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Traditional methods usually cluster the cells and manually identify cell clusters through marker genes, which is time-consuming and subjective. With the launch of several large-scale single-cell projects, millions of sequenced cells have been annotated and it is promising to transfer labels from the annotated datasets to newly generated datasets. One powerful way for the transferring is to learn cell relations through the graph neural network (GNN), but traditional GNNs are difficult to process millions of cells due to the expensive costs of the message-passing procedure at each training epoch. Here, we have developed a robust and scalable GNN-based method for accurate single-cell classification (GraphCS), where the graph is constructed to connect similar cells within and between labelled and unlabeled scRNA-seq datasets for propagation of shared information. To overcome the slow information propagation of GNN at each training epoch, the diffused information is pre-calculated via the approximate Generalized PageRank algorithm, enabling sublinear complexity over cell numbers. Compared with existing methods, GraphCS demonstrates better performance on simulated, cross-platform, cross-species and cross-omics scRNA-seq datasets. More importantly, our model provides a high speed and scalability on large datasets, and can achieve superior performance for 1 million cells within 50\u00a0min.<\/jats:p>","DOI":"10.1093\/bib\/bbab570","type":"journal-article","created":{"date-parts":[[2021,12,13]],"date-time":"2021-12-13T07:07:56Z","timestamp":1639379276000},"source":"Crossref","is-referenced-by-count":37,"title":["A robust and scalable graph neural network for accurate single-cell classification"],"prefix":"10.1093","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3145-430X","authenticated-orcid":false,"given":"Yuansong","family":"Zeng","sequence":"first","affiliation":[{"name":"School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou 510000, China"}]},{"given":"Zhuoyi","family":"Wei","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou 510000, China"}]},{"given":"Zixiang","family":"Pan","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou 510000, China"}]},{"given":"Yutong","family":"Lu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou 510000, China"}]},{"given":"Yuedong","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou 510000, China"},{"name":"Key Laboratory of Machine Intelligence and Advanced Computing (MOE), Guangzhou 510000, China"}]}],"member":"286","published-online":{"date-parts":[[2022,1,9]]},"reference":[{"issue":"4","key":"2022031506244654000_ref1","doi-asserted-by":"crossref","first-page":"346","DOI":"10.1016\/j.cels.2016.08.011","article-title":"A single-cell transcriptomic map of the human and mouse pancreas reveals inter-and intra-cell population structure","volume":"3","author":"Baron","year":"2016","journal-title":"Cell Syst"},{"issue":"7","key":"2022031506244654000_ref2","doi-asserted-by":"crossref","first-page":"1611","DOI":"10.1016\/j.cell.2017.10.044","article-title":"Single-cell transcriptomic analysis of primary and metastatic tumor ecosystems in head and neck cancer","volume":"171","author":"Puram","year":"2017","journal-title":"Cell"},{"issue":"1","key":"2022031506244654000_ref3","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41467-017-02305-6","article-title":"Single-cell RNA-sequencing uncovers transcriptional states and fate decisions in haematopoiesis","volume":"8","author":"Athanasiadis","year":"2017","journal-title":"Nat Commun"},{"issue":"5","key":"2022031506244654000_ref4","doi-asserted-by":"crossref","first-page":"1293","DOI":"10.1016\/j.cell.2018.05.060","article-title":"Single-cell map of diverse immune phenotypes in the breast tumor microenvironment","volume":"174","author":"Azizi","year":"2018","journal-title":"Cell"},{"issue":"5","key":"2022031506244654000_ref5","doi-asserted-by":"crossref","first-page":"1309","DOI":"10.1016\/j.cell.2018.06.052","article-title":"A single-cell atlas of in vivo mammalian chromatin accessibility","volume":"174","author":"Cusanovich","year":"2018","journal-title":"Cell"},{"issue":"4","key":"2022031506244654000_ref6","doi-asserted-by":"crossref","first-page":"385","DOI":"10.1016\/j.cels.2016.09.002","article-title":"A single-cell transcriptome atlas of the human pancreas","volume":"3","author":"Muraro","year":"2016","journal-title":"Cell Syst"},{"issue":"7727","key":"2022031506244654000_ref7","doi-asserted-by":"crossref","first-page":"367","DOI":"10.1038\/s41586-018-0590-4","article-title":"Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris","volume":"562","author":"T. 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