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SC3: consensus clustering of single-cell RNA-seq data

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Kiselev, Vladimir Yu Kirschner, Kristina Schaub, Michael T [UCL] Andrews, Tallulah Yiu, Andrew Hemberg, Martin

Single-cell RNA-seq enables the quantitative characterization of cell types based on global transcriptome profiles. We present single-cell consensus clustering (SC3), a user-friendly tool for unsupervised clustering, which achieves high accuracy and robustness by combining multiple clustering solutions through a consensus approach (http://bioconductor.org/packages/SC3). We demonstrate that SC3 is capable of identifying subclones from the transcriptomes of neoplastic cells collected from patients.

Document type Article de périodique (Journal article) – Article de recherche
Access type Accès interdit
Publication date 2017
Language Anglais
Journal information "Nature Methods" - Vol. 14, p. 483-486 (2017)
Peer reviewed yes
Publisher Springer Nature
issn 1548-7091
e-issn 1548-7105
Publication status Publié
Affiliation UCL - SST/ICTM/INMA - Pôle en ingénierie mathématique
Keywords Biotechnology ; Cell Biology ; Biochemistry ; Molecular Biology
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Bibliographic reference Kiselev, Vladimir Yu ; Kirschner, Kristina ; Schaub, Michael T ; Andrews, Tallulah ; Yiu, Andrew ; et. al. SC3: consensus clustering of single-cell RNA-seq data. In: Nature Methods, Vol. 14, p. 483-486 (2017)
Permanent URL http://hdl.handle.net/2078.1/194706