- Author
- Yao Chen (UGent) , Ward De Spiegelaere (UGent) , Matthijs Vynck (UGent) , Wim Trypsteen (UGent) , David Gleerup (UGent) , Jo Vandesompele (UGent) and Olivier Thas (UGent)
- Organization
- Project
- Abstract
- Digital PCR (dPCR) is an accurate technique for quantifying nucleic acids, but variance estimation remains a challenge due to violations of the assumptions underlying many existing methods. To address this, we propose two generic approaches, NonPVar and BinomVar, for calculating variance in dPCR data. These methods are evaluated using simulated and empirical data, incorporating common sources of variability. Unlike classical methods, our approaches are flexible and applicable to complex functions of partition counts like copy number variation (CNV), fractional abundance, and DNA integrity. An R Shiny app is provided to facilitate method selection and implementation. Our findings demonstrate that these methods improve accuracy and adaptability, offering robust tools for uncertainty estimation in dPCR experiments.
- Keywords
- POLYMERASE-CHAIN-REACTION
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Citation
Please use this url to cite or link to this publication: http://hdl.handle.net/1854/LU-01JP4J006Q7ED3PPWQH79CN9YF
- MLA
- Chen, Yao, et al. “Flexible Methods for Uncertainty Estimation of Digital PCR Data.” ISCIENCE, vol. 28, no. 3, 2025, doi:10.1016/j.isci.2025.111772.
- APA
- Chen, Y., De Spiegelaere, W., Vynck, M., Trypsteen, W., Gleerup, D., Vandesompele, J., & Thas, O. (2025). Flexible methods for uncertainty estimation of digital PCR data. ISCIENCE, 28(3). https://doi.org/10.1016/j.isci.2025.111772
- Chicago author-date
- Chen, Yao, Ward De Spiegelaere, Matthijs Vynck, Wim Trypsteen, David Gleerup, Jo Vandesompele, and Olivier Thas. 2025. “Flexible Methods for Uncertainty Estimation of Digital PCR Data.” ISCIENCE 28 (3). https://doi.org/10.1016/j.isci.2025.111772.
- Chicago author-date (all authors)
- Chen, Yao, Ward De Spiegelaere, Matthijs Vynck, Wim Trypsteen, David Gleerup, Jo Vandesompele, and Olivier Thas. 2025. “Flexible Methods for Uncertainty Estimation of Digital PCR Data.” ISCIENCE 28 (3). doi:10.1016/j.isci.2025.111772.
- Vancouver
- 1.Chen Y, De Spiegelaere W, Vynck M, Trypsteen W, Gleerup D, Vandesompele J, et al. Flexible methods for uncertainty estimation of digital PCR data. ISCIENCE. 2025;28(3).
- IEEE
- [1]Y. Chen et al., “Flexible methods for uncertainty estimation of digital PCR data,” ISCIENCE, vol. 28, no. 3, 2025.
@article{01JP4J006Q7ED3PPWQH79CN9YF,
abstract = {{Digital PCR (dPCR) is an accurate technique for quantifying nucleic acids, but variance estimation remains a challenge due to violations of the assumptions underlying many existing methods. To address this, we propose two generic approaches, NonPVar and BinomVar, for calculating variance in dPCR data. These methods are evaluated using simulated and empirical data, incorporating common sources of variability. Unlike classical methods, our approaches are flexible and applicable to complex functions of partition counts like copy number variation (CNV), fractional abundance, and DNA integrity. An R Shiny app is provided to facilitate method selection and implementation. Our findings demonstrate that these methods improve accuracy and adaptability, offering robust tools for uncertainty estimation in dPCR experiments.}},
articleno = {{111772}},
author = {{Chen, Yao and De Spiegelaere, Ward and Vynck, Matthijs and Trypsteen, Wim and Gleerup, David and Vandesompele, Jo and Thas, Olivier}},
issn = {{2589-0042}},
journal = {{ISCIENCE}},
keywords = {{POLYMERASE-CHAIN-REACTION}},
language = {{eng}},
number = {{3}},
pages = {{17}},
title = {{Flexible methods for uncertainty estimation of digital PCR data}},
url = {{http://doi.org/10.1016/j.isci.2025.111772}},
volume = {{28}},
year = {{2025}},
}
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