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)

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.

• Item Type: Articles
• Status: Published
• Refereed: Yes
• 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: 1548-7091
• 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