Abstract
BackgroundqPCR has established itself as the technique of choice for the quantification of gene expression. Procedures for conducting qPCR have received significant attention; however, more rigorous approaches to the statistical analysis of qPCR data are needed.ResultsHere we develop a mathematical model, termed the Common Base Method, for analysis of qPCR data based on threshold cycle values (Cq) and efficiencies of reactions (E). The Common Base Method keeps all calculations in the logscale as long as possible by working with log10(E) ∙ Cq, which we call the efficiency-weighted Cq value; subsequent statistical analyses are then applied in the logscale. We show how efficiency-weighted Cq values may be analyzed using a simple paired or unpaired experimental design and develop blocking methods to help reduce unexplained variation.ConclusionsThe Common Base Method has several advantages. It allows for the incorporation of well-specific efficiencies and multiple reference genes. The method does not necessitate the pairing of samples that must be performed using traditional analysis methods in order to calculate relative expression ratios. Our method is also simple enough to be implemented in any spreadsheet or statistical software without additional scripts or proprietary components.
Highlights
QPCR has established itself as the technique of choice for the quantification of gene expression
We propose the use of individual E and Cq values to develop a new Common Base Method and notation that combine the simplicity of the 2−ΔΔCq method with the greater presumed accuracy of methods including those of Pfaffl [3], Schefé et al [15], and Yuan et al [21] that use actual E values instead of the theoretical maximum of 2
One of the challenges of quantitative polymerase chain reaction (qPCR), and other platebased experiments, is that data are derived from qPCR plates that may be run at different times using reagents of differing ages or even using different machines
Summary
QPCR has established itself as the technique of choice for the quantification of gene expression. The use of quantitative polymerase chain reaction (qPCR) for diverse applications has increased dramatically [1,2,3,4] since its development in the late 1980s [5] and has been established as the technique of choice for the quantification of gene expression [2, 6, 7]. The protocols and procedures for preparing and processing samples as well as conducting the actual qPCR experiments [4, 7, 9], along with specific concerns and considerations [8, 10,11,12], have been covered in detail by others. Current methods used to analyze qPCR data utilize at a minimum the Cq values. One common method to analyze relative gene expression data is the Livak-Schmittgen [14] method ( 2−ΔΔCq ), which compares two values in the exponent that represent the normalized expression values for a gene of interest in sample type A relative to sample type B
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