014
• Subjects: algorithms; Calibration; developmental stages; fluorescence; least squares; Least-Squares Analysis; mechanistic models; Polymerase Chain Reaction - methods; qPCR; quantitative polymerase chain reaction; Statistical error propagation; Uncertainty; Uncertainty analysis; Weighted least squares; qPCR; Calibration; Weighted least squares; Uncertainty analysis; Statistical error propagation
• ISSN: 0003-2697; 1096-0309; 1096-0309
• DOI: 10.1016/j.ab.2014.06.015

Most methods for analyzing real-time quantitative polymerase chain reaction (qPCR) data for single experiments estimate the hypothetical cycle 0 signal y0 by first estimating the quantification cycle (Cq) and amplification efficiency (E) from least-squares fits of fluorescence intensity data for cycles near the onset of the growth phase. The resulting y0 values are statistically equivalent to the corresponding Cq if and only if E is taken to be error free. But uncertainty in E usually dominates the total uncertainty in y0, making the latter much degraded in precision compared with Cq. Bias in E can be an even greater source of error in y0. So-called mechanistic models achieve higher precision in estimating y0 by tacitly assuming E=2 in the baseline region and so are subject to this bias error. When used in calibration, the mechanistic y0 is statistically comparable to Cq from the other methods. When a signal threshold yq is used to define Cq, best estimation precision is obtained by setting yq near the maximum signal in the range of fitted cycles, in conflict with common practice in the y0 estimation algorithms.