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Biomol Detect Quantif. 2016 Jun 24;9:1-13. doi: 10.1016/j.bdq.2016.06.001. eCollection 2016 Sep.

Flexible analysis of digital PCR experiments using generalized linear mixed models.

Author information

1
Department of Mathematical Modelling, Statistics and Bioinformatics, Ghent University, Coupure Links 653, 9000 Ghent, Belgium.
2
Center for Medical Genetics, Ghent University, De Pintelaan 185, 9000 Ghent, Belgium; Bioinformatics Institute Ghent N2N, Ghent University, De Pintelaan 185, 9000 Ghent, Belgium; Biogazelle, Technologiepark 3, 9052 Zwijnaarde, Belgium.
3
Biogazelle, Technologiepark 3, 9052 Zwijnaarde, Belgium.
4
Center for Medical Genetics, Ghent University, De Pintelaan 185, 9000 Ghent, Belgium; Bioinformatics Institute Ghent N2N, Ghent University, De Pintelaan 185, 9000 Ghent, Belgium.
5
Department of Mathematical Modelling, Statistics and Bioinformatics, Ghent University, Coupure Links 653, 9000 Ghent, Belgium; National Institute for Applied Statistics Research Australia (NIASRA), University of Wollongong, NSW 2522, Australia.

Abstract

The use of digital PCR for quantification of nucleic acids is rapidly growing. A major drawback remains the lack of flexible data analysis tools. Published analysis approaches are either tailored to specific problem settings or fail to take into account sources of variability. We propose the generalized linear mixed models framework as a flexible tool for analyzing a wide range of experiments. We also introduce a method for estimating reference gene stability to improve accuracy and precision of copy number and relative expression estimates. We demonstrate the usefulness of the methodology on a complex experimental setup.

KEYWORDS:

Data analysis; Digital PCR; Mixed models; Quantification; Replicates; Statistics

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