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Flexible analysis of digital PCR experiments using generalized linear mixed models

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

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Chicago
Vynck, Matthijs, Jo Vandesompele, Nele Nijs, Björn Menten, Ariane De Ganck, and Olivier Thas. 2016. “Flexible Analysis of Digital PCR Experiments Using Generalized Linear Mixed Models.” Biomolecular Detection and Quantification 9: 1–13.
APA
Vynck, M., Vandesompele, J., Nijs, N., Menten, B., De Ganck, A., & Thas, O. (2016). Flexible analysis of digital PCR experiments using generalized linear mixed models. BIOMOLECULAR DETECTION AND QUANTIFICATION, 9, 1–13.
Vancouver
1.
Vynck M, Vandesompele J, Nijs N, Menten B, De Ganck A, Thas O. Flexible analysis of digital PCR experiments using generalized linear mixed models. BIOMOLECULAR DETECTION AND QUANTIFICATION. 2016;9:1–13.
MLA
Vynck, Matthijs, Jo Vandesompele, Nele Nijs, et al. “Flexible Analysis of Digital PCR Experiments Using Generalized Linear Mixed Models.” BIOMOLECULAR DETECTION AND QUANTIFICATION 9 (2016): 1–13. Print.
@article{8109549,
  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.},
  author       = {Vynck, Matthijs and Vandesompele, Jo and Nijs, Nele and Menten, Bj{\"o}rn and De Ganck, Ariane and Thas, Olivier},
  issn         = {2214-7535},
  journal      = {BIOMOLECULAR DETECTION AND QUANTIFICATION},
  language     = {eng},
  pages        = {1--13},
  title        = {Flexible analysis of digital PCR experiments using generalized linear mixed models},
  url          = {http://dx.doi.org/10.1016/j.bdq.2016.06.001},
  volume       = {9},
  year         = {2016},
}

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