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Single-cell RNA-seq data analysis using graph autoencoders and graph attention networks

With the development of high-throughput sequencing technology, the scale of single-cell RNA sequencing (scRNA-seq) data has surged. Its data are typically high-dimensional, with high dropout noise and high sparsity. Therefore, gene imputation and cell clustering analysis of scRNA-seq data is increas...

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Autores principales: Feng, Xiang, Fang, Fang, Long, Haixia, Zeng, Rao, Yao, Yuhua
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9780469/
https://www.ncbi.nlm.nih.gov/pubmed/36568390
http://dx.doi.org/10.3389/fgene.2022.1003711
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author Feng, Xiang
Fang, Fang
Long, Haixia
Zeng, Rao
Yao, Yuhua
author_facet Feng, Xiang
Fang, Fang
Long, Haixia
Zeng, Rao
Yao, Yuhua
author_sort Feng, Xiang
collection PubMed
description With the development of high-throughput sequencing technology, the scale of single-cell RNA sequencing (scRNA-seq) data has surged. Its data are typically high-dimensional, with high dropout noise and high sparsity. Therefore, gene imputation and cell clustering analysis of scRNA-seq data is increasingly important. Statistical or traditional machine learning methods are inefficient, and improved accuracy is needed. The methods based on deep learning cannot directly process non-Euclidean spatial data, such as cell diagrams. In this study, we developed scGAEGAT, a multi-modal model with graph autoencoders and graph attention networks for scRNA-seq analysis based on graph neural networks. Cosine similarity, median L1 distance, and root-mean-squared error were used to measure the gene imputation performance of different methods for comparison with scGAEGAT. Furthermore, adjusted mutual information, normalized mutual information, completeness score, and Silhouette coefficient score were used to measure the cell clustering performance of different methods for comparison with scGAEGAT. Experimental results demonstrated promising performance of the scGAEGAT model in gene imputation and cell clustering prediction on four scRNA-seq data sets with gold-standard cell labels.
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spelling pubmed-97804692022-12-24 Single-cell RNA-seq data analysis using graph autoencoders and graph attention networks Feng, Xiang Fang, Fang Long, Haixia Zeng, Rao Yao, Yuhua Front Genet Genetics With the development of high-throughput sequencing technology, the scale of single-cell RNA sequencing (scRNA-seq) data has surged. Its data are typically high-dimensional, with high dropout noise and high sparsity. Therefore, gene imputation and cell clustering analysis of scRNA-seq data is increasingly important. Statistical or traditional machine learning methods are inefficient, and improved accuracy is needed. The methods based on deep learning cannot directly process non-Euclidean spatial data, such as cell diagrams. In this study, we developed scGAEGAT, a multi-modal model with graph autoencoders and graph attention networks for scRNA-seq analysis based on graph neural networks. Cosine similarity, median L1 distance, and root-mean-squared error were used to measure the gene imputation performance of different methods for comparison with scGAEGAT. Furthermore, adjusted mutual information, normalized mutual information, completeness score, and Silhouette coefficient score were used to measure the cell clustering performance of different methods for comparison with scGAEGAT. Experimental results demonstrated promising performance of the scGAEGAT model in gene imputation and cell clustering prediction on four scRNA-seq data sets with gold-standard cell labels. Frontiers Media S.A. 2022-12-09 /pmc/articles/PMC9780469/ /pubmed/36568390 http://dx.doi.org/10.3389/fgene.2022.1003711 Text en Copyright © 2022 Feng, Fang, Long, Zeng and Yao. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Genetics
Feng, Xiang
Fang, Fang
Long, Haixia
Zeng, Rao
Yao, Yuhua
Single-cell RNA-seq data analysis using graph autoencoders and graph attention networks
title Single-cell RNA-seq data analysis using graph autoencoders and graph attention networks
title_full Single-cell RNA-seq data analysis using graph autoencoders and graph attention networks
title_fullStr Single-cell RNA-seq data analysis using graph autoencoders and graph attention networks
title_full_unstemmed Single-cell RNA-seq data analysis using graph autoencoders and graph attention networks
title_short Single-cell RNA-seq data analysis using graph autoencoders and graph attention networks
title_sort single-cell rna-seq data analysis using graph autoencoders and graph attention networks
topic Genetics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9780469/
https://www.ncbi.nlm.nih.gov/pubmed/36568390
http://dx.doi.org/10.3389/fgene.2022.1003711
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