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scSemiAAE: a semi-supervised clustering model for single-cell RNA-seq data

BACKGROUND: Single-cell RNA sequencing (scRNA-seq) strives to capture cellular diversity with higher resolution than bulk RNA sequencing. Clustering analysis is critical to transcriptome research as it allows for further identification and discovery of new cell types. Unsupervised clustering cannot...

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Autores principales: Wang, Zile, Wang, Haiyun, Zhao, Jianping, Zheng, Chunhou
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10214737/
https://www.ncbi.nlm.nih.gov/pubmed/37237310
http://dx.doi.org/10.1186/s12859-023-05339-4
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author Wang, Zile
Wang, Haiyun
Zhao, Jianping
Zheng, Chunhou
author_facet Wang, Zile
Wang, Haiyun
Zhao, Jianping
Zheng, Chunhou
author_sort Wang, Zile
collection PubMed
description BACKGROUND: Single-cell RNA sequencing (scRNA-seq) strives to capture cellular diversity with higher resolution than bulk RNA sequencing. Clustering analysis is critical to transcriptome research as it allows for further identification and discovery of new cell types. Unsupervised clustering cannot integrate prior knowledge where relevant information is widely available. Purely unsupervised clustering algorithms may not yield biologically interpretable clusters when confronted with the high dimensionality of scRNA-seq data and frequent dropout events, which makes identification of cell types more challenging. RESULTS: We propose scSemiAAE, a semi-supervised clustering model for scRNA sequence analysis using deep generative neural networks. Specifically, scSemiAAE carefully designs a ZINB adversarial autoencoder-based architecture that inherently integrates adversarial training and semi-supervised modules in the latent space. In a series of experiments on scRNA-seq datasets spanning thousands to tens of thousands of cells, scSemiAAE can significantly improve clustering performance compared to dozens of unsupervised and semi-supervised algorithms, promoting clustering and interpretability of downstream analyses. CONCLUSION: scSemiAAE is a Python-based algorithm implemented on the VSCode platform that provides efficient visualization, clustering, and cell type assignment for scRNA-seq data. The tool is available from https://github.com/WHang98/scSemiAAE. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12859-023-05339-4.
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spelling pubmed-102147372023-05-27 scSemiAAE: a semi-supervised clustering model for single-cell RNA-seq data Wang, Zile Wang, Haiyun Zhao, Jianping Zheng, Chunhou BMC Bioinformatics Research BACKGROUND: Single-cell RNA sequencing (scRNA-seq) strives to capture cellular diversity with higher resolution than bulk RNA sequencing. Clustering analysis is critical to transcriptome research as it allows for further identification and discovery of new cell types. Unsupervised clustering cannot integrate prior knowledge where relevant information is widely available. Purely unsupervised clustering algorithms may not yield biologically interpretable clusters when confronted with the high dimensionality of scRNA-seq data and frequent dropout events, which makes identification of cell types more challenging. RESULTS: We propose scSemiAAE, a semi-supervised clustering model for scRNA sequence analysis using deep generative neural networks. Specifically, scSemiAAE carefully designs a ZINB adversarial autoencoder-based architecture that inherently integrates adversarial training and semi-supervised modules in the latent space. In a series of experiments on scRNA-seq datasets spanning thousands to tens of thousands of cells, scSemiAAE can significantly improve clustering performance compared to dozens of unsupervised and semi-supervised algorithms, promoting clustering and interpretability of downstream analyses. CONCLUSION: scSemiAAE is a Python-based algorithm implemented on the VSCode platform that provides efficient visualization, clustering, and cell type assignment for scRNA-seq data. The tool is available from https://github.com/WHang98/scSemiAAE. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12859-023-05339-4. BioMed Central 2023-05-26 /pmc/articles/PMC10214737/ /pubmed/37237310 http://dx.doi.org/10.1186/s12859-023-05339-4 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Wang, Zile
Wang, Haiyun
Zhao, Jianping
Zheng, Chunhou
scSemiAAE: a semi-supervised clustering model for single-cell RNA-seq data
title scSemiAAE: a semi-supervised clustering model for single-cell RNA-seq data
title_full scSemiAAE: a semi-supervised clustering model for single-cell RNA-seq data
title_fullStr scSemiAAE: a semi-supervised clustering model for single-cell RNA-seq data
title_full_unstemmed scSemiAAE: a semi-supervised clustering model for single-cell RNA-seq data
title_short scSemiAAE: a semi-supervised clustering model for single-cell RNA-seq data
title_sort scsemiaae: a semi-supervised clustering model for single-cell rna-seq data
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10214737/
https://www.ncbi.nlm.nih.gov/pubmed/37237310
http://dx.doi.org/10.1186/s12859-023-05339-4
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AT zhengchunhou scsemiaaeasemisupervisedclusteringmodelforsinglecellrnaseqdata