SSRE: Cell Type Detection Based on Sparse Subspace Representation and Similarity Enhancement

Accurate identification of cell types from single-cell RNA sequencing (scRNA-seq) data plays a critical role in a variety of scRNA-seq analysis studies. This task corresponds to solving an unsupervised clustering problem, in which the similarity measurement between cells affects the result significa...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Zhenlan Liang, Min Li, Ruiqing Zheng, Yu Tian, Xuhua Yan, Jin Chen, Fang-Xiang Wu, Jianxin Wang
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	Single-cell RNA sequencing Clustering Cell type Similarity learning Enhancement Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/f6c8f0a9aa7e4bc18122a4fb673de84e
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:f6c8f0a9aa7e4bc18122a4fb673de84e
record_format	dspace
spelling	oai:doaj.org-article:f6c8f0a9aa7e4bc18122a4fb673de84e2021-11-16T04:09:23ZSSRE: Cell Type Detection Based on Sparse Subspace Representation and Similarity Enhancement1672-022910.1016/j.gpb.2020.09.004https://doaj.org/article/f6c8f0a9aa7e4bc18122a4fb673de84e2021-04-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S1672022921000383https://doaj.org/toc/1672-0229Accurate identification of cell types from single-cell RNA sequencing (scRNA-seq) data plays a critical role in a variety of scRNA-seq analysis studies. This task corresponds to solving an unsupervised clustering problem, in which the similarity measurement between cells affects the result significantly. Although many approaches for cell type identification have been proposed, the accuracy still needs to be improved. In this study, we proposed a novel single-cell clustering framework based on similarity learning, called SSRE. SSRE models the relationships between cells based on subspace assumption, and generates a sparse representation of the cell-to-cell similarity. The sparse representation retains the most similar neighbors for each cell. Besides, three classical pairwise similarities are incorporated with a gene selection and enhancement strategy to further improve the effectiveness of SSRE. Tested on ten real scRNA-seq datasets and five simulated datasets, SSRE achieved the superior performance in most cases compared to several state-of-the-art single-cell clustering methods. In addition, SSRE can be extended to visualization of scRNA-seq data and identification of differentially expressed genes. The matlab and python implementations of SSRE are available at https://github.com/CSUBioGroup/SSRE.Zhenlan LiangMin LiRuiqing ZhengYu TianXuhua YanJin ChenFang-Xiang WuJianxin WangElsevierarticleSingle-cell RNA sequencingClusteringCell typeSimilarity learningEnhancementBiology (General)QH301-705.5ENGenomics, Proteomics & Bioinformatics, Vol 19, Iss 2, Pp 282-291 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Single-cell RNA sequencing Clustering Cell type Similarity learning Enhancement Biology (General) QH301-705.5
spellingShingle	Single-cell RNA sequencing Clustering Cell type Similarity learning Enhancement Biology (General) QH301-705.5 Zhenlan Liang Min Li Ruiqing Zheng Yu Tian Xuhua Yan Jin Chen Fang-Xiang Wu Jianxin Wang SSRE: Cell Type Detection Based on Sparse Subspace Representation and Similarity Enhancement
description	Accurate identification of cell types from single-cell RNA sequencing (scRNA-seq) data plays a critical role in a variety of scRNA-seq analysis studies. This task corresponds to solving an unsupervised clustering problem, in which the similarity measurement between cells affects the result significantly. Although many approaches for cell type identification have been proposed, the accuracy still needs to be improved. In this study, we proposed a novel single-cell clustering framework based on similarity learning, called SSRE. SSRE models the relationships between cells based on subspace assumption, and generates a sparse representation of the cell-to-cell similarity. The sparse representation retains the most similar neighbors for each cell. Besides, three classical pairwise similarities are incorporated with a gene selection and enhancement strategy to further improve the effectiveness of SSRE. Tested on ten real scRNA-seq datasets and five simulated datasets, SSRE achieved the superior performance in most cases compared to several state-of-the-art single-cell clustering methods. In addition, SSRE can be extended to visualization of scRNA-seq data and identification of differentially expressed genes. The matlab and python implementations of SSRE are available at https://github.com/CSUBioGroup/SSRE.
format	article
author	Zhenlan Liang Min Li Ruiqing Zheng Yu Tian Xuhua Yan Jin Chen Fang-Xiang Wu Jianxin Wang
author_facet	Zhenlan Liang Min Li Ruiqing Zheng Yu Tian Xuhua Yan Jin Chen Fang-Xiang Wu Jianxin Wang
author_sort	Zhenlan Liang
title	SSRE: Cell Type Detection Based on Sparse Subspace Representation and Similarity Enhancement
title_short	SSRE: Cell Type Detection Based on Sparse Subspace Representation and Similarity Enhancement
title_full	SSRE: Cell Type Detection Based on Sparse Subspace Representation and Similarity Enhancement
title_fullStr	SSRE: Cell Type Detection Based on Sparse Subspace Representation and Similarity Enhancement
title_full_unstemmed	SSRE: Cell Type Detection Based on Sparse Subspace Representation and Similarity Enhancement
title_sort	ssre: cell type detection based on sparse subspace representation and similarity enhancement
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/f6c8f0a9aa7e4bc18122a4fb673de84e
work_keys_str_mv	AT zhenlanliang ssrecelltypedetectionbasedonsparsesubspacerepresentationandsimilarityenhancement AT minli ssrecelltypedetectionbasedonsparsesubspacerepresentationandsimilarityenhancement AT ruiqingzheng ssrecelltypedetectionbasedonsparsesubspacerepresentationandsimilarityenhancement AT yutian ssrecelltypedetectionbasedonsparsesubspacerepresentationandsimilarityenhancement AT xuhuayan ssrecelltypedetectionbasedonsparsesubspacerepresentationandsimilarityenhancement AT jinchen ssrecelltypedetectionbasedonsparsesubspacerepresentationandsimilarityenhancement AT fangxiangwu ssrecelltypedetectionbasedonsparsesubspacerepresentationandsimilarityenhancement AT jianxinwang ssrecelltypedetectionbasedonsparsesubspacerepresentationandsimilarityenhancement
_version_	1718426762967252992

SSRE: Cell Type Detection Based on Sparse Subspace Representation and Similarity Enhancement

Ejemplares similares