BBKNN: fast batch alignment of single cell transcriptomes

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dc.contributor.authorPolanski, Krzysztofko
dc.contributor.authorYoung, Matthew D.ko
dc.contributor.authorMiao, Zhichaoko
dc.contributor.authorMeyer, Kerstin B.ko
dc.contributor.authorTeichmann, Sarah A.ko
dc.contributor.authorPark, Jong-Eunko
dc.date.accessioned2020-12-04T02:30:08Z-
dc.date.available2020-12-04T02:30:08Z-
dc.date.created2020-12-04-
dc.date.created2020-12-04-
dc.date.issued2020-02-
dc.identifier.citationBIOINFORMATICS, v.36, no.3, pp.964 - 965-
dc.identifier.issn1367-4803-
dc.identifier.urihttp://hdl.handle.net/10203/278043-
dc.description.abstractMotivation: Increasing numbers of large scale single cell RNA-Seq projects are leading to a data explosion, which can only be fully exploited through data integration. A number of methods have been developed to combine diverse datasets by removing technical batch effects, but most are computationally intensive. To overcome the challenge of enormous datasets, we have developed BBKNN, an extremely fast graph-based data integration algorithm. We illustrate the power of BBKNN on large scale mouse atlasing data, and favourably benchmark its run time against a number of competing methods.-
dc.languageEnglish-
dc.publisherOXFORD UNIV PRESS-
dc.titleBBKNN: fast batch alignment of single cell transcriptomes-
dc.typeArticle-
dc.identifier.wosid000515095200053-
dc.identifier.scopusid2-s2.0-85074670646-
dc.type.rimsART-
dc.citation.volume36-
dc.citation.issue3-
dc.citation.beginningpage964-
dc.citation.endingpage965-
dc.citation.publicationnameBIOINFORMATICS-
dc.identifier.doi10.1093/bioinformatics/btz625-
dc.contributor.localauthorPark, Jong-Eun-
dc.contributor.nonIdAuthorPolanski, Krzysztof-
dc.contributor.nonIdAuthorYoung, Matthew D.-
dc.contributor.nonIdAuthorMiao, Zhichao-
dc.contributor.nonIdAuthorMeyer, Kerstin B.-
dc.contributor.nonIdAuthorTeichmann, Sarah A.-
dc.description.isOpenAccessY-
dc.type.journalArticleArticle-
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