spaMGCN: a graph convolutional network with autoencoder for spatial domain identification using multi-scale adaptation

Spatial domain identification is crucial in spatial transcriptomics analysis. Existing methods excel with continuous and clustered distributions but struggle with discrete ones. We present spaMGCN, an innovative approach specifically designed for identifying spatial domains, especially in discrete t...

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Published in:	Genome Biology Vol. 26; no. 1; p. 159
Main Authors:	Zhang, Tianjiao, Zhang, Hongfei, Zhao, Zhongqian, Shao, Saihong, Jiang, Yucai, Zhang, Xiang, Wang, Guohua
Format:	Journal Article
Language:	English
Published:	London BioMed Central 10.06.2025 Springer Nature B.V BMC
Subjects:	Animal Genetics and Genomics Animals Autoencoder Bioinformatics Biomedical and Life Sciences Cluster analysis Datasets Deep learning Discrete distribution spatial domain domain epigenome Epigenomics - methods Evolutionary Biology Gene expression Gene Expression Profiling - methods Graphs Human Genetics Humans Identification Life Sciences lymph Lymph nodes Lymph Nodes - metabolism Lymphocytes T Methodology Mice Microbial Genetics and Genomics Multi-source feature fusion Neighborhoods Neural networks Plant Genetics and Genomics Software Spatial domain identification Spatial multi-omics data Spleen Spleen - metabolism Statistical analysis T-lymphocytes Transcriptome Transcriptomics Discrete distribution spatial domain Multi-source feature fusion Spatial domain identification Spatial multi-omics data
ISSN:	1474-760X, 1474-7596, 1474-760X
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Abstract	Spatial domain identification is crucial in spatial transcriptomics analysis. Existing methods excel with continuous and clustered distributions but struggle with discrete ones. We present spaMGCN, an innovative approach specifically designed for identifying spatial domains, especially in discrete tissue distributions. By integrating spatial transcriptomics and spatial epigenomic data through an autoencoder and a multi-scale adaptive graph convolutional network, spaMGCN outperforms baseline methods. Our evaluations demonstrate its effectiveness in recognizing discrete T cell zones in mouse spleens and follicular cells in human lymph nodes, as well as effectively distinguishing capsule structures from surrounding tissues.
AbstractList	Spatial domain identification is crucial in spatial transcriptomics analysis. Existing methods excel with continuous and clustered distributions but struggle with discrete ones. We present spaMGCN, an innovative approach specifically designed for identifying spatial domains, especially in discrete tissue distributions. By integrating spatial transcriptomics and spatial epigenomic data through an autoencoder and a multi-scale adaptive graph convolutional network, spaMGCN outperforms baseline methods. Our evaluations demonstrate its effectiveness in recognizing discrete T cell zones in mouse spleens and follicular cells in human lymph nodes, as well as effectively distinguishing capsule structures from surrounding tissues. Abstract Spatial domain identification is crucial in spatial transcriptomics analysis. Existing methods excel with continuous and clustered distributions but struggle with discrete ones. We present spaMGCN, an innovative approach specifically designed for identifying spatial domains, especially in discrete tissue distributions. By integrating spatial transcriptomics and spatial epigenomic data through an autoencoder and a multi-scale adaptive graph convolutional network, spaMGCN outperforms baseline methods. Our evaluations demonstrate its effectiveness in recognizing discrete T cell zones in mouse spleens and follicular cells in human lymph nodes, as well as effectively distinguishing capsule structures from surrounding tissues. Spatial domain identification is crucial in spatial transcriptomics analysis. Existing methods excel with continuous and clustered distributions but struggle with discrete ones. We present spaMGCN, an innovative approach specifically designed for identifying spatial domains, especially in discrete tissue distributions. By integrating spatial transcriptomics and spatial epigenomic data through an autoencoder and a multi-scale adaptive graph convolutional network, spaMGCN outperforms baseline methods. Our evaluations demonstrate its effectiveness in recognizing discrete T cell zones in mouse spleens and follicular cells in human lymph nodes, as well as effectively distinguishing capsule structures from surrounding tissues.Spatial domain identification is crucial in spatial transcriptomics analysis. Existing methods excel with continuous and clustered distributions but struggle with discrete ones. We present spaMGCN, an innovative approach specifically designed for identifying spatial domains, especially in discrete tissue distributions. By integrating spatial transcriptomics and spatial epigenomic data through an autoencoder and a multi-scale adaptive graph convolutional network, spaMGCN outperforms baseline methods. Our evaluations demonstrate its effectiveness in recognizing discrete T cell zones in mouse spleens and follicular cells in human lymph nodes, as well as effectively distinguishing capsule structures from surrounding tissues.
ArticleNumber	159
Author	Jiang, Yucai Shao, Saihong Zhao, Zhongqian Zhang, Hongfei Wang, Guohua Zhang, Xiang Zhang, Tianjiao
Author_xml	– sequence: 1 givenname: Tianjiao surname: Zhang fullname: Zhang, Tianjiao organization: College of Computer and Control Engineering, Northeast Forestry University – sequence: 2 givenname: Hongfei surname: Zhang fullname: Zhang, Hongfei organization: College of Computer and Control Engineering, Northeast Forestry University – sequence: 3 givenname: Zhongqian surname: Zhao fullname: Zhao, Zhongqian organization: College of Computer and Control Engineering, Northeast Forestry University – sequence: 4 givenname: Saihong surname: Shao fullname: Shao, Saihong organization: College of Computer and Control Engineering, Northeast Forestry University – sequence: 5 givenname: Yucai surname: Jiang fullname: Jiang, Yucai organization: College of Computer and Control Engineering, Northeast Forestry University – sequence: 6 givenname: Xiang surname: Zhang fullname: Zhang, Xiang organization: College of Computer and Control Engineering, Northeast Forestry University – sequence: 7 givenname: Guohua surname: Wang fullname: Wang, Guohua email: ghwang@nefu.edu.cn organization: College of Computer and Control Engineering, Northeast Forestry University, Faculty of Computing, Harbin Institute of Technology
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Keywords	Discrete distribution spatial domain Multi-source feature fusion Spatial domain identification Spatial multi-omics data
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Snippet	Spatial domain identification is crucial in spatial transcriptomics analysis. Existing methods excel with continuous and clustered distributions but struggle... Abstract Spatial domain identification is crucial in spatial transcriptomics analysis. Existing methods excel with continuous and clustered distributions but...
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SubjectTerms	Animal Genetics and Genomics Animals Autoencoder Bioinformatics Biomedical and Life Sciences Cluster analysis Datasets Deep learning Discrete distribution spatial domain domain epigenome Epigenomics - methods Evolutionary Biology Gene expression Gene Expression Profiling - methods Graphs Human Genetics Humans Identification Life Sciences lymph Lymph nodes Lymph Nodes - metabolism Lymphocytes T Methodology Mice Microbial Genetics and Genomics Multi-source feature fusion Neighborhoods Neural networks Plant Genetics and Genomics Software Spatial domain identification Spatial multi-omics data Spleen Spleen - metabolism Statistical analysis T-lymphocytes Transcriptome Transcriptomics
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Title	spaMGCN: a graph convolutional network with autoencoder for spatial domain identification using multi-scale adaptation
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