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

2017; Nature Portfolio; Volume: 14; Issue: 5 Linguagem: Inglês

10.1038/nmeth.4236

1548-7105

Vladimir Yu Kiselev, Kristina Kirschner, Michael T. Schaub, Tallulah Andrews, Andrew Yiu, Tamir Chandra, Kedar Nath Natarajan, Wolf Reik, Mauricio Barahona, Anthony R. Green, Martin Hemberg,

Gene expression and cancer classification

Single-cell consensus clustering (SC3) provides user-friendly, robust and accurate cell clustering as well as downstream analysis for single-cell RNA-seq data. 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.