{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,17]],"date-time":"2026-07-17T02:22:40Z","timestamp":1784254960262,"version":"3.55.0"},"reference-count":47,"publisher":"Oxford University Press (OUP)","issue":"6","license":[{"start":{"date-parts":[[2024,10,2]],"date-time":"2024-10-02T00:00:00Z","timestamp":1727827200000},"content-version":"vor","delay-in-days":9,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82403430"],"award-info":[{"award-number":["82403430"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Technology Program Joint Fund of Liaoning Province","award":["2023-BSBA-207"],"award-info":[{"award-number":["2023-BSBA-207"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,9,23]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Single-cell cross-modal joint clustering has been extensively utilized to investigate the tumor microenvironment. Although numerous approaches have been suggested, accurate clustering remains the main challenge. First, the gene expression matrix frequently contains numerous missing values due to measurement limitations. The majority of existing clustering methods treat it as a typical multi-modal dataset without further processing. Few methods conduct recovery before clustering and do not sufficiently engage with the underlying research, leading to suboptimal outcomes. Additionally, the existing cross-modal information fusion strategy does not ensure consistency of representations across different modes, potentially leading to the integration of conflicting information, which could degrade performance. To address these challenges, we propose the \u2019Recover then Aggregate\u2019 strategy and introduce the Unified Cross-Modal Deep Clustering model. Specifically, we have developed a data augmentation technique based on neighborhood similarity, iteratively imposing rank constraints on the Laplacian matrix, thus updating the similarity matrix and recovering dropout events. Concurrently, we integrate cross-modal features and employ contrastive learning to align modality-specific representations with consistent ones, enhancing the effective integration of diverse modal information. Comprehensive experiments on five real-world multi-modal datasets have demonstrated this method\u2019s superior effectiveness in single-cell clustering tasks.<\/jats:p>","DOI":"10.1093\/bib\/bbae485","type":"journal-article","created":{"date-parts":[[2024,10,2]],"date-time":"2024-10-02T13:10:16Z","timestamp":1727874616000},"source":"Crossref","is-referenced-by-count":4,"title":["Recover then aggregate: unified cross-modal deep clustering with global structural information for single-cell data"],"prefix":"10.1093","volume":"25","author":[{"given":"Ziyi","family":"Wang","sequence":"first","affiliation":[{"name":"Department of Surgical Oncology and General Surgery, First Hospital of China Medical University , Shenyang 110001 ,","place":["PR China"]},{"name":"Section of Esophageal and Mediastinal Oncology , Department of Thoracic Surgery, National Cancer Center\/National Clinical Research Center for Cancer\/Cancer Hospital, , Beijing 100730 ,","place":["China"]},{"name":"Chinese Academy of Medical Sciences and Peking Union Medical College , Department of Thoracic Surgery, National Cancer Center\/National Clinical Research Center for Cancer\/Cancer Hospital, , Beijing 100730 ,","place":["China"]},{"name":"Department of Thoracic Surgery, The First Hospital of China Medical University , No.155 North Nanjing Street, Shenyang 110001 ,","place":["People\u2019s Republic of China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peng","family":"Luo","sequence":"additional","affiliation":[{"name":"Department of Thoracic Surgery , Xinqiao Hospital, , Chongqing 400038 ,","place":["China"]},{"name":"Army Medical University , Xinqiao Hospital, , Chongqing 400038 ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mingming","family":"Xiao","sequence":"additional","affiliation":[{"name":"Department of Pathology, People\u2019s Hospital of China Medical University (Liaoning Provincial People\u2019s Hospital) , Shenyang, Liaoning Province 110015 ,","place":["People\u2019s Republic of China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Boyang","family":"Wang","sequence":"additional","affiliation":[{"name":"Electrical and Computer Engineering, University of Illinois at Chicago , Chicago, IL 60607 ,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tianyu","family":"Liu","sequence":"additional","affiliation":[{"name":"Computer Science and Engineering, University of California , Riverside, Riverside, CA 92521 ,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiangyu","family":"Sun","sequence":"additional","affiliation":[{"name":"Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute , Shenyang 110042, Liaoning ,","place":["China"]},{"name":"Cancer Hospital of Dalian University of Technology , Shenyang, Liaoning Province 110042 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