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)
|
Text
141804.pdf - Accepted Version 1MB |
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 |
University Staff: Request a correction | Enlighten Editors: Update this record