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Nat Methods. 2019 Apr;16(4):311-314. doi: 10.1038/s41592-019-0353-7. Epub 2019 Mar 18.

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

Author information

1
Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA, USA.
2
Department of Automation, Tsinghua National Laboratory for Information Science and Technology, Tsinghua University, Beijing, China.
3
Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA, USA. lani.wu@ucsf.edu.
4
Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA, USA. steven.altschuler@ucsf.edu.

Abstract

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.

PMID:
30886411
DOI:
10.1038/s41592-019-0353-7

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