Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/31546
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dc.contributor.authorAssefa, Alemu Takele-
dc.contributor.authorVandesompele, Jo-
dc.contributor.authorTHAS, Olivier-
dc.date.accessioned2020-08-04T13:48:15Z-
dc.date.available2020-08-04T13:48:15Z-
dc.date.issued2020-
dc.date.submitted2020-08-04T13:02:30Z-
dc.identifier.citationBioinformatics, 36 (10) , p. 3276 -3278-
dc.identifier.urihttp://hdl.handle.net/1942/31546-
dc.description.abstractA Summary: SPsimSeq is a semi-parametric simulation method to generate bulk and single-cell RNA-sequencing data. It is designed to simulate gene expression data with maximal retention of the characteristics of real data. It is reasonably flexible to accommodate a wide range of experimental scenarios, including different sample sizes, biological signals (differential expression) and confounding batch effects.-
dc.description.sponsorshipThis work was supported by the UGent Special Research Fund Concerted Research Actions [GOA grant number BOF16-GOA-023].-
dc.language.isoen-
dc.publisherOXFORD UNIV PRESS-
dc.rightsThe Author(s) 2020. Published by Oxford University Press. This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://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. For commercial re-use, please contact journals.permissions@oup.com-
dc.titleSPsimSeq: semi-parametric simulation of bulk and single-cell RNA-sequencing data-
dc.typeJournal Contribution-
local.bibliographicCitation.authorsBirol, Inanc-
dc.identifier.epage3278-
dc.identifier.issue10-
dc.identifier.spage3276-
dc.identifier.volume36-
local.format.pages3-
local.bibliographicCitation.jcatA1-
dc.description.notesAssefa, AT (corresponding author), Univ Ghent, Data Anal & Math Modeling, Ghent, Belgium.-
dc.description.notesalemutakele.assefa@ugent.be-
dc.description.otherAssefa, AT (corresponding author), Univ Ghent, Data Anal & Math Modeling, Ghent, Belgium. alemutakele.assefa@ugent.be-
local.publisher.placeGREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND-
local.type.refereedRefereed-
local.type.specifiedArticle-
dc.identifier.doi10.1093/bioinformatics/btaa105-
dc.identifier.pmid32065619-
dc.identifier.isiWOS:000537447900051-
dc.contributor.orcidTakele, Alemu/0000-0002-7773-0621-
local.provider.typewosris-
local.uhasselt.uhpubyes-
local.description.affiliation[Assefa, Alemu Takele; Thas, Olivier] Univ Ghent, Data Anal & Math Modeling, Ghent, Belgium.-
local.description.affiliation[Vandesompele, Jo] Univ Ghent, Biomol Med, Ghent, Belgium.-
local.description.affiliation[Vandesompele, Jo; Thas, Olivier] Univ Ghent, Canc Res Inst Ghent, Ghent, Belgium.-
local.description.affiliation[Vandesompele, Jo] Univ Ghent, Ctr Med Genet, Ghent, Belgium.-
local.description.affiliation[Thas, Olivier] Univ Wollongong, NIASRA, Wollongong, NSW, Australia.-
local.description.affiliation[Thas, Olivier] Hasselt Univ, Data Sci Inst, I BioStat, Hasselt, Belgium.-
local.uhasselt.internationalyes-
item.validationecoom 2021-
item.accessRightsOpen Access-
item.fulltextWith Fulltext-
item.fullcitationAssefa, Alemu Takele; Vandesompele, Jo & THAS, Olivier (2020) SPsimSeq: semi-parametric simulation of bulk and single-cell RNA-sequencing data. In: Bioinformatics, 36 (10) , p. 3276 -3278.-
item.contributorAssefa, Alemu Takele-
item.contributorVandesompele, Jo-
item.contributorTHAS, Olivier-
crisitem.journal.issn1367-4803-
crisitem.journal.eissn1367-4811-
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