Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/33764
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dc.contributor.authorSara Aibar-
dc.contributor.authorCarmen Bravo González-Blas-
dc.contributor.authorThomas Moerman-
dc.contributor.authorVân Anh Huynh-Thu-
dc.contributor.authorHana Imrichova-
dc.contributor.authorGert Hulselmans-
dc.contributor.authorFlorian Rambow-
dc.contributor.authorJean-Christophe Marine-
dc.contributor.authorPierre Geurts-
dc.contributor.authorAERTS, Jan-
dc.contributor.authorJoost van den Oord-
dc.contributor.authorZeynep Kalender Atak-
dc.contributor.authorJasper Wouters-
dc.contributor.authorStein Aerts-
dc.date.accessioned2021-03-30T07:10:05Z-
dc.date.available2021-03-30T07:10:05Z-
dc.date.issued2017-
dc.date.submitted2021-03-22T13:03:34Z-
dc.identifier.citationNature Methods, 14 (11) , p. 1083 -1086-
dc.identifier.isbn15487105 15487091-
dc.identifier.urihttp://hdl.handle.net/1942/33764-
dc.description.abstractWe present SCENIC, a computational method for simultaneous gene regulatory network reconstruction and cell-state identification from single-cell RNA-seq data (http://scenic.aertslab.org). On a compendium of single-cell data from tumors and brain, we demonstrate that cis-regulatory analysis can be exploited to guide the identification of transcription factors and cell states. SCENIC provides critical biological insights into the mechanisms driving cellular heterogeneity.-
dc.language.isoen-
dc.publisherSpringer Science and Business Media {LLC}-
dc.subject.otherAlgorithms-
dc.subject.otherAnimals-
dc.subject.otherBrain-
dc.subject.otherCluster Analysis-
dc.subject.otherGene Expression Profiling-
dc.subject.otherHumans-
dc.subject.otherMice-
dc.subject.otherGene Regulatory Networks-
dc.subject.otherSingle-Cell Analysis-
dc.titleSCENIC: single-cell regulatory network inference and clustering-
dc.typeJournal Contribution-
dc.identifier.epage1086-
dc.identifier.issue11-
dc.identifier.spage1083-
dc.identifier.volume14-
local.bibliographicCitation.jcatA1-
local.type.refereedRefereed-
local.type.specifiedArticle-
dc.identifier.doi10.1038/nmeth.4463-
dc.identifier.pmid28991892-
dc.identifier.scopus2-s2.0-85032583384-
dc.identifier.urlhttps://doi.org/10.1038/nmeth.4463-
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local.provider.typeOrcid-
local.uhasselt.uhpubno-
local.uhasselt.internationalno-
item.accessRightsClosed Access-
item.fulltextNo Fulltext-
item.contributorSara Aibar-
item.contributorCarmen Bravo González-Blas-
item.contributorThomas Moerman-
item.contributorVân Anh Huynh-Thu-
item.contributorHana Imrichova-
item.contributorGert Hulselmans-
item.contributorFlorian Rambow-
item.contributorJean-Christophe Marine-
item.contributorPierre Geurts-
item.contributorAERTS, Jan-
item.contributorJoost van den Oord-
item.contributorZeynep Kalender Atak-
item.contributorJasper Wouters-
item.contributorStein Aerts-
item.fullcitationSara Aibar; Carmen Bravo González-Blas; Thomas Moerman; Vân Anh Huynh-Thu; Hana Imrichova; Gert Hulselmans; Florian Rambow; Jean-Christophe Marine; Pierre Geurts; AERTS, Jan; Joost van den Oord; Zeynep Kalender Atak; Jasper Wouters & Stein Aerts (2017) SCENIC: single-cell regulatory network inference and clustering. In: Nature Methods, 14 (11) , p. 1083 -1086.-
crisitem.journal.issn1548-7091-
crisitem.journal.eissn1548-7105-
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