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Biomol Detect Quantif. 2015 Jul 30;5:10-4. doi: 10.1016/j.bdq.2015.07.001. eCollection 2015 Sep.

Removal of between-run variation in a multi-plate qPCR experiment.

Author information

1
Department of Anatomy, Embryology and Physiology, Academic Medical Centre, University of Amsterdam, Amsterdam, The Netherlands.
2
Biogazelle, Ghent, Belgium.
3
Center of Molecular Biology (ZMBH), University of Heidelberg, Heidelberg 69120, Germany.

Abstract

Quantitative PCR (qPCR) is the method of choice in gene expression analysis. However, the number of groups or treatments, target genes and technical replicates quickly exceeds the capacity of a single run on a qPCR machine and the measurements have to be spread over more than 1 plate. Such multi-plate measurements often show similar proportional differences between experimental conditions, but different absolute values, even though the measurements were technically carried out with identical procedures. Removal of this between-plate variation will enhance the power of the statistical analysis on the resulting data. Inclusion and application of calibrator samples, with replicate measurements distributed over the plates, assumes a multiplicative difference between plates. However, random and technical errors in these calibrators will propagate to all samples on the plate. To avoid this effect, the systematic bias between plates can be removed with a correction factor based on all overlapping technical and biological replicates between plates. This approach removes the requirement for all calibrator samples to be measured successfully on every plate. This paper extends an already published factor correction method to the use in multi-plate qPCR experiments. The between-run correction factor is derived from the target quantities which are calculated from the quantification threshold, PCR efficiency and observed C q value. To enable further statistical analysis in existing qPCR software packages, an efficiency-corrected C q value is reported, based on the corrected target quantity and a PCR efficiency per target. The latter is calculated as the mean of the PCR efficiencies taking the number of reactions per amplicon per plate into account. Export to the RDML format completes an RDML-supported analysis pipeline of qPCR data ranging from raw fluorescence data, amplification curve analysis and application of reference genes to statistical analysis.

KEYWORDS:

Between-plate correction; Between-run variation; Multi-plate experiment; RDML; Software; qPCR

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