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

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.

SUBMITTER: Deng Y 

PROVIDER: S-EPMC6774994 | biostudies-literature | 2019 Apr

REPOSITORIES: biostudies-literature

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

Deng Yue Y   Bao Feng F   Dai Qionghai Q   Wu Lani F LF   Altschuler Steven J SJ  

Nature methods 20190318 4

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. ...[more]

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