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

被引：120

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

页码：311 / +

页数：7

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