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Nat Methods. 2017 May;14(5):483-486. doi: 10.1038/nmeth.4236. Epub 2017 Mar 27.

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

Author information

1
Wellcome Trust Sanger Institute, Hinxton, Cambridge, UK.
2
Cambridge Institute for Medical Research, Wellcome Trust/MRC Stem Cell Institute and Department of Haematology, University of Cambridge, Hills Road, Cambridge, UK.
3
Department of Mathematics and naXys, University of Namur, Namur, Belgium.
4
ICTEAM, Université Catholique de Louvain, Louvain-la-Neuve, Belgium.
5
Epigenetics Programme, The Babraham Institute, Babraham, Cambridge, UK.
6
EMBL-European Bioinformatics Institute, Hinxton, Cambridge, UK.
7
Centre for Trophoblast Research, University of Cambridge, Cambridge, UK.
8
Department of Mathematics, Imperial College London, London, UK.

Abstract

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.

PMID:
28346451
PMCID:
PMC5410170
DOI:
10.1038/nmeth.4236
[Indexed for MEDLINE]
Free PMC Article

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