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However, data analysis remains challenging due to the discrete nature and high noise level of the data, as well as the lack of modality. Here, we propose scMultiNet, a multi-task deep adversarial neural network that can integrate different tasks to analyze single-cell multi-modal data. In particular, we achieve joint training of multi-modal integration and cross-modal prediction tasks by introducing a cross-modal bi-prediction module and a multi-head self-attention module. Data denoising is further enhanced by integrating an indicator matrix that constrains and precisely reconstructs the original expression values. Extensive simulations and real data experiments demonstrate that scMultiNet outperforms existing state-of-the-art methods in dimensionality reduction, visualization, clustering, batch elimination, data denoising, multi-modal integration, single-cell cross-modality translation, and in revealing cell type\u2013specific biological insights. In addition, we demonstrate that scMultiNet can effectively transfer the complex relationships between modalities from one batch to another. In summary, scMultiNet stands as a comprehensive end-to-end framework, ideally suited for analyzing single-cell multi-omics data.<\/jats:p>","DOI":"10.1093\/bib\/bbag016","type":"journal-article","created":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T12:52:10Z","timestamp":1773233530000},"source":"Crossref","is-referenced-by-count":0,"title":["A deep adversarial network model for multi-task analysis of single-cell omics data"],"prefix":"10.1093","volume":"27","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2966-9130","authenticated-orcid":false,"given":"Junlin","family":"Xu","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, Wuhan University of Science and Technology , Wuhan, Hubei 430065 ,","place":["China"]}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-6749-6492","authenticated-orcid":false,"given":"Cheng","family":"Guo","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Wuhan University of Science and Technology , Wuhan, Hubei 430065 ,","place":["China"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2384-1158","authenticated-orcid":false,"given":"Yajie","family":"Meng","sequence":"additional","affiliation":[{"name":"School of Computer Science and Artificial Intelligence, Wuhan Textile University , Wuhan 430200 ,","place":["China"]}]},{"given":"Shuting","family":"Jin","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Wuhan University of Science and Technology , Wuhan, Hubei 430065 ,","place":["China"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9263-8463","authenticated-orcid":false,"given":"Changcheng","family":"Lu","sequence":"additional","affiliation":[{"name":"College of Computer Science and Electronic Engineering, Hunan University , Changsha, Hunan 410082 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and Frontier Sciences, University of Electronic Science and Technology of China , Chengdu 611731 ,","place":["China"]}]},{"given":"Tian","family":"Tian","sequence":"additional","affiliation":[{"name":"School of Computer Science, National Engineering Research Center for Multimedia Software, Institute of Artificial Intelligence, and Hubei Key Laboratory of Multimedia and Network Communication Engineering, Wuhan University , Wuhan, Hubei 430072 ,","place":["China"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1081-7658","authenticated-orcid":false,"given":"Xiangxiang","family":"Zeng","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Chemo and Biosensing, College of Computer Science and Electronic Engineering, Hunan University , Changsha 410082 ,","place":["China"]},{"name":"The Ministry of Education Key Laboratory of Fusion Computing of Supercomputing and Artificial Intelligence, Hunan University , Changsha 410082 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