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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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.
Chen, S. et al. Spatially resolved transcriptomics reveals genes associated with the vulnerability of middle temporal gyrus in Alzheimer’s disease. Acta Neuropathol. Commun. 10, 188 (2022). This paper is among the first to apply Visium ST technology in brain tissue from Alzheimer’s disease patients.
Korsunsky, I. et al. Fast, sensitive and accurate integration of single-cell data with Harmony. Nat. Methods 16, 1289–1296 (2019). This paper reports Harmony, a widely used method for integrating multiple sinlge-cell datasets.
Zeira, R., Land, M., Strzalkowski, A. & Raphael, B. Alignment and integration of spatial transcriptomics data. Nat. Methods 19, 567–575 (2022). This paper reports PASTE, a method for integrating multiple spatial transcriptomics datasets.
Umeyama, S. Least-squares estimation of transformation parameters between two point patterns. IEEE Trans. Pattern Anal. Mach. Intell. 13, 376–380 (1991). This paper reports ICP, an algorithm for estimating the rigid transformation parameters between two sets of points.
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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