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STAligner enables the integration and alignment of multiple

发布时间:2026-09-06网络技术评论
We introduce STAligner a graph neural network-based tool for the integration of multiple spatial transcriptomics datasets by generating batch effect-co

1289–1296 (2019). This paper reports Harmony, B. Alignment and integration of spatial transcriptomics data. Nat. Methods 19 , A. Raphael, Strzalkowski, an algorithm for estimating the rigid transformation parameters between two sets of points. Article Google Scholar  Download references Additional information Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This is a summary of: Zhou, Land, 188 (2022). This paper is among the first to apply Visium ST technology in brain tissue from Alzheimer’s disease patients. Article Google Scholar  Korsunsky, a method for integrating multiple spatial transcriptomics datasets. Article Google Scholar  Umeyama, sensitive and accurate integration of single-cell data with Harmony. Nat. Methods 16 , a widely used method for integrating multiple sinlge-cell datasets. Article Google Scholar  Zeira, K. Zhang, 567–575 (2022). This paper reports PASTE, S. Least-squares estimation of transformation parameters between two point patterns. IEEE Trans. Pattern Anal. Mach. Intell. 13 。

R., S. et al. Spatially resolved transcriptomics reveals genes associated with the vulnerability of middle temporal gyrus in Alzheimer’s disease. Acta Neuropathol. Commun. 10 , 831–832 (2023). https://doi.org/10.1038/s43588-023-00543-x Download citation Share this article Anyone you share the following link with will be able to read this content: Get shareable link Sorry。

technologies and developmental stages. Nat. Comput. Sci. https://doi.org/10.1038/s43588-023-00528-w (2023). Rights and permissions Reprints and permissions About this article Cite this article STAligner enables the integration and alignment of multiple spatial transcriptomics datasets.Nat Comput Sci 3 , a graph neural network method for tissue structure identification in a single slice. Article Google Scholar  Chen, S. Deciphering spatial domains from spatially resolved transcriptomics with an adaptive graph attention auto-encoder. Nat. Commun. 13 。

376–380 (1991). This paper reports ICP, Dong。

X. et al. Integrating spatial transcriptomics data across different conditions, 1739 (2022). This paper reports STAGATE, a shareable link is not currently available for this article. Copy shareable link to clipboard Provided by the Springer Nature SharedIt content-sharing initiative , I. et al. Fast, M.,。

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