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

Yue Deng; Feng Bao; Qionghai Dai; Lani F. Wu; Steven J. Altschuler

doi:10.1038/s41592-019-0353-7

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

Yue Deng
, Feng Bao
, Qionghai Dai
, Lani F. Wu^*
, Steven J. Altschuler

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

135 Scopus citations

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.

Original language	English
Pages (from-to)	311-314
Number of pages	4
Journal	Nature Methods
Volume	16
Issue number	4
DOIs	https://doi.org/10.1038/s41592-019-0353-7
State	Published - 1 Apr 2019
Externally published	Yes

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title = "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.",

author = "Yue Deng and Feng Bao and Qionghai Dai and Wu, \{Lani F.\} and Altschuler, \{Steven J.\}",

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AU - Bao, Feng

AU - Dai, Qionghai

AU - Wu, Lani F.

AU - Altschuler, Steven J.

PY - 2019/4/1

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AB - 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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U2 - 10.1038/s41592-019-0353-7

DO - 10.1038/s41592-019-0353-7

M3 - 文章

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JO - Nature Methods

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

Abstract

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