Journal
NATURE METHODS
Volume 16, Issue 4, Pages 311-+Publisher
NATURE PUBLISHING GROUP
DOI: 10.1038/s41592-019-0353-7
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Funding
- NIH [R01 EY028205, GM112690]
- NSF [PHY-1545915, SU2C/MSKCC 2015-003]
- NCI-NIH [RO1 CA185404, CA184984]
- Institute of Computational Health Sciences (ICHS) at UCSF
- Project of NSFC [61327902]
- Project of Beijing Municipal Science & Technology Commission [Z181100003118014]
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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.
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