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However, the highly different features on sparsity, heterogeneity and dimensionality between multi-omics data have severely hindered its integrative analysis.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>We proposed deep cross-omics cycle attention (DCCA) model, a computational tool for joint analysis of single-cell multi-omics data, by combining variational autoencoders (VAEs) and attention-transfer. Specifically, we show that DCCA can leverage one omics data to fine-tune the network trained for another omics data, given a dataset of parallel multi-omics data within the same cell. Studies on both simulated and real datasets from various platforms, DCCA demonstrates its superior capability: (i) dissecting cellular heterogeneity; (ii) denoising and aggregating data and (iii) constructing the link between multi-omics data, which is used to infer new transcriptional regulatory relations. In our applications, DCCA was demonstrated to have a superior power to generate missing stages or omics in a biologically meaningful manner, which provides a new way to analyze and also understand complicated biological processes.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>DCCA source code is available at https:\/\/github.com\/cmzuo11\/DCCA, and has been deposited in archived format at https:\/\/doi.org\/10.5281\/zenodo.4762065.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Supplementary information<\/jats:title>\n                  <jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btab403","type":"journal-article","created":{"date-parts":[[2021,5,22]],"date-time":"2021-05-22T11:09:29Z","timestamp":1621681769000},"page":"4091-4099","source":"Crossref","is-referenced-by-count":77,"title":["Deep cross-omics cycle attention model for joint analysis of single-cell multi-omics data"],"prefix":"10.1093","volume":"37","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5887-2926","authenticated-orcid":false,"given":"Chunman","family":"Zuo","sequence":"first","affiliation":[{"name":"State Key Laboratory of Cell Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences , Shanghai 200031, China"}]},{"given":"Hao","family":"Dai","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Cell Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences , Shanghai 200031, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3960-0068","authenticated-orcid":false,"given":"Luonan","family":"Chen","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Cell Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences , Shanghai 200031, China"},{"name":"Key Laboratory of Systems Health Science of Zhejiang Province, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Chinese Academy of Sciences , Hangzhou 310024, China"},{"name":"School of Life Science and Technology, ShanghaiTech University , Shanghai 201210, China"},{"name":"Pazhou Lab , Guangzhou 510330, China"}]}],"member":"286","published-online":{"date-parts":[[2021,5,24]]},"reference":[{"key":"2023051607092799300_btab403-B2","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1186\/s13059-020-02015-1","article-title":"MOFA plus: a statistical framework for comprehensive integration of multi-modal single-cell data","volume":"21","author":"Argelaguet","year":"2020","journal-title":"Genome Biol"},{"key":"2023051607092799300_btab403-B3","doi-asserted-by":"crossref","first-page":"e10","DOI":"10.1093\/nar\/gky950","article-title":"Classifying cells with Scasat, a single-cell ATAC-seq analysis tool","volume":"47","author":"Baker","year":"2019","journal-title":"Nucleic Acids Res"},{"key":"2023051607092799300_btab403-B4","doi-asserted-by":"crossref","first-page":"423","DOI":"10.1109\/TPAMI.2018.2798607","article-title":"Multimodal machine learning: a survey and taxonomy","volume":"41","author":"Baltrusaitis","year":"2019","journal-title":"IEEE Trans. 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