{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T14:10:43Z","timestamp":1774447843776,"version":"3.50.1"},"reference-count":18,"publisher":"Oxford University Press (OUP)","issue":"23","license":[{"start":{"date-parts":[[2022,10,17]],"date-time":"2022-10-17T00:00:00Z","timestamp":1665964800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/pages\/standard-publication-reuse-rights"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R01-131399"],"award-info":[{"award-number":["R01-131399"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["U54-AG075931"],"award-info":[{"award-number":["U54-AG075931"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R35-GM126985"],"award-info":[{"award-number":["R35-GM126985"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["NSF1945971"],"award-info":[{"award-number":["NSF1945971"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,11,30]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>Gene expression imputation has been an essential step of the single-cell RNA-Seq data analysis workflow. Among several deep-learning methods, the debut of scGNN gained substantial recognition in 2021 for its superior performance and the ability to produce a cell\u2013cell graph. However, the implementation of scGNN was relatively time-consuming and its performance could still be optimized.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>The implementation of scGNN 2.0 is significantly faster than scGNN thanks to a simplified close-loop architecture. For all eight datasets, cell clustering performance was increased by 85.02% on average in terms of adjusted rand index, and the imputation Median L1 Error was reduced by 67.94% on average. With the built-in visualizations, users can quickly assess the imputation and cell clustering results, compare against benchmarks and interpret the cell\u2013cell interaction. The expanded input and output formats also pave the way for custom workflows that integrate scGNN 2.0 with other scRNA-Seq toolkits on both Python and R platforms.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>scGNN 2.0 is implemented in Python (as of version 3.8) with the source code available at https:\/\/github.com\/OSU-BMBL\/scGNN2.0.<\/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\/btac684","type":"journal-article","created":{"date-parts":[[2022,10,14]],"date-time":"2022-10-14T22:23:18Z","timestamp":1665786198000},"page":"5322-5325","source":"Crossref","is-referenced-by-count":26,"title":["scGNN 2.0: a graph neural network tool for imputation and clustering of single-cell RNA-Seq data"],"prefix":"10.1093","volume":"38","author":[{"given":"Haocheng","family":"Gu","sequence":"first","affiliation":[{"name":"Department of Biomedical Informatics, College of Medicine, The Ohio State University , Columbus, OH 43210, USA"}]},{"given":"Hao","family":"Cheng","sequence":"additional","affiliation":[{"name":"Department of Biomedical Informatics, College of Medicine, The Ohio State University , Columbus, OH 43210, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6269-398X","authenticated-orcid":false,"given":"Anjun","family":"Ma","sequence":"additional","affiliation":[{"name":"Department of Biomedical Informatics, College of Medicine, The Ohio State University , Columbus, OH 43210, USA"},{"name":"Pelotonia Institute for Immuno-Oncology, The James Comprehensive Cancer Center, The Ohio State University , Columbus, OH 43210, USA"}]},{"given":"Yang","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Biomedical Informatics, College of Medicine, The Ohio State University , Columbus, OH 43210, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2260-4310","authenticated-orcid":false,"given":"Juexin","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering and Computer Science, and Christopher S. 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