Robust regression methods for real-time polymerase chain reaction

被引：7

作者：

Trypsteen, Wim ^{[1
,2
]}

De Neve, Jan ^{[3
]}

Bosman, Kobus ^{[4
]}

Nijhuis, Monique ^{[4
]}

Thas, Olivier ^{[3
,5
]}

Vandekerckhove, Linos ^{[1
,2
]}

De Spiegelaere, Ward ^{[1
,2
]}

机构：

[1] Univ Ghent, Dept Internal Med, HIV Translat Res Unit, B-9000 Ghent, Belgium

[2] Ghent Univ Hosp, B-9000 Ghent, Belgium

[3] Univ Ghent, Dept Math Modelling Stat & Bioinformat, B-9000 Ghent, Belgium

[4] Univ Med Ctr Utrecht, Dept Med Microbiol & Virol, NL-3584 CX Utrecht, Netherlands

[5] Univ Wollongong, Sch Math & Appl Stat, NIASRA, Wollongong, NSW 2522, Australia

来源：

ANALYTICAL BIOCHEMISTRY | 2015年 / 480卷

关键词：

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

D O I：

10.1016/j.ab.2015.04.001

中图分类号：

Q5 [生物化学];

学科分类号：

071010 ; 081704 ;

摘要：

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.

引用

页码：34 / 36

页数：3

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