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scDSSC: Deep Sparse Subspace Clustering for scRNA-seq Data

Single cell RNA sequencing (scRNA-seq) enables researchers to characterize transcriptomic profiles at the single-cell resolution with increasingly high throughput. Clustering is a crucial step in single cell analysis. Clustering analysis of transcriptome profiled by scRNA-seq can reveal the heteroge...

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Detalles Bibliográficos
Autores principales: Wang, HaiYun, Zhao, JianPing, Zheng, ChunHou, Su, YanSen
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9810169/
https://www.ncbi.nlm.nih.gov/pubmed/36534702
http://dx.doi.org/10.1371/journal.pcbi.1010772
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author Wang, HaiYun
Zhao, JianPing
Zheng, ChunHou
Su, YanSen
author_facet Wang, HaiYun
Zhao, JianPing
Zheng, ChunHou
Su, YanSen
author_sort Wang, HaiYun
collection PubMed
description Single cell RNA sequencing (scRNA-seq) enables researchers to characterize transcriptomic profiles at the single-cell resolution with increasingly high throughput. Clustering is a crucial step in single cell analysis. Clustering analysis of transcriptome profiled by scRNA-seq can reveal the heterogeneity and diversity of cells. However, single cell study still remains great challenges due to its high noise and dimension. Subspace clustering aims at discovering the intrinsic structure of data in unsupervised fashion. In this paper, we propose a deep sparse subspace clustering method scDSSC combining noise reduction and dimensionality reduction for scRNA-seq data, which simultaneously learns feature representation and clustering via explicit modelling of scRNA-seq data generation. Experiments on a variety of scRNA-seq datasets from thousands to tens of thousands of cells have shown that scDSSC can significantly improve clustering performance and facilitate the interpretability of clustering and downstream analysis. Compared to some popular scRNA-deq analysis methods, scDSSC outperformed state-of-the-art methods under various clustering performance metrics.
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spelling pubmed-98101692023-01-04 scDSSC: Deep Sparse Subspace Clustering for scRNA-seq Data Wang, HaiYun Zhao, JianPing Zheng, ChunHou Su, YanSen PLoS Comput Biol Research Article Single cell RNA sequencing (scRNA-seq) enables researchers to characterize transcriptomic profiles at the single-cell resolution with increasingly high throughput. Clustering is a crucial step in single cell analysis. Clustering analysis of transcriptome profiled by scRNA-seq can reveal the heterogeneity and diversity of cells. However, single cell study still remains great challenges due to its high noise and dimension. Subspace clustering aims at discovering the intrinsic structure of data in unsupervised fashion. In this paper, we propose a deep sparse subspace clustering method scDSSC combining noise reduction and dimensionality reduction for scRNA-seq data, which simultaneously learns feature representation and clustering via explicit modelling of scRNA-seq data generation. Experiments on a variety of scRNA-seq datasets from thousands to tens of thousands of cells have shown that scDSSC can significantly improve clustering performance and facilitate the interpretability of clustering and downstream analysis. Compared to some popular scRNA-deq analysis methods, scDSSC outperformed state-of-the-art methods under various clustering performance metrics. Public Library of Science 2022-12-19 /pmc/articles/PMC9810169/ /pubmed/36534702 http://dx.doi.org/10.1371/journal.pcbi.1010772 Text en © 2022 Wang et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Wang, HaiYun
Zhao, JianPing
Zheng, ChunHou
Su, YanSen
scDSSC: Deep Sparse Subspace Clustering for scRNA-seq Data
title scDSSC: Deep Sparse Subspace Clustering for scRNA-seq Data
title_full scDSSC: Deep Sparse Subspace Clustering for scRNA-seq Data
title_fullStr scDSSC: Deep Sparse Subspace Clustering for scRNA-seq Data
title_full_unstemmed scDSSC: Deep Sparse Subspace Clustering for scRNA-seq Data
title_short scDSSC: Deep Sparse Subspace Clustering for scRNA-seq Data
title_sort scdssc: deep sparse subspace clustering for scrna-seq data
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9810169/
https://www.ncbi.nlm.nih.gov/pubmed/36534702
http://dx.doi.org/10.1371/journal.pcbi.1010772
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