SC3: consensus clustering of single-cell RNA-seq data
Nature methods, 2017•nature.com
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.
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.
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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