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

doi:10.1038/s41592-019-0353-7

2019-03-18

116

Steven J. Altschuler *

DOI：10.1038/s41592-019-0353-7

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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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Nature Methods

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university of california san francisco

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被引数: 67

University of California System

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