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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Published in:Briefings in bioinformatics Vol. 26; no. 1
Main Authors: Zhang, Tianjiao, Zhang, Xiang, Wu, Zhenao, Ren, Jixiang, Zhao, Zhongqian, Zhang, Hongfei, Wang, Guohua, Wang, Tao
Format: Journal Article
Language:English
Published: England Oxford University Press 22.11.2024
Oxford Publishing Limited (England)
Subjects:
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
Online Access:Get full text
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Summary: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.
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ISSN:1467-5463
1477-4054
1477-4054
DOI:10.1093/bib/bbae619