Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/40740
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dc.contributor.authorVynck, Matthijs-
dc.contributor.authorChen, Yao-
dc.contributor.authorGleerup, David-
dc.contributor.authorVandesompele, Jo-
dc.contributor.authorTrypsteen, Wim-
dc.contributor.authorLievens, Antoon-
dc.contributor.authorTHAS, Olivier-
dc.contributor.authorDe Spiegelaere, Ward-
dc.date.accessioned2023-08-22T13:39:06Z-
dc.date.available2023-08-22T13:39:06Z-
dc.date.issued2023-
dc.date.submitted2023-08-04T12:26:31Z-
dc.identifier.citationCLINICAL CHEMISTRY, 69 (9) , p. 976-990-
dc.identifier.issn0009-9147-
dc.identifier.issn1530-8561-
dc.identifier.urihttp://hdl.handle.net/1942/40740-
dc.description.abstractBackground Partition classification is a critical step in the digital PCR data analysis pipeline. A range of partition classification methods have been developed, many motivated by specific experimental setups. An overview of these partition classification methods is lacking and their comparative properties are often unclear, likely impacting the proper application of these methods. Content This review provides a summary of all available digital PCR partition classification approaches and the challenges they aim to overcome, serving as a guide for the digital PCR practitioner wishing to apply them. We additionally discuss strengths and weaknesses of these methods, which can further guide practitioners in vigilant application of these existing methods. This review provides method developers with ideas for improving methods or designing new ones. The latter is further stimulated by our identification and discussion of application gaps in the literature, for which there are currently no or few methods available. This review provides an overview of digital PCR partition classification methods, their properties, and potential applications. Ideas for further advances are presented and may bolster method development.-
dc.description.sponsorshipO. Thas and W. De Spiegelaere obtained grant 01IO0420 from Ghent University’s Special Research Fund (BOF) on which Y. Chen and D. Gleerup are enrolled. O. Thas and W. De Spiegelaere obtained research funding from Stilla Technologies on which M. Vynck is enrolled. W. De Spiegelaere, Flanders Innovation & entrepreneurship—VLAIO, free loan of dPCR equipment for research from Stilla Technologies, Optolane, Thermo Fisher-
dc.language.isoen-
dc.publisherOXFORD UNIV PRESS INC-
dc.rightsAmerican Association for Clinical Chemistry 2023. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com. Free access-
dc.subject.otherHumans-
dc.subject.otherPolymerase Chain Reaction-
dc.titleDigital PCR Partition Classification-
dc.typeJournal Contribution-
dc.identifier.epage990-
dc.identifier.issue9-
dc.identifier.spage976-
dc.identifier.volume69-
local.format.pages15-
local.bibliographicCitation.jcatA1-
dc.description.notesDe Spiegelaere, W (corresponding author), Salisburylaan 133,Entrance 78, B-9820 Merelbeke, Belgium.-
dc.description.notesward.despiegelaere@ugent.be-
local.publisher.placeJOURNALS DEPT, 2001 EVANS RD, CARY, NC 27513 USA-
local.type.refereedRefereed-
local.type.specifiedReview-
dc.identifier.doi10.1093/clinchem/hvad063-
dc.identifier.pmid37401391-
dc.identifier.isi001021889600001-
dc.contributor.orcidVandesompele, Jo/0000-0001-6274-0184; De Spiegelaere,-
dc.contributor.orcidWard/0000-0003-2097-8439-
dc.identifier.eissn1530-8561-
local.provider.typewosris-
local.description.affiliation[Vynck, Matthijs; Chen, Yao; Gleerup, David; Vandesompele, Jo; Trypsteen, Wim; Lievens, Antoon; Thas, Olivier; De Spiegelaere, Ward] Univ Ghent, Digital PCR Consortium, Ghent, Belgium.-
local.description.affiliation[Vynck, Matthijs; Chen, Yao; Gleerup, David; De Spiegelaere, Ward] Univ Ghent, Fac Vet Med, Dept Morphol Imaging Orthoped Rehabil & Nutr, Ghent, Belgium.-
local.description.affiliation[Chen, Yao; Thas, Olivier] Univ Ghent, Fac Sci, Dept Appl Math, Comp Sci & Stat, Ghent, Belgium.-
local.description.affiliation[Vandesompele, Jo; Trypsteen, Wim] Canc Res Inst Ghent, OncoRNALab, Ghent, Belgium.-
local.description.affiliation[Vandesompele, Jo; Trypsteen, Wim; De Spiegelaere, Ward] Univ Ghent, Dept Biomol Med, Ghent, Belgium.-
local.description.affiliation[Vandesompele, Jo; Trypsteen, Wim] Univ Ghent, Ctr Med Genet, Ghent, Belgium.-
local.description.affiliation[Vandesompele, Jo] CellCarta, Zwijnaarde, Belgium.-
local.description.affiliation[Lievens, Antoon] BASF Innovat Ctr Ghent, Zwijnaarde, Belgium.-
local.description.affiliation[Thas, Olivier] Hasselt Univ, Data Sci Inst, I BioStat, Hasselt, Belgium.-
local.description.affiliation[Thas, Olivier] Univ Wollongong, Natl Inst Appl Stat Res Australia, Sch Math & Appl Stat, Wollongong, Australia.-
local.description.affiliation[De Spiegelaere, Ward] Salisburylaan 133,Entrance 78, B-9820 Merelbeke, Belgium.-
local.uhasselt.internationalyes-
item.fulltextWith Fulltext-
item.fullcitationVynck, Matthijs; Chen, Yao; Gleerup, David; Vandesompele, Jo; Trypsteen, Wim; Lievens, Antoon; THAS, Olivier & De Spiegelaere, Ward (2023) Digital PCR Partition Classification. In: CLINICAL CHEMISTRY, 69 (9) , p. 976-990.-
item.accessRightsOpen Access-
item.contributorVynck, Matthijs-
item.contributorChen, Yao-
item.contributorGleerup, David-
item.contributorVandesompele, Jo-
item.contributorTrypsteen, Wim-
item.contributorLievens, Antoon-
item.contributorTHAS, Olivier-
item.contributorDe Spiegelaere, Ward-
crisitem.journal.issn0009-9147-
crisitem.journal.eissn1530-8561-
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