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

Kiselev, Vladimir Yu;Kirschner, Kristina;Schaub, Michael T;Andrews, Tallulah;Hemberg, Martin;et.al.
(2017) Nature Methods : techniques for life scientists and chemists — Vol. 14, p. 483-486 (2017)

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Authors
  • Kiselev, Vladimir Yu
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  • Kirschner, Kristina
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  • Schaub, Michael TUCLouvain
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  • Andrews, Tallulah
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  • Hemberg, Martin
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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.
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Kiselev, V. Y., Kirschner, K., Schaub, M. T., Andrews, T., Yiu, A., Chandra, T., Natarajan, K. N., Reik, W., Barahona, M., Green, A. R., & Hemberg, M. (2017). SC3: consensus clustering of single-cell RNA-seq data. Nature Methods : techniques for life scientists and chemists, 14, 483-486. https://doi.org/10.1038/nmeth.4236 (Original work published 2017)