Model-based deep embedding for constrained clustering analysis of single cell RNA-seq data

Clustering cells based on similarities in gene expression is the first step towards identifying cell types in scRNASeq data. Here the authors incorporate biological knowledge into the clustering step to facilitate the biological interpretability of clusters, and subsequent cell type identification.

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Tian Tian, Jie Zhang, Xiang Lin, Zhi Wei, Hakon Hakonarson
Format:	article
Langue:	EN
Publié:	Nature Portfolio 2021
Sujets:	Science Q
Accès en ligne:	https://doaj.org/article/5d61bdf78def44fd9d00e5d1ec5fd2c2
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

id	oai:doaj.org-article:5d61bdf78def44fd9d00e5d1ec5fd2c2
record_format	dspace
spelling	oai:doaj.org-article:5d61bdf78def44fd9d00e5d1ec5fd2c22021-12-02T16:36:00ZModel-based deep embedding for constrained clustering analysis of single cell RNA-seq data10.1038/s41467-021-22008-32041-1723https://doaj.org/article/5d61bdf78def44fd9d00e5d1ec5fd2c22021-03-01T00:00:00Zhttps://doi.org/10.1038/s41467-021-22008-3https://doaj.org/toc/2041-1723Clustering cells based on similarities in gene expression is the first step towards identifying cell types in scRNASeq data. Here the authors incorporate biological knowledge into the clustering step to facilitate the biological interpretability of clusters, and subsequent cell type identification.Tian TianJie ZhangXiang LinZhi WeiHakon HakonarsonNature PortfolioarticleScienceQENNature Communications, Vol 12, Iss 1, Pp 1-12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Tian Tian Jie Zhang Xiang Lin Zhi Wei Hakon Hakonarson Model-based deep embedding for constrained clustering analysis of single cell RNA-seq data
description	Clustering cells based on similarities in gene expression is the first step towards identifying cell types in scRNASeq data. Here the authors incorporate biological knowledge into the clustering step to facilitate the biological interpretability of clusters, and subsequent cell type identification.
format	article
author	Tian Tian Jie Zhang Xiang Lin Zhi Wei Hakon Hakonarson
author_facet	Tian Tian Jie Zhang Xiang Lin Zhi Wei Hakon Hakonarson
author_sort	Tian Tian
title	Model-based deep embedding for constrained clustering analysis of single cell RNA-seq data
title_short	Model-based deep embedding for constrained clustering analysis of single cell RNA-seq data
title_full	Model-based deep embedding for constrained clustering analysis of single cell RNA-seq data
title_fullStr	Model-based deep embedding for constrained clustering analysis of single cell RNA-seq data
title_full_unstemmed	Model-based deep embedding for constrained clustering analysis of single cell RNA-seq data
title_sort	model-based deep embedding for constrained clustering analysis of single cell rna-seq data
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/5d61bdf78def44fd9d00e5d1ec5fd2c2
work_keys_str_mv	AT tiantian modelbaseddeepembeddingforconstrainedclusteringanalysisofsinglecellrnaseqdata AT jiezhang modelbaseddeepembeddingforconstrainedclusteringanalysisofsinglecellrnaseqdata AT xianglin modelbaseddeepembeddingforconstrainedclusteringanalysisofsinglecellrnaseqdata AT zhiwei modelbaseddeepembeddingforconstrainedclusteringanalysisofsinglecellrnaseqdata AT hakonhakonarson modelbaseddeepembeddingforconstrainedclusteringanalysisofsinglecellrnaseqdata
_version_	1718383635469434880

Model-based deep embedding for constrained clustering analysis of single cell RNA-seq data

Documents similaires