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Open Access Highly Accessed Methodology article

Statistical significance of quantitative PCR

Yann Karlen1, Alan McNair1, Sébastien Perseguers2, Christian Mazza3 and Nicolas Mermod1*

Author Affiliations

1 Institute of Biotechnology, University of Lausanne, 1015 Lausanne, Switzerland

2 Max-Planck-Institute für Quantenoptik, 85748 Garching, Germany

3 Department of Mathematics, University of Fribourg, CH-1700 Fribourg, Switzerland

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BMC Bioinformatics 2007, 8:131  doi:10.1186/1471-2105-8-131

Published: 20 April 2007

Additional files

Additional file 1:

Additional tables. Additional Table 1: Primer sequences and qPCR dataset description. Additional Table 2: Mean efficiency of each primer set. Additional Table 3: Induction ratio of extracellular matrix gene by TGF-β as assessed from 10 replicate assays. Additional Table 4: Induction ratio of extracellular matrix gene by TGF-β as assessed from 3 replicate assays

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Additional file 2:

Complete set of data and macro. Excel file containing all raw qPCR data and the macro used into the present article.

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Open Data

Additional file 3:

Additional Figures. Additional Figure 1: Reproducibility of Ct measurements. Additional Figure 2: Precision and Robustness of the different models.

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Additional file 4:

Mathematical Justification of LinReg. Justification of the LinReg method to estimate PCR efficiency, when PCR is considered as a branching process.

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Additional file 5:

ΔCt systematic bias. When not fulfilled, the ΔCt assumption of equal efficiency induces a bias in induction estimates. Equations are developed to estimate the bias as a function of the real efficiency.

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Additional file 6:

Equation development. Detailed development of all equations 1–14 of the Methods section.

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Additional file 7:

Statistical significance and required sample size. Presentation of all of the equations leading to the development of eq.15 of the Methods section.

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