SC3: consensus clustering of single cell RNA-seq data

Kiselev, V. Y. et al. (2017) SC3: consensus clustering of single cell RNA-seq data. Nature Methods, 14(5), pp. 483-486. (doi: 10.1038/nmeth.4236) (PMID:28346451)

141804.pdf - Accepted Version



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 ( We demonstrate that SC3 is capable of identifying subclones from the transcriptomes of neoplastic cells collected from patients.

Item Type:Articles
Glasgow Author(s) Enlighten ID:Kirschner, Dr Kristina
Authors: Kiselev, V. Y., Kirschner, K., Schaub, M. T., Andrews, T., Yiu, A., Chandra, T., Natarajan, K. N., Reik, W., Barahona, M., Green, A. R., and Hemberg, M.
College/School:College of Medical Veterinary and Life Sciences > School of Cancer Sciences
College of Medical Veterinary and Life Sciences > School of Life Sciences
Journal Name:Nature Methods
Publisher:Nature Publishing Group
ISSN (Online):1548-7105
Published Online:27 March 2017
Copyright Holders:Copyright © 2017 Nature America, Inc., part of Springer Nature
First Published:First published in Nature Methods 14(5): 483-486
Publisher Policy:Reproduced in accordance with the publisher copyright policy

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