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STdGCN: spatial transcriptomic cell-type deconvolution using graph convolutional networks.
- Source :
-
Genome biology [Genome Biol] 2024 Aug 05; Vol. 25 (1), pp. 206. Date of Electronic Publication: 2024 Aug 05. - Publication Year :
- 2024
-
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.<br /> (© 2024. The Author(s).)
Details
- Language :
- English
- ISSN :
- 1474-760X
- Volume :
- 25
- Issue :
- 1
- Database :
- MEDLINE
- Journal :
- Genome biology
- Publication Type :
- Academic Journal
- Accession number :
- 39103939
- Full Text :
- https://doi.org/10.1186/s13059-024-03353-0