SpatialGlue is a tool designed to decipher spatial domains from spatial multi-omics data acquired from a single tissue section. It employes graph neural networks with a dual-attention mechanism to accomplish within-omics integration of measured features and spatial information, followed by cross-omics integration.
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References
Zhang, D. et al. Spatial epigenome–transcriptome co-profiling of mammalian tissues. Nature 616, 113–122 (2023). This paper reports two technologies for spatially resolved, genome-wide joint profiling of epigenome and transcriptome.
Ben-Chetrit, N. et al. Integration of whole transcriptome spatial profiling with protein markers. Nat. Biotechnol. 41, 788–793 (2023). This paper reports the SPOTS technique for high-throughput simultaneous spatial transcriptomics and protein profiling.
Long, Y. et al. Spatially informed clustering, integration, and deconvolution of spatial transcriptomics with GraphST. Nat. Commun. 14, 1155 (2023). This paper reports GraphST, a graph self-supervised contrastive learning method for spatial transcriptomics.
Salehi, A. & Davulcu, H. Graph attention auto-encoders. Proc. IEEE 32nd International Conference on Tools with Artificial Intelligence, ICTAI 2020 989–996 (IEEE, 2020). This paper describes the use of attention mechanisms with graph autoencoders.
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This is a summary of: Long, Y. et al. Deciphering spatial domains from spatial multi-omics with SpatialGlue. Nat. Methods https://doi.org/10.1038/s41592-024-02316-4 (2024).
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A machine learning tool for spatial multi-omics. Nat Methods (2024). https://doi.org/10.1038/s41592-024-02358-8
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DOI: https://doi.org/10.1038/s41592-024-02358-8