Robust regression methods for real-time polymerase chain reaction
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Publication date
2015-07-01
Authors
Trypsteen, Wim
De Neve, Jan
Bosman, Kobus
Nijhuis, Monique
Thas, Olivier
Vandekerckhove, Linos
De Spiegelaere, Ward
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Document Type
Article
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taverne
Abstract
Current real-time polymerase chain reaction (PCR) data analysis methods implement linear least squares regression methods for primer efficiency estimation based on standard curve dilution series. This method is sensitive to outliers that distort the outcome and are often ignored or removed by the end user. Here, robust regression methods are shown to provide a reliable alternative because they are less affected by outliers and often result in more precise primer efficiency estimators than the linear least squares method. (C) 2015 Elsevier Inc. All rights reserved.
Keywords
Robust regression, Real-time PCR, Outliers, qPCR, Standard curve, PCR efficiency estimation, PCR, Taverne
Citation
Trypsteen, W, De Neve, J, Bosman, K, Nijhuis, M, Thas, O, Vandekerckhove, L & De Spiegelaere, W 2015, 'Robust regression methods for real-time polymerase chain reaction', Analytical Biochemistry, vol. 480, pp. 34-36. https://doi.org/10.1016/j.ab.2015.04.001