Robust regression methods for real-time polymerase chain reaction

Wim Trypsteen; Jan De Neve; Kobus Bosman; Monique Nijhuis; Olivier Thas; Linos Vandekerckhove; Ward De Spiegelaere

doi:10.1016/j.ab.2015.04.001

Robust regression methods for real-time polymerase chain reaction

Wim Trypsteen, Jan De Neve, Kobus Bosman, Monique Nijhuis, Olivier Thas, Linos Vandekerckhove^*, Ward De Spiegelaere

^*Corresponding author for this work

Department of Medical Microbiology

Research output: Contribution to journal › Article › Academic › peer-review

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.

Original language	English
Pages (from-to)	34-36
Number of pages	3
Journal	Analytical Biochemistry
Volume	480
DOIs	https://doi.org/10.1016/j.ab.2015.04.001
Publication status	Published - 1 Jul 2015

Keywords

Robust regression
Real-time PCR
Outliers
qPCR
Standard curve
PCR efficiency estimation
PCR

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10.1016/j.ab.2015.04.001

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title = "Robust regression methods for real-time polymerase chain reaction",

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",

author = "Wim Trypsteen and {De Neve}, Jan and Kobus Bosman and Monique Nijhuis and Olivier Thas and Linos Vandekerckhove and {De Spiegelaere}, Ward",

year = "2015",

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doi = "10.1016/j.ab.2015.04.001",

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AU - Trypsteen, Wim

AU - De Neve, Jan

AU - Bosman, Kobus

AU - Nijhuis, Monique

AU - Thas, Olivier

AU - Vandekerckhove, Linos

AU - De Spiegelaere, Ward

PY - 2015/7/1

Y1 - 2015/7/1

N2 - 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.

AB - 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.

KW - Robust regression

KW - Real-time PCR

KW - Outliers

KW - qPCR

KW - Standard curve

KW - PCR efficiency estimation

KW - PCR

U2 - 10.1016/j.ab.2015.04.001

DO - 10.1016/j.ab.2015.04.001

M3 - Article

SN - 0003-2697

VL - 480

SP - 34

EP - 36

JO - Analytical Biochemistry

JF - Analytical Biochemistry

ER -

Robust regression methods for real-time polymerase chain reaction

Abstract

Keywords

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