دورية أكاديمية
STdGCN: spatial transcriptomic cell-type deconvolution using graph convolutional networks
| العنوان: | STdGCN: spatial transcriptomic cell-type deconvolution using graph convolutional networks |
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| المؤلفون: | Yawei Li, Yuan Luo |
| المصدر: | Genome Biology, Vol 25, Iss 1, Pp 1-24 (2024) |
| بيانات النشر: | BMC, 2024. |
| سنة النشر: | 2024 |
| المجموعة: | LCC:Biology (General) LCC:Genetics |
| مصطلحات موضوعية: | Spatial transcriptomics, Cell-type deconvolution, Deep learning, Graph convolutional networks, Biology (General), QH301-705.5, Genetics, QH426-470 |
| الوصف: | Abstract Spatially resolved transcriptomics integrates high-throughput transcriptome measurements with preserved spatial cellular organization information. However, many technologies cannot reach single-cell resolution. We present STdGCN, a graph model leveraging single-cell RNA sequencing (scRNA-seq) as reference for cell-type deconvolution in spatial transcriptomic (ST) data. STdGCN incorporates expression profiles from scRNA-seq and spatial localization from ST data for deconvolution. Extensive benchmarking on multiple datasets demonstrates that STdGCN outperforms 17 state-of-the-art models. In a human breast cancer Visium dataset, STdGCN delineates stroma, lymphocytes, and cancer cells, aiding tumor microenvironment analysis. In human heart ST data, STdGCN identifies changes in endothelial-cardiomyocyte communications during tissue development. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 1474-760X |
| Relation: | https://doaj.org/toc/1474-760X |
| DOI: | 10.1186/s13059-024-03353-0 |
| URL الوصول: | https://doaj.org/article/5f9471fffef54b019befb8ac77a48884 |
| رقم الأكسشن: | edsdoj.5f9471fffef54b019befb8ac77a48884 |
| قاعدة البيانات: | Directory of Open Access Journals |
Find this article in full text from Springer Verlag. 
Full Text Finder | تدمد: | 1474760X |
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| DOI: | 10.1186/s13059-024-03353-0 |