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Model-based classification for digital PCR : your Umbrella for rain

Bart Jacobs, Els Goetghebeur UGent, Jo Vandesompele UGent, Ariane De Ganck UGent, Nele Nijs, Anneleen Beckers, Nina Papazova, Nancy H Roosens and Lieven Clement UGent (2017) ANALYTICAL CHEMISTRY. 89(8). p.4461-4467
abstract
Standard data analysis pipelines for digital PCR estimate the concentration of a target nucleic acid by digitizing the end-point fluorescence of the parallel micro-PCR reactions, using an automated hard threshold. While it is known that misclassification has a major impact on the concentration estimate and substantially reduces accuracy, the uncertainty of this classification is typically ignored. We introduce a model-based clustering method to estimate the probability that the target is present (absent) in a partition conditional on its observed fluorescence and the distributional shape in no-template control samples. This methodology acknowledges the inherent uncertainty of the classification and provides a natural measure of precision, both at individual partition level and at the level of the global concentration. We illustrate our method on genetically modified organism, inhibition, dynamic range, and mutation detection experiments. We show that our method provides concentration estimates of similar accuracy or better than the current standard, along with a more realistic measure of precision. The individual partition probabilities and diagnostic density plots further allow for some quality control. An R implementation of our method, called Umbrella, is available, providing a more objective and automated data analysis procedure for absolute dPCR quantification.
Please use this url to cite or link to this publication:
author
organization
year
type
journalArticle (original)
publication status
published
subject
keyword
POLYMERASE-CHAIN-REACTION, INTENSITY DISTRIBUTION ANALYSIS, GMO QUANTIFICATION, DENSITY-ESTIMATION, DROPLET
journal title
ANALYTICAL CHEMISTRY
Anal. Chem.
volume
89
issue
8
pages
4461 - 4467
Web of Science type
Article
Web of Science id
000399858800024
ISSN
0003-2700
1520-6882
DOI
10.1021/acs.analchem.6b04208
project
Bioinformatics: from nucleotids to networks (N2N)
language
English
UGent publication?
yes
classification
A1
copyright statement
I have transferred the copyright for this publication to the publisher
id
8517391
handle
http://hdl.handle.net/1854/LU-8517391
date created
2017-04-10 12:02:02
date last changed
2017-08-10 11:16:56
@article{8517391,
  abstract     = {Standard data analysis pipelines for digital PCR estimate the concentration of a target nucleic acid by digitizing the end-point fluorescence of the parallel micro-PCR reactions, using an automated hard threshold. While it is known that misclassification has a major impact on the concentration estimate and substantially reduces accuracy, the uncertainty of this classification is typically ignored. We introduce a model-based clustering method to estimate the probability that the target is present (absent) in a partition conditional on its observed fluorescence and the distributional shape in no-template control samples. This methodology acknowledges the inherent uncertainty of the classification and provides a natural measure of precision, both at individual partition level and at the level of the global concentration. We illustrate our method on genetically modified organism, inhibition, dynamic range, and mutation detection experiments. We show that our method provides concentration estimates of similar accuracy or better than the current standard, along with a more realistic measure of precision. The individual partition probabilities and diagnostic density plots further allow for some quality control. An R implementation of our method, called Umbrella, is available, providing a more objective and automated data analysis procedure for absolute dPCR quantification.},
  author       = {Jacobs, Bart and Goetghebeur, Els and Vandesompele, Jo and De Ganck, Ariane and Nijs, Nele and Beckers, Anneleen and Papazova, Nina and Roosens, Nancy H and Clement, Lieven},
  issn         = {0003-2700},
  journal      = {ANALYTICAL CHEMISTRY},
  keyword      = {POLYMERASE-CHAIN-REACTION,INTENSITY DISTRIBUTION ANALYSIS,GMO QUANTIFICATION,DENSITY-ESTIMATION,DROPLET},
  language     = {eng},
  number       = {8},
  pages        = {4461--4467},
  title        = {Model-based classification for digital PCR : your Umbrella for rain},
  url          = {http://dx.doi.org/10.1021/acs.analchem.6b04208},
  volume       = {89},
  year         = {2017},
}

Chicago
Jacobs, Bart, Els Goetghebeur, Jo Vandesompele, Ariane De Ganck, Nele Nijs, Anneleen Beckers, Nina Papazova, Nancy H Roosens, and Lieven Clement. 2017. “Model-based Classification for Digital PCR : Your Umbrella for Rain.” Analytical Chemistry 89 (8): 4461–4467.
APA
Jacobs, Bart, Goetghebeur, E., Vandesompele, J., De Ganck, A., Nijs, N., Beckers, A., Papazova, N., et al. (2017). Model-based classification for digital PCR : your Umbrella for rain. ANALYTICAL CHEMISTRY, 89(8), 4461–4467.
Vancouver
1.
Jacobs B, Goetghebeur E, Vandesompele J, De Ganck A, Nijs N, Beckers A, et al. Model-based classification for digital PCR : your Umbrella for rain. ANALYTICAL CHEMISTRY. 2017;89(8):4461–7.
MLA
Jacobs, Bart, Els Goetghebeur, Jo Vandesompele, et al. “Model-based Classification for Digital PCR : Your Umbrella for Rain.” ANALYTICAL CHEMISTRY 89.8 (2017): 4461–4467. Print.