聚类分析
生物导体
计算机科学
稳健性(进化)
计算生物学
转录组
共识聚类
数据挖掘
细胞
核糖核酸
RNA序列
人工智能
生物
基因
相关聚类
遗传学
基因表达
CURE数据聚类算法
作者
Vladimir Yu Kiselev,Kristina Kirschner,Michael T. Schaub,Tallulah Andrews,Andrew Yiu,Tamir Chandra,Kedar Nath Natarajan,Wolf Reik,Mauricio Barahona,Anthony Green,Martin Hemberg
出处
期刊:Nature Methods
[Springer Nature]
日期:2017-03-27
卷期号:14 (5): 483-486
被引量:1160
摘要
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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