VGAE-CCI: variational graph autoencoder-based construction of 3D spatial cell–cell communication network

Abstract Cell–cell communication plays a critical role in maintaining normal biological functions, regulating development and differentiation, and controlling immune responses. The rapid development of single-cell RNA sequencing and spatial transcriptomics sequencing (ST-seq) technologies provides e...

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Vydáno v:	Briefings in bioinformatics Ročník 26; číslo 1
Hlavní autoři:	Zhang, Tianjiao, Zhang, Xiang, Wu, Zhenao, Ren, Jixiang, Zhao, Zhongqian, Zhang, Hongfei, Wang, Guohua, Wang, Tao
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	England Oxford University Press 22.11.2024 Oxford Publishing Limited (England)
Témata:	Algorithms Cell Communication Cell differentiation Cell interactions Communication Communication networks Computational Biology - methods Deep Learning Gene Expression Profiling - methods Gene sequencing Graph neural networks Humans Immune response Problem Solving Protocol Reliability Single-Cell Analysis - methods Spatial data Tissues Transcriptome Transcriptomics cell–cell communication variational graph autoencoder scRNA-seq ST-seq
ISSN:	1467-5463, 1477-4054, 1477-4054
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Abstract	Abstract Cell–cell communication plays a critical role in maintaining normal biological functions, regulating development and differentiation, and controlling immune responses. The rapid development of single-cell RNA sequencing and spatial transcriptomics sequencing (ST-seq) technologies provides essential data support for in-depth and comprehensive analysis of cell–cell communication. However, ST-seq data often contain incomplete data and systematic biases, which may reduce the accuracy and reliability of predicting cell–cell communication. Furthermore, other methods for analyzing cell–cell communication mainly focus on individual tissue sections, neglecting cell–cell communication across multiple tissue layers, and fail to comprehensively elucidate cell–cell communication networks within three-dimensional tissues. To address the aforementioned issues, we propose VGAE-CCI, a deep learning framework based on the Variational Graph Autoencoder, capable of identifying cell–cell communication across multiple tissue layers. Additionally, this model can be applied to spatial transcriptomics data with missing or partially incomplete data and can clustered cells at single-cell resolution based on spatial encoding information within complex tissues, thereby enabling more accurate inference of cell–cell communication. Finally, we tested our method on six datasets and compared it with other state of art methods for predicting cell–cell communication. Our method outperformed other methods across multiple metrics, demonstrating its efficiency and reliability in predicting cell–cell communication.
AbstractList	Cell-cell communication plays a critical role in maintaining normal biological functions, regulating development and differentiation, and controlling immune responses. The rapid development of single-cell RNA sequencing and spatial transcriptomics sequencing (ST-seq) technologies provides essential data support for in-depth and comprehensive analysis of cell-cell communication. However, ST-seq data often contain incomplete data and systematic biases, which may reduce the accuracy and reliability of predicting cell-cell communication. Furthermore, other methods for analyzing cell-cell communication mainly focus on individual tissue sections, neglecting cell-cell communication across multiple tissue layers, and fail to comprehensively elucidate cell-cell communication networks within three-dimensional tissues. To address the aforementioned issues, we propose VGAE-CCI, a deep learning framework based on the Variational Graph Autoencoder, capable of identifying cell-cell communication across multiple tissue layers. Additionally, this model can be applied to spatial transcriptomics data with missing or partially incomplete data and can clustered cells at single-cell resolution based on spatial encoding information within complex tissues, thereby enabling more accurate inference of cell-cell communication. Finally, we tested our method on six datasets and compared it with other state of art methods for predicting cell-cell communication. Our method outperformed other methods across multiple metrics, demonstrating its efficiency and reliability in predicting cell-cell communication.Cell-cell communication plays a critical role in maintaining normal biological functions, regulating development and differentiation, and controlling immune responses. The rapid development of single-cell RNA sequencing and spatial transcriptomics sequencing (ST-seq) technologies provides essential data support for in-depth and comprehensive analysis of cell-cell communication. However, ST-seq data often contain incomplete data and systematic biases, which may reduce the accuracy and reliability of predicting cell-cell communication. Furthermore, other methods for analyzing cell-cell communication mainly focus on individual tissue sections, neglecting cell-cell communication across multiple tissue layers, and fail to comprehensively elucidate cell-cell communication networks within three-dimensional tissues. To address the aforementioned issues, we propose VGAE-CCI, a deep learning framework based on the Variational Graph Autoencoder, capable of identifying cell-cell communication across multiple tissue layers. Additionally, this model can be applied to spatial transcriptomics data with missing or partially incomplete data and can clustered cells at single-cell resolution based on spatial encoding information within complex tissues, thereby enabling more accurate inference of cell-cell communication. Finally, we tested our method on six datasets and compared it with other state of art methods for predicting cell-cell communication. Our method outperformed other methods across multiple metrics, demonstrating its efficiency and reliability in predicting cell-cell communication. Cell–cell communication plays a critical role in maintaining normal biological functions, regulating development and differentiation, and controlling immune responses. The rapid development of single-cell RNA sequencing and spatial transcriptomics sequencing (ST-seq) technologies provides essential data support for in-depth and comprehensive analysis of cell–cell communication. However, ST-seq data often contain incomplete data and systematic biases, which may reduce the accuracy and reliability of predicting cell–cell communication. Furthermore, other methods for analyzing cell–cell communication mainly focus on individual tissue sections, neglecting cell–cell communication across multiple tissue layers, and fail to comprehensively elucidate cell–cell communication networks within three-dimensional tissues. To address the aforementioned issues, we propose VGAE-CCI, a deep learning framework based on the Variational Graph Autoencoder, capable of identifying cell–cell communication across multiple tissue layers. Additionally, this model can be applied to spatial transcriptomics data with missing or partially incomplete data and can clustered cells at single-cell resolution based on spatial encoding information within complex tissues, thereby enabling more accurate inference of cell–cell communication. Finally, we tested our method on six datasets and compared it with other state of art methods for predicting cell–cell communication. Our method outperformed other methods across multiple metrics, demonstrating its efficiency and reliability in predicting cell–cell communication. Abstract Cell–cell communication plays a critical role in maintaining normal biological functions, regulating development and differentiation, and controlling immune responses. The rapid development of single-cell RNA sequencing and spatial transcriptomics sequencing (ST-seq) technologies provides essential data support for in-depth and comprehensive analysis of cell–cell communication. However, ST-seq data often contain incomplete data and systematic biases, which may reduce the accuracy and reliability of predicting cell–cell communication. Furthermore, other methods for analyzing cell–cell communication mainly focus on individual tissue sections, neglecting cell–cell communication across multiple tissue layers, and fail to comprehensively elucidate cell–cell communication networks within three-dimensional tissues. To address the aforementioned issues, we propose VGAE-CCI, a deep learning framework based on the Variational Graph Autoencoder, capable of identifying cell–cell communication across multiple tissue layers. Additionally, this model can be applied to spatial transcriptomics data with missing or partially incomplete data and can clustered cells at single-cell resolution based on spatial encoding information within complex tissues, thereby enabling more accurate inference of cell–cell communication. Finally, we tested our method on six datasets and compared it with other state of art methods for predicting cell–cell communication. Our method outperformed other methods across multiple metrics, demonstrating its efficiency and reliability in predicting cell–cell communication.
Author	Wu, Zhenao Ren, Jixiang Zhao, Zhongqian Wang, Tao Zhang, Hongfei Wang, Guohua Zhang, Xiang Zhang, Tianjiao
Author_xml	– sequence: 1 givenname: Tianjiao orcidid: 0000-0001-9807-8620 surname: Zhang fullname: Zhang, Tianjiao email: tianjiaozhang@nefu.edu.cn – sequence: 2 givenname: Xiang orcidid: 0009-0000-2663-3672 surname: Zhang fullname: Zhang, Xiang email: 2765063780@qq.com – sequence: 3 givenname: Zhenao surname: Wu fullname: Wu, Zhenao email: 2022112562@nefu.edu.cn – sequence: 4 givenname: Jixiang orcidid: 0009-0008-3584-6125 surname: Ren fullname: Ren, Jixiang email: 1241126773@qq.com – sequence: 5 givenname: Zhongqian surname: Zhao fullname: Zhao, Zhongqian email: 2023112598@nefu.edu.cn – sequence: 6 givenname: Hongfei surname: Zhang fullname: Zhang, Hongfei email: 2023112592@nefu.edu.cn – sequence: 7 givenname: Guohua surname: Wang fullname: Wang, Guohua email: ghwang@nefu.edu.cn – sequence: 8 givenname: Tao orcidid: 0000-0002-5728-6463 surname: Wang fullname: Wang, Tao email: ghwang@nefu.edu.cn
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Snippet	Abstract Cell–cell communication plays a critical role in maintaining normal biological functions, regulating development and differentiation, and controlling... Cell–cell communication plays a critical role in maintaining normal biological functions, regulating development and differentiation, and controlling immune... Cell-cell communication plays a critical role in maintaining normal biological functions, regulating development and differentiation, and controlling immune...
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SubjectTerms	Algorithms Cell Communication Cell differentiation Cell interactions Communication Communication networks Computational Biology - methods Deep Learning Gene Expression Profiling - methods Gene sequencing Graph neural networks Humans Immune response Problem Solving Protocol Reliability Single-Cell Analysis - methods Spatial data Tissues Transcriptome Transcriptomics
Title	VGAE-CCI: variational graph autoencoder-based construction of 3D spatial cell–cell communication network
URI	https://www.ncbi.nlm.nih.gov/pubmed/39581873 https://www.proquest.com/docview/3133521301 https://www.proquest.com/docview/3132608033 https://pubmed.ncbi.nlm.nih.gov/PMC11586124
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