{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T18:03:24Z","timestamp":1783015404488,"version":"3.54.6"},"reference-count":47,"publisher":"Oxford University Press (OUP)","issue":"5","license":[{"start":{"date-parts":[[2024,9,18]],"date-time":"2024-09-18T00:00:00Z","timestamp":1726617600000},"content-version":"vor","delay-in-days":55,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Science and Technology Innovation Key R&D Program of Chongqing","award":["CSTB2024TIAD-STX0003"],"award-info":[{"award-number":["CSTB2024TIAD-STX0003"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,7,25]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Single-cell multi-omics integration enables joint analysis at the single-cell level of resolution to provide more accurate understanding of complex biological systems, while spatial multi-omics integration is benefit to the exploration of cell spatial heterogeneity to facilitate more comprehensive downstream analyses. Existing methods are mainly designed for single-cell multi-omics data with little consideration of spatial information and still have room for performance improvement. A reliable multi-omics integration method designed for both single-cell and spatially resolved data is necessary and significant. We propose a multi-omics integration method based on dual-path graph attention auto-encoder (SSGATE). It can construct the neighborhood graphs based on single-cell expression profiles or spatial coordinates, enabling it to process single-cell data and utilize spatial information from spatially resolved data. It can also perform self-supervised learning for integration through the graph attention auto-encoders from two paths. SSGATE is applied to integration of transcriptomics and proteomics, including single-cell and spatially resolved data of various tissues from different sequencing technologies. SSGATE shows better performance and stronger robustness than competitive methods and facilitates downstream analysis.<\/jats:p>","DOI":"10.1093\/bib\/bbae450","type":"journal-article","created":{"date-parts":[[2024,8,30]],"date-time":"2024-08-30T08:38:38Z","timestamp":1725007118000},"source":"Crossref","is-referenced-by-count":29,"title":["Multi-omics integration for both single-cell and spatially resolved data based on dual-path graph attention auto-encoder"],"prefix":"10.1093","volume":"25","author":[{"given":"Tongxuan","family":"Lv","sequence":"first","affiliation":[{"name":"BGI Research , No. 9, Yunhua Road, Yantian District, Shenzhen 518083, China"},{"name":"College of Life Sciences, University of Chinese Academy of Sciences , No. 19, Yuquan Road, Shijingshan District, Beijing 100049, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yong","family":"Zhang","sequence":"additional","affiliation":[{"name":"BGI Research , No. 9, Yunhua Road, Yantian District, 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