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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Bibliographic Details
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
Online Access:	Get full text
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