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Statistical methods for efficiency adjusted real‐time PCR quantification
Author(s) -
Yuan Joshua S.,
Wang Donglin,
Stewart C. Neal
Publication year - 2008
Publication title -
biotechnology journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.144
H-Index - 84
eISSN - 1860-7314
pISSN - 1860-6768
DOI - 10.1002/biot.200700169
Subject(s) - computer science , data mining , equivalence (formal languages) , statistical hypothesis testing , set (abstract data type) , statistical model , data set , statistical analysis , statistics , mathematics , artificial intelligence , discrete mathematics , programming language
Abstract The statistical treatment for hypothesis testing using real‐time PCR data is a challenge for quantification of gene expression. One has to consider two key factors in precise statistical analysis of real‐time PCR data: a well‐defined statistical model and the integration of amplification efficiency (AE) into the model. Previous publications in real‐time PCR data analysis often fall short in integrating the AE into the model. Novel, user‐friendly, and universal AE‐integrated statistical methods were developed for real‐time PCR data analysis with four goals. First, we addressed the definition of AE, introduced the concept of efficiency‐adjusted ΔΔCt, and developed a general mathematical method for its calculation. Second, we developed several linear combination approaches for the estimation of efficiency adjusted ΔΔCt and statistical significance for hypothesis testing based on different mathematical formulae and experimental designs. Statistical methods were also adopted to estimate the AE and its equivalence among the samples. A weighted ΔΔCt method was introduced to analyze the data with multiple internal controls. Third, we implemented the linear models with SAS programs and analyzed a set of data for each model. In order to allow other researchers to use and compare different approaches, SAS programs are included in the Supporting Information. Fourth, the results from analysis of different statistical models were compared and discussed. Our results underline the differences between the efficiency adjusted ΔΔCt methods and previously published methods, thereby better identifying and controlling the source of errors introduced by real‐time PCR data analysis.