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Topological identification and interpretation for single-cell gene regulation elucidation across multiple platforms using scMGCA

Single-cell RNA sequencing provides high-throughput gene expression information to explore cellular heterogeneity at the individual cell level. A major challenge in characterizing high-throughput gene expression data arises from challenges related to dimensionality, and the prevalence of dropout eve...

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Autores principales: Yu, Zhuohan, Su, Yanchi, Lu, Yifu, Yang, Yuning, Wang, Fuzhou, Zhang, Shixiong, Chang, Yi, Wong, Ka-Chun, Li, Xiangtao
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9877026/
https://www.ncbi.nlm.nih.gov/pubmed/36697410
http://dx.doi.org/10.1038/s41467-023-36134-7
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author Yu, Zhuohan
Su, Yanchi
Lu, Yifu
Yang, Yuning
Wang, Fuzhou
Zhang, Shixiong
Chang, Yi
Wong, Ka-Chun
Li, Xiangtao
author_facet Yu, Zhuohan
Su, Yanchi
Lu, Yifu
Yang, Yuning
Wang, Fuzhou
Zhang, Shixiong
Chang, Yi
Wong, Ka-Chun
Li, Xiangtao
author_sort Yu, Zhuohan
collection PubMed
description Single-cell RNA sequencing provides high-throughput gene expression information to explore cellular heterogeneity at the individual cell level. A major challenge in characterizing high-throughput gene expression data arises from challenges related to dimensionality, and the prevalence of dropout events. To address these concerns, we develop a deep graph learning method, scMGCA, for single-cell data analysis. scMGCA is based on a graph-embedding autoencoder that simultaneously learns cell-cell topology representation and cluster assignments. We show that scMGCA is accurate and effective for cell segregation and batch effect correction, outperforming other state-of-the-art models across multiple platforms. In addition, we perform genomic interpretation on the key compressed transcriptomic space of the graph-embedding autoencoder to demonstrate the underlying gene regulation mechanism. We demonstrate that in a pancreatic ductal adenocarcinoma dataset, scMGCA successfully provides annotations on the specific cell types and reveals differential gene expression levels across multiple tumor-associated and cell signalling pathways.
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spelling pubmed-98770262023-01-27 Topological identification and interpretation for single-cell gene regulation elucidation across multiple platforms using scMGCA Yu, Zhuohan Su, Yanchi Lu, Yifu Yang, Yuning Wang, Fuzhou Zhang, Shixiong Chang, Yi Wong, Ka-Chun Li, Xiangtao Nat Commun Article Single-cell RNA sequencing provides high-throughput gene expression information to explore cellular heterogeneity at the individual cell level. A major challenge in characterizing high-throughput gene expression data arises from challenges related to dimensionality, and the prevalence of dropout events. To address these concerns, we develop a deep graph learning method, scMGCA, for single-cell data analysis. scMGCA is based on a graph-embedding autoencoder that simultaneously learns cell-cell topology representation and cluster assignments. We show that scMGCA is accurate and effective for cell segregation and batch effect correction, outperforming other state-of-the-art models across multiple platforms. In addition, we perform genomic interpretation on the key compressed transcriptomic space of the graph-embedding autoencoder to demonstrate the underlying gene regulation mechanism. We demonstrate that in a pancreatic ductal adenocarcinoma dataset, scMGCA successfully provides annotations on the specific cell types and reveals differential gene expression levels across multiple tumor-associated and cell signalling pathways. Nature Publishing Group UK 2023-01-25 /pmc/articles/PMC9877026/ /pubmed/36697410 http://dx.doi.org/10.1038/s41467-023-36134-7 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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Yu, Zhuohan
Su, Yanchi
Lu, Yifu
Yang, Yuning
Wang, Fuzhou
Zhang, Shixiong
Chang, Yi
Wong, Ka-Chun
Li, Xiangtao
Topological identification and interpretation for single-cell gene regulation elucidation across multiple platforms using scMGCA
title Topological identification and interpretation for single-cell gene regulation elucidation across multiple platforms using scMGCA
title_full Topological identification and interpretation for single-cell gene regulation elucidation across multiple platforms using scMGCA
title_fullStr Topological identification and interpretation for single-cell gene regulation elucidation across multiple platforms using scMGCA
title_full_unstemmed Topological identification and interpretation for single-cell gene regulation elucidation across multiple platforms using scMGCA
title_short Topological identification and interpretation for single-cell gene regulation elucidation across multiple platforms using scMGCA
title_sort topological identification and interpretation for single-cell gene regulation elucidation across multiple platforms using scmgca
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9877026/
https://www.ncbi.nlm.nih.gov/pubmed/36697410
http://dx.doi.org/10.1038/s41467-023-36134-7
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