{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T02:34:59Z","timestamp":1775183699284,"version":"3.50.1"},"reference-count":20,"publisher":"Oxford University Press (OUP)","issue":"Supplement_2","license":[{"start":{"date-parts":[[2024,9,4]],"date-time":"2024-09-04T00:00:00Z","timestamp":1725408000000},"content-version":"vor","delay-in-days":3,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2022YFA1004801"],"award-info":[{"award-number":["2022YFA1004801"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["12071466"],"award-info":[{"award-number":["12071466"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,9,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Summary<\/jats:title>\n                    <jats:p>Spatial transcriptomics (ST) technologies enable the measurement of mRNA expression while simultaneously capturing spot locations. By integrating ST data, the 3D structure of a tissue can be reconstructed, yielding a comprehensive understanding of the tissue\u2019s intricacies. Nevertheless, a computational challenge persists: how to remove batch effects while preserving genuine biological structure variations across ST data. To address this, we introduce Graspot, a graph attention network designed for spatial transcriptomics data integration with unbalanced optimal transport. Graspot adeptly harnesses both gene expression and spatial information to align common structures across multiple ST datasets. It embeds multiple ST datasets into a unified latent space, facilitating the partial alignment of spots from different slices. Demonstrating superior performance compared to existing methods on four real ST datasets, Graspot excels in ST data integration, including tasks that require partial alignment. In particular, Graspot efficiently integrates multiple ST slices and guides coordinate alignment. In addition, Graspot accurately aligns the spatio-temporal transcriptomics data to reconstruct human heart developmental processes.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and implementation<\/jats:title>\n                    <jats:p>Graspot software is available at https:\/\/github.com\/zhan009\/Graspot.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btae394","type":"journal-article","created":{"date-parts":[[2024,6,15]],"date-time":"2024-06-15T19:01:18Z","timestamp":1718478078000},"page":"ii137-ii145","source":"Crossref","is-referenced-by-count":5,"title":["Graspot: a graph attention network for spatial transcriptomics data integration with optimal transport"],"prefix":"10.1093","volume":"40","author":[{"given":"Zizhan","family":"Gao","sequence":"first","affiliation":[{"name":"Academy of Mathematics and Systems Science, Chinese Academy of Sciences , Beijing 100190,","place":["China"]},{"name":"School of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing 100049,","place":["China"]}]},{"given":"Kai","family":"Cao","sequence":"additional","affiliation":[{"name":"Eric and Wendy Schmidt Center, Broad Institute of MIT and Harvard , Cambridge, MA 02142,","place":["United States"]}]},{"given":"Lin","family":"Wan","sequence":"additional","affiliation":[{"name":"Academy of Mathematics and Systems Science, Chinese Academy of Sciences , Beijing 100190,","place":["China"]},{"name":"School of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing 100049,","place":["China"]}]}],"member":"286","published-online":{"date-parts":[[2024,9,4]]},"reference":[{"key":"2024102913564899600_btae394-B1","doi-asserted-by":"crossref","first-page":"6012","DOI":"10.1038\/s41467-021-26271-2","article-title":"Spatial deconvolution of HER2-positive breast cancer delineates tumor-associated cell type 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