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A machine learning tool for spatial multi-omics

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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Fig. 1: Dissecting the spatial epigenome–transcriptome of a mouse brain section.

References

  1. 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.

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  2. 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.

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  3. 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.

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  4. 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

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