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In this study, we propose a universal framework for the integration of single-cell multi-omics data based on graph convolutional network (GCN-SC). Among the multiple single-cell data, GCN-SC usually selects one data with the largest number of cells as the reference and the rest as the query dataset. It utilizes mutual nearest neighbor algorithm to identify cell-pairs, which provide connections between cells both within and across the reference and query datasets. A GCN algorithm further takes the mixed graph constructed from these cell-pairs to adjust count matrices from the query datasets. Finally, dimension reduction is performed by using non-negative matrix factorization before visualization. By applying GCN-SC on six datasets, we show that GCN-SC can effectively integrate sequencing data from multiple single-cell sequencing technologies, species or different omics, which outperforms the state-of-the-art methods, including Seurat, LIGER, GLUER and Pamona.<\/jats:p>","DOI":"10.1093\/bib\/bbad081","type":"journal-article","created":{"date-parts":[[2023,3,17]],"date-time":"2023-03-17T13:27:47Z","timestamp":1679059667000},"source":"Crossref","is-referenced-by-count":34,"title":["A universal framework for single-cell multi-omics data integration with graph convolutional networks"],"prefix":"10.1093","volume":"24","author":[{"given":"Hongli","family":"Gao","sequence":"first","affiliation":[{"name":"Qingdao University of Science and Technology , China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8835-8370","authenticated-orcid":false,"given":"Bin","family":"Zhang","sequence":"additional","affiliation":[{"name":"Computational 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