SC3：单细胞RNA测序数据的一致性聚类
SC3: consensus clustering of single-cell RNA-seq data.
作者信息
Kiselev Vladimir Yu, Kirschner Kristina, Schaub Michael T, Andrews Tallulah, Yiu Andrew, Chandra Tamir, Natarajan Kedar N, Reik Wolf, Barahona Mauricio, Green Anthony R, Hemberg Martin
机构信息
Wellcome Trust Sanger Institute, Hinxton, Cambridge, UK.
Cambridge Institute for Medical Research, Wellcome Trust/MRC Stem Cell Institute and Department of Haematology, University of Cambridge, Hills Road, Cambridge, UK.
出版信息
Nat Methods. 2017 May;14(5):483-486. doi: 10.1038/nmeth.4236. Epub 2017 Mar 27.
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.
单细胞RNA测序能够基于整体转录组图谱对细胞类型进行定量表征。我们提出了单细胞一致性聚类（SC3），这是一种用于无监督聚类的用户友好型工具，它通过一种一致性方法组合多种聚类解决方案，从而实现了高精度和高稳健性（http://bioconductor.org/packages/SC3）。我们证明SC3能够从从患者收集的肿瘤细胞转录组中识别亚克隆。
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