Methodology article
Statistical significance of quantitative PCR
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* Corresponding author: Nicolas Mermod nicolas.mermod@unil.ch
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
BMC Bioinformatics 2007, 8:131 doi:10.1186/1471-2105-8-131
Published: 20 April 2007Additional 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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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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