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http://hdl.handle.net/1942/42855
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DC Field | Value | Language |
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dc.contributor.author | Chen, Yao | - |
dc.contributor.author | De Spiegelaere, Ward | - |
dc.contributor.author | Trypsteen, Wim | - |
dc.contributor.author | Gleerup, David | - |
dc.contributor.author | Vandesompele, Jo | - |
dc.contributor.author | Lievens, Antoon | - |
dc.contributor.author | Vynck, Matthijs | - |
dc.contributor.author | THAS, Olivier | - |
dc.date.accessioned | 2024-05-06T08:49:33Z | - |
dc.date.available | 2024-05-06T08:49:33Z | - |
dc.date.issued | 2024 | - |
dc.date.submitted | 2024-05-06T07:50:57Z | - |
dc.identifier.citation | BRIEFINGS IN BIOINFORMATICS, 25 (3) (Art N° bbae120) | - |
dc.identifier.uri | http://hdl.handle.net/1942/42855 | - |
dc.description.abstract | Digital PCR (dPCR) is a highly accurate technique for the quantification of target nucleic acid(s). It has shown great potential in clinical applications, like tumor liquid biopsy and validation of biomarkers. Accurate classification of partitions based on end-point fluorescence intensities is crucial to avoid biased estimators of the concentration of the target molecules. We have evaluated many clustering methods, from general-purpose methods to specific methods for dPCR and flowcytometry, on both simulated and real-life data. Clustering method performance was evaluated by simulating various scenarios. Based on our extensive comparison of clustering methods, we describe the limits of these methods, and formulate guidelines for choosing an appropriate method. In addition, we have developed a novel method for simulating realistic dPCR data. The method is based on a mixture distribution of a Poisson point process and a skew-$t$ distribution, which enables the generation of irregularities of cluster shapes and randomness of partitions between clusters ('rain') as commonly observed in dPCR data. Users can fine-tune the model parameters and generate labeled datasets, using their own data as a template. Besides, the database of experimental dPCR data augmented with the labeled simulated data can serve as training and testing data for new clustering methods. The simulation method is available as an R Shiny app. | - |
dc.description.sponsorship | Y.C. and D.G. are funded by Ghent University’s Special Research Fund (BOF, grant 01IO0420 awarded to O.T. and W.D.S.). M.V. is funded by Stilla Technologies (grant awarded to O.T. and W.D.S.). | - |
dc.language.iso | en | - |
dc.publisher | OXFORD UNIV PRESS | - |
dc.rights | The Author(s) 2024. Published by Oxford University Press. This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (https://creativecommons.org/ licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. | - |
dc.subject.other | digital PCR; nucleic acid quantification; clustering; simulation; | - |
dc.subject.other | nucleic acid amplification; absolute quantification; molecular | - |
dc.subject.other | diagnostics; high-precision PCR | - |
dc.title | Benchmarking digital PCR partition classification methods with empirical and simulated duplex data | - |
dc.type | Journal Contribution | - |
dc.identifier.issue | 3 | - |
dc.identifier.volume | 25 | - |
local.format.pages | 13 | - |
local.bibliographicCitation.jcat | A1 | - |
dc.description.notes | Thas, O (corresponding author), Univ Ghent, Dept Appl Math Comp Sci & Stat, B-9000 Ghent, Belgium. | - |
dc.description.notes | olivier.thas@ugent.be | - |
local.publisher.place | GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND | - |
local.type.refereed | Refereed | - |
local.type.specified | Article | - |
local.bibliographicCitation.artnr | bbae120 | - |
dc.identifier.doi | 10.1093/bib/bbae120 | - |
dc.identifier.pmid | 38555473 | - |
dc.identifier.isi | 001193845100005 | - |
dc.contributor.orcid | Vynck, Matthijs/0000-0001-9875-385X; Chen, Yao/0000-0001-8172-3996; De | - |
dc.contributor.orcid | Spiegelaere, Ward/0000-0003-2097-8439 | - |
local.provider.type | wosris | - |
local.description.affiliation | [Thas, Olivier] Univ Ghent, Dept Appl Math Comp Sci & Stat, B-9000 Ghent, Belgium. | - |
local.description.affiliation | [Chen, Yao] Univ Ghent, Dept Appl Math Comp Sci & Stat, Ghent, Belgium. | - |
local.description.affiliation | [De Spiegelaere, Ward; Vandesompele, Jo] Univ Ghent, Ghent, Belgium. | - |
local.description.affiliation | [De Spiegelaere, Ward] Univ Ghent, dPCR Facil, Fac Vet Med, Ghent, Belgium. | - |
local.description.affiliation | [Vandesompele, Jo] Canc Res Inst, Ghent, Belgium. | - |
local.description.affiliation | [Vandesompele, Jo] Canc Res Inst, OncoRNALab, Ghent, Belgium. | - |
local.description.affiliation | [Thas, Olivier] Hasselt Univ, Biostat, Hasselt, Belgium. | - |
local.description.affiliation | [Thas, Olivier] Univ Ghent, Fac Sci, Ghent, Belgium. | - |
local.description.affiliation | [Thas, Olivier] Univ Wollongong, Wollongong, Australia. | - |
local.uhasselt.international | yes | - |
item.fullcitation | Chen, Yao; De Spiegelaere, Ward; Trypsteen, Wim; Gleerup, David; Vandesompele, Jo; Lievens, Antoon; Vynck, Matthijs & THAS, Olivier (2024) Benchmarking digital PCR partition classification methods with empirical and simulated duplex data. In: BRIEFINGS IN BIOINFORMATICS, 25 (3) (Art N° bbae120). | - |
item.fulltext | With Fulltext | - |
item.contributor | Chen, Yao | - |
item.contributor | De Spiegelaere, Ward | - |
item.contributor | Trypsteen, Wim | - |
item.contributor | Gleerup, David | - |
item.contributor | Vandesompele, Jo | - |
item.contributor | Lievens, Antoon | - |
item.contributor | Vynck, Matthijs | - |
item.contributor | THAS, Olivier | - |
item.accessRights | Open Access | - |
crisitem.journal.issn | 1467-5463 | - |
crisitem.journal.eissn | 1477-4054 | - |
Appears in Collections: | Research publications |
Files in This Item:
File | Description | Size | Format | |
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Benchmarking digital PCR partition classification methods with empirical and simulated duplex data.pdf | Published version | 1.27 MB | Adobe PDF | View/Open |
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