Journal
NATURE METHODS
Volume 14, Issue 5, Pages 483-+Publisher
NATURE PORTFOLIO
DOI: 10.1038/NMETH.4236
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Funding
- Wellcome Trust [104710/Z/14/Z, 095645/Z/11/Z]
- FRS-FNRS
- Belgian Network DYSCO (Dynamical Systems, Control and Optimisation) - Interuniversity Attraction Poles Programme
- Belgian State Science Policy Office
- ARC (Action de Recherche Concerte) on Mining and Optimization of Big Data Models - Wallonia-Brussels Federation
- EPSRC [EP/N014529/1]
- Sanger Institute
- University of Edinburgh
- Bloodwise [13003]
- Medical Research Council
- Kay Kendall Leukaemia Fund
- Cambridge NIHR Biomedical Research Center
- Cambridge Experimental Cancer Medicine Centre
- Leukemia and Lymphoma Society of America [07037]
- MRC
- BBSRC [BB/K010867/1]
- EU BLUEPRINT
- EpiGeneSys
- Wellcome Trust [104710/Z/14/Z, 095645/Z/11/Z] Funding Source: Wellcome Trust
- BBSRC [BBS/E/B/000C0426] Funding Source: UKRI
- EPSRC [EP/N014529/1] Funding Source: UKRI
- Biotechnology and Biological Sciences Research Council [BBS/E/B/000C0426] Funding Source: researchfish
- Engineering and Physical Sciences Research Council [EP/N014529/1] Funding Source: researchfish
- Medical Research Council [MC_PC_12009] Funding Source: researchfish
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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.
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