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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Vydáno v:	Genome Biology Ročník 26; číslo 1; s. 159
Hlavní autoři:	Zhang, Tianjiao, Zhang, Hongfei, Zhao, Zhongqian, Shao, Saihong, Jiang, Yucai, Zhang, Xiang, Wang, Guohua
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	London BioMed Central 10.06.2025 Springer Nature B.V BMC
Témata:	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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Popis
Shrnutí:	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.
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
ISSN:	1474-760X 1474-7596 1474-760X
DOI:	10.1186/s13059-025-03637-z