Deep learning enables accurate clustering with batch effect removal in single-cell RNA-seq analysis

Increasingly large scRNA-seq datasets demand better and more scalable analysis tools. Here, the authors introduce a scalable unsupervised deep embedding algorithm that clusters scRNA-seq data by iteratively optimizing a clustering objective function and enables removal of batch effects.

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Autores principales:	Xiangjie Li, Kui Wang, Yafei Lyu, Huize Pan, Jingxiao Zhang, Dwight Stambolian, Katalin Susztak, Muredach P. Reilly, Gang Hu, Mingyao Li
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/d25ce1857e63443bb60277874e79ad11
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id	oai:doaj.org-article:d25ce1857e63443bb60277874e79ad11
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spelling	oai:doaj.org-article:d25ce1857e63443bb60277874e79ad112021-12-02T15:36:11ZDeep learning enables accurate clustering with batch effect removal in single-cell RNA-seq analysis10.1038/s41467-020-15851-32041-1723https://doaj.org/article/d25ce1857e63443bb60277874e79ad112020-05-01T00:00:00Zhttps://doi.org/10.1038/s41467-020-15851-3https://doaj.org/toc/2041-1723Increasingly large scRNA-seq datasets demand better and more scalable analysis tools. Here, the authors introduce a scalable unsupervised deep embedding algorithm that clusters scRNA-seq data by iteratively optimizing a clustering objective function and enables removal of batch effects.Xiangjie LiKui WangYafei LyuHuize PanJingxiao ZhangDwight StambolianKatalin SusztakMuredach P. ReillyGang HuMingyao LiNature PortfolioarticleScienceQENNature Communications, Vol 11, Iss 1, Pp 1-14 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Xiangjie Li Kui Wang Yafei Lyu Huize Pan Jingxiao Zhang Dwight Stambolian Katalin Susztak Muredach P. Reilly Gang Hu Mingyao Li Deep learning enables accurate clustering with batch effect removal in single-cell RNA-seq analysis
description	Increasingly large scRNA-seq datasets demand better and more scalable analysis tools. Here, the authors introduce a scalable unsupervised deep embedding algorithm that clusters scRNA-seq data by iteratively optimizing a clustering objective function and enables removal of batch effects.
format	article
author	Xiangjie Li Kui Wang Yafei Lyu Huize Pan Jingxiao Zhang Dwight Stambolian Katalin Susztak Muredach P. Reilly Gang Hu Mingyao Li
author_facet	Xiangjie Li Kui Wang Yafei Lyu Huize Pan Jingxiao Zhang Dwight Stambolian Katalin Susztak Muredach P. Reilly Gang Hu Mingyao Li
author_sort	Xiangjie Li
title	Deep learning enables accurate clustering with batch effect removal in single-cell RNA-seq analysis
title_short	Deep learning enables accurate clustering with batch effect removal in single-cell RNA-seq analysis
title_full	Deep learning enables accurate clustering with batch effect removal in single-cell RNA-seq analysis
title_fullStr	Deep learning enables accurate clustering with batch effect removal in single-cell RNA-seq analysis
title_full_unstemmed	Deep learning enables accurate clustering with batch effect removal in single-cell RNA-seq analysis
title_sort	deep learning enables accurate clustering with batch effect removal in single-cell rna-seq analysis
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/d25ce1857e63443bb60277874e79ad11
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Deep learning enables accurate clustering with batch effect removal in single-cell RNA-seq analysis

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