{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,27]],"date-time":"2026-05-27T12:47:50Z","timestamp":1779886070941,"version":"3.53.1"},"reference-count":49,"publisher":"Oxford University Press (OUP)","issue":"2","license":[{"start":{"date-parts":[[2023,2,13]],"date-time":"2023-02-13T00:00:00Z","timestamp":1676246400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2020YFB0204803"],"award-info":[{"award-number":["2020YFB0204803"]}],"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":["12126610"],"award-info":[{"award-number":["12126610"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62201150"],"award-info":[{"award-number":["62201150"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Guangdong Key Field R&D Plan","award":["2019B020228001"],"award-info":[{"award-number":["2019B020228001"]}]},{"name":"Guangdong Key Field R&D Plan","award":["2018B010109006"],"award-info":[{"award-number":["2018B010109006"]}]},{"name":"Introducing Innovative and Entrepreneurial Teams","award":["2016ZT06D211"],"award-info":[{"award-number":["2016ZT06D211"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,3,19]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Recent advances in spatial transcriptomics have enabled measurements of gene expression at cell\/spot resolution meanwhile retaining both the spatial information and the histology images of the tissues. Accurately identifying the spatial domains of spots is a vital step for various downstream tasks in spatial transcriptomics analysis. To remove noises in gene expression, several methods have been developed to combine histopathological images for data analysis of spatial transcriptomics. However, these methods either use the image only for the spatial relations for spots, or individually learn the embeddings of the gene expression and image without fully coupling the information. Here, we propose a novel method ConGI to accurately exploit spatial domains by adapting gene expression with histopathological images through contrastive learning. Specifically, we designed three contrastive loss functions within and between two modalities (the gene expression and image data) to learn the common representations. The learned representations are then used to cluster the spatial domains on both tumor and normal spatial transcriptomics datasets. ConGI was shown to outperform existing methods for the spatial domain identification. In addition, the learned representations have also been shown powerful for various downstream tasks, including trajectory inference, clustering, and visualization.<\/jats:p>","DOI":"10.1093\/bib\/bbad048","type":"journal-article","created":{"date-parts":[[2023,2,14]],"date-time":"2023-02-14T01:42:07Z","timestamp":1676338927000},"source":"Crossref","is-referenced-by-count":59,"title":["Identifying spatial domain by adapting transcriptomics with histology through contrastive learning"],"prefix":"10.1093","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3145-430X","authenticated-orcid":false,"given":"Yuansong","family":"Zeng","sequence":"first","affiliation":[{"name":"School of Computer Science and Engineering, Sun Yat-sen University , Guangzhou 510000 , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rui","family":"Yin","sequence":"additional","affiliation":[{"name":"Key Laboratory of Machine Intelligence and Advanced Computing (MOE) , Guangzhou 510000 , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mai","family":"Luo","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Sun Yat-sen University , Guangzhou 510000 , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianing","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Sun Yat-sen University , Guangzhou 510000 , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zixiang","family":"Pan","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Sun Yat-sen University , Guangzhou 510000 , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yutong","family":"Lu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Sun Yat-sen University , Guangzhou 510000 , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weijiang","family":"Yu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Sun Yat-sen University , Guangzhou 510000 , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuedong","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Sun Yat-sen University , Guangzhou 510000 , China"},{"name":"Department of Computer Science, The University of Hong Kong , Hong Kong SAR , China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2023,2,13]]},"reference":[{"issue":"5","key":"2023032004455738200_","doi-asserted-by":"crossref","first-page":"534","DOI":"10.1038\/s41592-022-01409-2","article-title":"Museum of spatial transcriptomics","volume":"19","author":"Moses","year":"2022","journal-title":"Nat 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