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Single-cell RNA sequencing (scRNA-seq) is one of the most common single cell technologies, which enables probing transcriptional states in thousands of cells in one experiment. Identification of cell types from scRNA-seq measurements is a fundamental and crucial question to answer. Most previous studies directly take gene expression as input while ignoring the comprehensive gene\u2013gene interactions.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>We propose scGraph, an automatic cell identification algorithm leveraging gene interaction relationships to enhance the performance of the cell-type identification. scGraph is based on a graph neural network to aggregate the information of interacting genes. In a series of experiments, we demonstrate that scGraph is accurate and outperforms eight comparison methods in the task of cell-type identification. Moreover, scGraph automatically learns the gene interaction relationships from biological data and the pathway enrichment analysis shows consistent findings with previous analysis, providing insights on the analysis of regulatory mechanism.<\/jats:p><\/jats:sec><jats:sec><jats:title>Availability and implementation<\/jats:title><jats:p>scGraph is freely available at https:\/\/github.com\/QijinYin\/scGraph and https:\/\/figshare.com\/articles\/software\/scGraph\/17157743.<\/jats:p><\/jats:sec><jats:sec><jats:title>Supplementary information<\/jats:title><jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p><\/jats:sec>","DOI":"10.1093\/bioinformatics\/btac199","type":"journal-article","created":{"date-parts":[[2022,4,8]],"date-time":"2022-04-08T12:42:18Z","timestamp":1649421738000},"page":"2996-3003","source":"Crossref","is-referenced-by-count":27,"title":["scGraph: a graph neural network-based approach to automatically identify cell 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China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9684-5643","authenticated-orcid":false,"given":"Xuegong","family":"Zhang","sequence":"additional","affiliation":[{"name":"Ministry of Education Key Laboratory of Bioinformatics, Research Department of Bioinformatics at the Beijing National Research Center for Information Science and Technology, Center for Synthetic and Systems Biology, Department of Automation, Tsinghua University , Beijing 100084, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7533-3753","authenticated-orcid":false,"given":"Rui","family":"Jiang","sequence":"additional","affiliation":[{"name":"Ministry of Education Key Laboratory of Bioinformatics, Research Department of Bioinformatics at the Beijing National Research Center for Information Science and Technology, Center for Synthetic and Systems Biology, Department of Automation, Tsinghua University , Beijing 100084, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hairong","family":"Lv","sequence":"additional","affiliation":[{"name":"Ministry of Education Key Laboratory of Bioinformatics, Research Department of Bioinformatics at the Beijing National Research Center for Information Science and Technology, Center for Synthetic and Systems Biology, Department of Automation, Tsinghua University , Beijing 100084, China"},{"name":"Fuzhou Institute of Data Technology , Fuzhou 350200, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2022,4,8]]},"reference":[{"key":"2023041402572286100_","doi-asserted-by":"crossref","first-page":"194","DOI":"10.1186\/s13059-019-1795-z","article-title":"A comparison of automatic cell identification methods for single-cell RNA sequencing data","volume":"20","author":"Abdelaal","year":"2019","journal-title":"Genome 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