Robust regression methods for real-time polymerase chain reaction

Authors

Wim Trypsteen, Ghent University
Jan De Neve, Ghent UniversityFollow
Kobus Bosman, University Medical Center Utrecht
Monique Nijhuis, University Medical Center Utrecht
Olivier Thas, University of WollongongFollow
Linos Vandekerckhove, Ghent University
Ward De Spiegelaere, Ghent University

RIS ID

100801

Publication Details

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, 480 34-36.

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.