We introduce STAligner — a graph neural network-based tool for the integration of multiple spatial transcriptomics datasets by generating batch effect-corrected embeddings, thereby enabling consensus spatial domain identification and accurate 3D tissue reconstruction.
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References
Dong, K. & Zhang, S. Deciphering spatial domains from spatially resolved transcriptomics with an adaptive graph attention auto-encoder. Nat. Commun. 13, 1739 (2022). This paper reports STAGATE, a graph neural network method for tissue structure identification in a single slice.
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This is a summary of: Zhou, X. et al. Integrating spatial transcriptomics data across different conditions, technologies and developmental stages. Nat. Comput. Sci. https://doi.org/10.1038/s43588-023-00528-w (2023).
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STAligner enables the integration and alignment of multiple spatial transcriptomics datasets. Nat Comput Sci 3, 831–832 (2023). https://doi.org/10.1038/s43588-023-00543-x
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DOI: https://doi.org/10.1038/s43588-023-00543-x
