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Attention-Based Graph Neural Network for Label Propagation in Single-Cell Omics

Single-cell data analysis has been at forefront of development in biology and medicine since sequencing data have been made available. An important challenge in single-cell data analysis is the identification of cell types. Several methods have been proposed for cell-type identification. However, th...

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Detalles Bibliográficos
Autores principales: Bhadani, Rahul, Chen, Zhuo, An, Lingling
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9957137/
https://www.ncbi.nlm.nih.gov/pubmed/36833434
http://dx.doi.org/10.3390/genes14020506
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author Bhadani, Rahul
Chen, Zhuo
An, Lingling
author_facet Bhadani, Rahul
Chen, Zhuo
An, Lingling
author_sort Bhadani, Rahul
collection PubMed
description Single-cell data analysis has been at forefront of development in biology and medicine since sequencing data have been made available. An important challenge in single-cell data analysis is the identification of cell types. Several methods have been proposed for cell-type identification. However, these methods do not capture the higher-order topological relationship between different samples. In this work, we propose an attention-based graph neural network that captures the higher-order topological relationship between different samples and performs transductive learning for predicting cell types. The evaluation of our method on both simulation and publicly available datasets demonstrates the superiority of our method, scAGN, in terms of prediction accuracy. In addition, our method works best for highly sparse datasets in terms of F1 score, precision score, recall score, and Matthew’s correlation coefficients as well. Further, our method’s runtime complexity is consistently faster compared to other methods.
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spelling pubmed-99571372023-02-25 Attention-Based Graph Neural Network for Label Propagation in Single-Cell Omics Bhadani, Rahul Chen, Zhuo An, Lingling Genes (Basel) Article Single-cell data analysis has been at forefront of development in biology and medicine since sequencing data have been made available. An important challenge in single-cell data analysis is the identification of cell types. Several methods have been proposed for cell-type identification. However, these methods do not capture the higher-order topological relationship between different samples. In this work, we propose an attention-based graph neural network that captures the higher-order topological relationship between different samples and performs transductive learning for predicting cell types. The evaluation of our method on both simulation and publicly available datasets demonstrates the superiority of our method, scAGN, in terms of prediction accuracy. In addition, our method works best for highly sparse datasets in terms of F1 score, precision score, recall score, and Matthew’s correlation coefficients as well. Further, our method’s runtime complexity is consistently faster compared to other methods. MDPI 2023-02-16 /pmc/articles/PMC9957137/ /pubmed/36833434 http://dx.doi.org/10.3390/genes14020506 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Bhadani, Rahul
Chen, Zhuo
An, Lingling
Attention-Based Graph Neural Network for Label Propagation in Single-Cell Omics
title Attention-Based Graph Neural Network for Label Propagation in Single-Cell Omics
title_full Attention-Based Graph Neural Network for Label Propagation in Single-Cell Omics
title_fullStr Attention-Based Graph Neural Network for Label Propagation in Single-Cell Omics
title_full_unstemmed Attention-Based Graph Neural Network for Label Propagation in Single-Cell Omics
title_short Attention-Based Graph Neural Network for Label Propagation in Single-Cell Omics
title_sort attention-based graph neural network for label propagation in single-cell omics
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9957137/
https://www.ncbi.nlm.nih.gov/pubmed/36833434
http://dx.doi.org/10.3390/genes14020506
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