stDyer enables spatial domain clustering with dynamic graph embedding
Abstract Spatially resolved transcriptomics (SRT) data provide critical insights into gene expression patterns within tissue contexts, necessitating effective methods for identifying spatial domains. We introduce stDyer, an end-to-end deep learning framework for spatial domain clustering in SRT data...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2025-02-01
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| Series: | Genome Biology |
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| Online Access: | https://doi.org/10.1186/s13059-025-03503-y |
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| _version_ | 1849724004690034688 |
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| author | Ke Xu Yu Xu Zirui Wang Xin Maizie Zhou Lu Zhang |
| author_facet | Ke Xu Yu Xu Zirui Wang Xin Maizie Zhou Lu Zhang |
| author_sort | Ke Xu |
| collection | DOAJ |
| description | Abstract Spatially resolved transcriptomics (SRT) data provide critical insights into gene expression patterns within tissue contexts, necessitating effective methods for identifying spatial domains. We introduce stDyer, an end-to-end deep learning framework for spatial domain clustering in SRT data. stDyer combines Gaussian Mixture Variational AutoEncoder with graph attention networks to learn embeddings and perform clustering. Its dynamic graphs adaptively link units based on Gaussian Mixture assignments, improving clustering and producing smoother domain boundaries. stDyer’s mini-batch strategy and multi-GPU support facilitate scalability to large datasets. Benchmarking against state-of-the-art tools, stDyer demonstrates superior performance in spatial domain clustering, multi-slice analysis, and large-scale dataset handling. |
| format | Article |
| id | doaj-art-395ed433997f421faebd4c53621a7e1b |
| institution | DOAJ |
| issn | 1474-760X |
| language | English |
| publishDate | 2025-02-01 |
| publisher | BMC |
| record_format | Article |
| series | Genome Biology |
| spelling | doaj-art-395ed433997f421faebd4c53621a7e1b2025-08-20T03:10:52ZengBMCGenome Biology1474-760X2025-02-0126112510.1186/s13059-025-03503-ystDyer enables spatial domain clustering with dynamic graph embeddingKe Xu0Yu Xu1Zirui Wang2Xin Maizie Zhou3Lu Zhang4Department of Computer Science, Hong Kong Baptist UniversityDepartment of Computer Science, Hong Kong Baptist UniversityDepartment of Computer Science, Hong Kong Baptist UniversityDepartment of Biomedical Engineering, Vanderbilt UniversityDepartment of Computer Science, Hong Kong Baptist UniversityAbstract Spatially resolved transcriptomics (SRT) data provide critical insights into gene expression patterns within tissue contexts, necessitating effective methods for identifying spatial domains. We introduce stDyer, an end-to-end deep learning framework for spatial domain clustering in SRT data. stDyer combines Gaussian Mixture Variational AutoEncoder with graph attention networks to learn embeddings and perform clustering. Its dynamic graphs adaptively link units based on Gaussian Mixture assignments, improving clustering and producing smoother domain boundaries. stDyer’s mini-batch strategy and multi-GPU support facilitate scalability to large datasets. Benchmarking against state-of-the-art tools, stDyer demonstrates superior performance in spatial domain clustering, multi-slice analysis, and large-scale dataset handling.https://doi.org/10.1186/s13059-025-03503-ySpatially resolved transcriptomicsSpatial domain clusteringDynamic graphsDeep learning |
| spellingShingle | Ke Xu Yu Xu Zirui Wang Xin Maizie Zhou Lu Zhang stDyer enables spatial domain clustering with dynamic graph embedding Genome Biology Spatially resolved transcriptomics Spatial domain clustering Dynamic graphs Deep learning |
| title | stDyer enables spatial domain clustering with dynamic graph embedding |
| title_full | stDyer enables spatial domain clustering with dynamic graph embedding |
| title_fullStr | stDyer enables spatial domain clustering with dynamic graph embedding |
| title_full_unstemmed | stDyer enables spatial domain clustering with dynamic graph embedding |
| title_short | stDyer enables spatial domain clustering with dynamic graph embedding |
| title_sort | stdyer enables spatial domain clustering with dynamic graph embedding |
| topic | Spatially resolved transcriptomics Spatial domain clustering Dynamic graphs Deep learning |
| url | https://doi.org/10.1186/s13059-025-03503-y |
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