Scalable analysis of cell-type composition from single-cell transcriptomics using deep recurrent learning

被引：114
作者：
Deng, Yue [1 ]
Bao, Feng [2 ]
Dai, Qionghai [2 ]
Wu, Lani F. [1 ]
Altschuler, Steven J. [1 ]
机构：
[1] Univ Calif San Francisco, Dept Pharmaceut Chem, San Francisco, CA 94143 USA
[2] Tsinghua Univ, Tsinghua Natl Lab Informat Sci & Technol, Dept Automat, Beijing, Peoples R China
来源：
NATURE METHODS | 2019年 / 16卷 / 04期
关键词：
NETWORK;
D O I：
10.1038/s41592-019-0353-7
中图分类号：
Q5 [生物化学];
学科分类号：
071010 ; 081704 ;
摘要：
Recent advances in large-scale single-cell RNA-seq enable fine-grained characterization of phenotypically distinct cellular states in heterogeneous tissues. We present scScope, a scalable deep-learning-based approach that can accurately and rapidly identify cell-type composition from millions of noisy single-cell gene-expression profiles.
引用
收藏
页码：311 / +
页数：7
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