GraphCVAE: Uncovering cell heterogeneity and therapeutic target discovery through residual and contrastive learning

Advancements in Spatial Transcriptomics (ST) technologies in recent years have transformed the analysis of tissue structure and function within spatial contexts. However, accurately identifying spatial domains remains challenging due to data sparsity and noise. Traditional clustering methods often f...

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Published in:	Life sciences (1973) Vol. 359; p. 123208
Main Authors:	Zhang, Zhiwei, Wang, Mengqiu, Dai, Ruoyan, Wang, Zhenghui, Lei, Lixin, Zhao, Xudong, Han, Kaitai, Shi, Chaojing, Guo, Qianjin
Format:	Journal Article
Language:	English
Published:	Netherlands Elsevier Inc 15.12.2024
Subjects:	colorectal neoplasms Colorectal Neoplasms - genetics Colorectal Neoplasms - pathology Contrastive learning data collection Deep learning domain extracellular matrix Gene expression Gene Expression Profiling - methods glioblastoma Glioblastoma - genetics Glioblastoma - pathology Humans Machine Learning Neural Networks, Computer Prefrontal Cortex Spatial clustering spatial data Spatial transcriptomics therapeutics Transcriptome transcriptomics Variational graph autoencoder Deep learning Spatial clustering Gene expression Spatial transcriptomics Variational graph autoencoder Contrastive learning
ISSN:	0024-3205, 1879-0631, 1879-0631
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Abstract	Advancements in Spatial Transcriptomics (ST) technologies in recent years have transformed the analysis of tissue structure and function within spatial contexts. However, accurately identifying spatial domains remains challenging due to data sparsity and noise. Traditional clustering methods often fail to capture spatial dependencies, while spatial clustering methods struggle with batch effects and data integration. We introduce GraphCVAE, a model designed to enhance spatial domain identification by integrating spatial and morphological information, correcting batch effects, and managing heterogeneous data. GraphCVAE employs a multi-layer Graph Convolutional Network (GCN) and a variational autoencoder to improve the representation and integration of spatial information. Through contrastive learning, the model captures subtle differences between cell types and states. Extensive testing on various ST datasets demonstrates GraphCVAE's robustness and biological contributions. In the dorsolateral prefrontal cortex (DLPFC) dataset, it accurately delineates cortical layer boundaries. In glioblastoma, GraphCVAE reveals critical therapeutic targets such as TF and NFIB. In colorectal cancer, it explores the role of the extracellular matrix in colorectal cancer. The model's performance metrics consistently surpass existing methods, validating its effectiveness. GraphCVAE's advanced visualization capabilities further highlight its precision in resolving spatial structures, making it a powerful tool for spatial transcriptomics analysis and offering new insights into disease studies.
AbstractList	Advancements in Spatial Transcriptomics (ST) technologies in recent years have transformed the analysis of tissue structure and function within spatial contexts. However, accurately identifying spatial domains remains challenging due to data sparsity and noise. Traditional clustering methods often fail to capture spatial dependencies, while spatial clustering methods struggle with batch effects and data integration. We introduce GraphCVAE, a model designed to enhance spatial domain identification by integrating spatial and morphological information, correcting batch effects, and managing heterogeneous data. GraphCVAE employs a multi-layer Graph Convolutional Network (GCN) and a variational autoencoder to improve the representation and integration of spatial information. Through contrastive learning, the model captures subtle differences between cell types and states. Extensive testing on various ST datasets demonstrates GraphCVAE's robustness and biological contributions. In the dorsolateral prefrontal cortex (DLPFC) dataset, it accurately delineates cortical layer boundaries. In glioblastoma, GraphCVAE reveals critical therapeutic targets such as TF and NFIB. In colorectal cancer, it explores the role of the extracellular matrix in colorectal cancer. The model's performance metrics consistently surpass existing methods, validating its effectiveness. GraphCVAE's advanced visualization capabilities further highlight its precision in resolving spatial structures, making it a powerful tool for spatial transcriptomics analysis and offering new insights into disease studies. Advancements in Spatial Transcriptomics (ST) technologies in recent years have transformed the analysis of tissue structure and function within spatial contexts. However, accurately identifying spatial domains remains challenging due to data sparsity and noise. Traditional clustering methods often fail to capture spatial dependencies, while spatial clustering methods struggle with batch effects and data integration. We introduce GraphCVAE, a model designed to enhance spatial domain identification by integrating spatial and morphological information, correcting batch effects, and managing heterogeneous data. GraphCVAE employs a multi-layer Graph Convolutional Network (GCN) and a variational autoencoder to improve the representation and integration of spatial information. Through contrastive learning, the model captures subtle differences between cell types and states. Extensive testing on various ST datasets demonstrates GraphCVAE's robustness and biological contributions. In the dorsolateral prefrontal cortex (DLPFC) dataset, it accurately delineates cortical layer boundaries. In glioblastoma, GraphCVAE reveals critical therapeutic targets such as TF and NFIB. In colorectal cancer, it explores the role of the extracellular matrix in colorectal cancer. The model's performance metrics consistently surpass existing methods, validating its effectiveness. GraphCVAE's advanced visualization capabilities further highlight its precision in resolving spatial structures, making it a powerful tool for spatial transcriptomics analysis and offering new insights into disease studies.Advancements in Spatial Transcriptomics (ST) technologies in recent years have transformed the analysis of tissue structure and function within spatial contexts. However, accurately identifying spatial domains remains challenging due to data sparsity and noise. Traditional clustering methods often fail to capture spatial dependencies, while spatial clustering methods struggle with batch effects and data integration. We introduce GraphCVAE, a model designed to enhance spatial domain identification by integrating spatial and morphological information, correcting batch effects, and managing heterogeneous data. GraphCVAE employs a multi-layer Graph Convolutional Network (GCN) and a variational autoencoder to improve the representation and integration of spatial information. Through contrastive learning, the model captures subtle differences between cell types and states. Extensive testing on various ST datasets demonstrates GraphCVAE's robustness and biological contributions. In the dorsolateral prefrontal cortex (DLPFC) dataset, it accurately delineates cortical layer boundaries. In glioblastoma, GraphCVAE reveals critical therapeutic targets such as TF and NFIB. In colorectal cancer, it explores the role of the extracellular matrix in colorectal cancer. The model's performance metrics consistently surpass existing methods, validating its effectiveness. GraphCVAE's advanced visualization capabilities further highlight its precision in resolving spatial structures, making it a powerful tool for spatial transcriptomics analysis and offering new insights into disease studies.
ArticleNumber	123208
Author	Zhao, Xudong Zhang, Zhiwei Wang, Zhenghui Dai, Ruoyan Shi, Chaojing Lei, Lixin Wang, Mengqiu Han, Kaitai Guo, Qianjin
Author_xml	– sequence: 1 givenname: Zhiwei surname: Zhang fullname: Zhang, Zhiwei – sequence: 2 givenname: Mengqiu surname: Wang fullname: Wang, Mengqiu – sequence: 3 givenname: Ruoyan surname: Dai fullname: Dai, Ruoyan – sequence: 4 givenname: Zhenghui surname: Wang fullname: Wang, Zhenghui – sequence: 5 givenname: Lixin surname: Lei fullname: Lei, Lixin – sequence: 6 givenname: Xudong surname: Zhao fullname: Zhao, Xudong – sequence: 7 givenname: Kaitai surname: Han fullname: Han, Kaitai – sequence: 8 givenname: Chaojing surname: Shi fullname: Shi, Chaojing – sequence: 9 givenname: Qianjin surname: Guo fullname: Guo, Qianjin email: guoqj@iccas.ac.cn, guoqianjin@bipt.edu.cn
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/39488267$$D View this record in MEDLINE/PubMed
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Cites_doi	10.1038/s41593-020-00787-0 10.1177/0192623309335060 10.1101/gad.1632008 10.1039/C0AN00560F 10.1038/s41467-022-29439-6 10.32614/RJ-2016-021 10.1038/s41587-020-0739-1 10.1016/j.nlm.2007.04.013 10.1038/s41592-019-0619-0 10.1126/science.abp9444 10.1109/3477.764879 10.1002/iub.1956 10.1093/nar/gkac901 10.1109/TNN.2008.2005601 10.1038/s41467-023-36796-3 10.1093/bib/bbae016 10.1371/journal.pgen.1005175 10.1038/s41467-023-43458-x 10.1186/s13059-019-1663-x 10.1016/j.expneurol.2024.114804 10.18632/oncotarget.8720 10.3389/fphar.2022.1071897 10.1038/ncomms13998 10.3390/ijms23136879 10.1523/JNEUROSCI.0590-08.2008 10.1371/journal.pcbi.1002488 10.1523/JNEUROSCI.1588-14.2014 10.1186/s13059-017-1382-0 10.1038/s41587-021-00935-2 10.1038/s41467-022-33619-9 10.4161/cc.7.10.5953 10.1088/1742-5468/2008/10/P10008 10.1517/14728222.12.4.491 10.1038/bjc.2014.226 10.1016/j.canlet.2015.02.021 10.1038/ncomms14495 10.1038/s41592-022-01409-2
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Keywords	Deep learning Spatial clustering Gene expression Spatial transcriptomics Variational graph autoencoder Contrastive learning
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Snippet	Advancements in Spatial Transcriptomics (ST) technologies in recent years have transformed the analysis of tissue structure and function within spatial...
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SubjectTerms	colorectal neoplasms Colorectal Neoplasms - genetics Colorectal Neoplasms - pathology Contrastive learning data collection Deep learning domain extracellular matrix Gene expression Gene Expression Profiling - methods glioblastoma Glioblastoma - genetics Glioblastoma - pathology Humans Machine Learning Neural Networks, Computer Prefrontal Cortex Spatial clustering spatial data Spatial transcriptomics therapeutics Transcriptome transcriptomics Variational graph autoencoder
Title	GraphCVAE: Uncovering cell heterogeneity and therapeutic target discovery through residual and contrastive learning
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