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Hierarchical cell-type identifier accurately distinguishes immune-cell subtypes enabling precise profiling of tissue microenvironment with single-cell RNA-sequencing

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dc.contributor.authorLee, Joongho-
dc.contributor.authorKim, Minsoo-
dc.contributor.authorKang, Keunsoo-
dc.contributor.authorYang, Chul-Su-
dc.contributor.authorYoon, Seokhyun-
dc.date.accessioned2023-05-03T09:32:01Z-
dc.date.available2023-05-03T09:32:01Z-
dc.date.issued2023-03-
dc.identifier.issn1467-5463-
dc.identifier.issn1477-4054-
dc.identifier.urihttps://scholarworks.bwise.kr/erica/handle/2021.sw.erica/112516-
dc.description.abstractSingle-cell RNA-seq enabled in-depth study on tissue micro-environment and immune-profiling, where a crucial step is to annotate cell identity. Immune cells play key roles in many diseases, whereas their activities are hard to track due to their diverse and highly variable nature. Existing cell-type identifiers had limited performance for this purpose. We present HiCAT, a hierarchical, marker-based cell-type identifier utilising gene set analysis for statistical scoring for given markers. It features successive identification of major-type, minor-type and subsets utilising subset markers structured in a three-level taxonomy tree. Comparison with manual annotation and pairwise match test showed HiCAT outperforms others in major- and minor-type identification. For subsets, we qualitatively evaluated the marker expression profile demonstrating that HiCAT provide the clearest immune-cell landscape. HiCAT was also used for immune-cell profiling in ulcerative colitis and discovered distinct features of the disease in macrophage and T-cell subsets that could not be identified previously. © The Author(s) 2023. Published by Oxford University Press.-
dc.format.extent15-
dc.language영어-
dc.language.isoENG-
dc.publisherNLM (Medline)-
dc.titleHierarchical cell-type identifier accurately distinguishes immune-cell subtypes enabling precise profiling of tissue microenvironment with single-cell RNA-sequencing-
dc.typeArticle-
dc.publisher.location영국-
dc.identifier.doi10.1093/bib/bbad006-
dc.identifier.scopusid2-s2.0-85150665343-
dc.identifier.wosid001042120200016-
dc.identifier.bibliographicCitationBriefings in bioinformatics, v.24, no.2, pp 1 - 15-
dc.citation.titleBriefings in bioinformatics-
dc.citation.volume24-
dc.citation.number2-
dc.citation.startPage1-
dc.citation.endPage15-
dc.type.docTypeArticle-
dc.description.isOpenAccessY-
dc.description.journalRegisteredClassscie-
dc.description.journalRegisteredClassscopus-
dc.relation.journalResearchAreaBiochemistry & Molecular Biology-
dc.relation.journalResearchAreaMathematical & Computational Biology-
dc.relation.journalWebOfScienceCategoryBiochemical Research Methods-
dc.relation.journalWebOfScienceCategoryMathematical & Computational Biology-
dc.subject.keywordAuthorcell-type identifier-
dc.subject.keywordAuthorcell-type markers-
dc.subject.keywordAuthorhierarchical identification-
dc.subject.keywordAuthorsingle-cell RNA-seq-
dc.identifier.urlhttps://academic.oup.com/bib/article/24/2/bbad006/6995373?login=true-
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ERICA 과학기술융합대학 (ERICA 의약생명과학과)
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