{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,22]],"date-time":"2026-07-22T20:26:55Z","timestamp":1784752015213,"version":"3.55.0"},"reference-count":56,"publisher":"Oxford University Press (OUP)","issue":"5","license":[{"start":{"date-parts":[[2022,7,29]],"date-time":"2022-07-29T00:00:00Z","timestamp":1659052800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2021YFF1201201"],"award-info":[{"award-number":["2021YFF1201201"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,9,20]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Single-cell RNA-sequencing (scRNA-seq) has been widely used to depict gene expression profiles at the single-cell resolution. However, its relatively high dropout rate often results in artificial zero expressions of genes and therefore compromised reliability of results. To overcome such unwanted sparsity of scRNA-seq data, several imputation algorithms have been developed to recover the single-cell expression profiles. Here, we propose a novel approach, GE-Impute, to impute the dropout zeros in scRNA-seq data with graph embedding-based neural network model. GE-Impute learns the neural graph representation for each cell and reconstructs the cell\u2013cell similarity network accordingly, which enables better imputation of dropout zeros based on the more accurately allocated neighbors in the similarity network. Gene expression correlation analysis between true expression data and simulated dropout data suggests significantly better performance of GE-Impute on recovering dropout zeros for both droplet- and plated-based scRNA-seq data. GE-Impute also outperforms other imputation methods in identifying differentially expressed genes and improving the unsupervised clustering on datasets from various scRNA-seq techniques. Moreover, GE-Impute enhances the identification of marker genes, facilitating the cell type assignment of clusters. In trajectory analysis, GE-Impute improves time-course scRNA-seq data analysis and reconstructing differentiation trajectory. The above results together demonstrate that GE-Impute could be a useful method to recover the single-cell expression profiles, thus enabling better biological interpretation of scRNA-seq data. GE-Impute is implemented in Python and is freely available at https:\/\/github.com\/wxbCaterpillar\/GE-Impute.<\/jats:p>","DOI":"10.1093\/bib\/bbac313","type":"journal-article","created":{"date-parts":[[2022,7,28]],"date-time":"2022-07-28T20:20:39Z","timestamp":1659039639000},"source":"Crossref","is-referenced-by-count":31,"title":["GE-Impute: graph embedding-based imputation for single-cell RNA-seq data"],"prefix":"10.1093","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1341-411X","authenticated-orcid":false,"given":"Xiaobin","family":"Wu","sequence":"first","affiliation":[{"name":"Department of Biomedical Informatics, Center for Noncoding RNA Medicine, School of Basic Medical Sciences, Peking University , Beijing, China"},{"name":"MOE Key Laboratory of Molecular Cardiovascular Sciences, Peking University , Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuan","family":"Zhou","sequence":"additional","affiliation":[{"name":"Department of Biomedical Informatics, Center for Noncoding RNA Medicine, School of Basic Medical Sciences, Peking University , Beijing, China"},{"name":"MOE Key Laboratory of Molecular Cardiovascular Sciences, Peking University , Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2022,7,29]]},"reference":[{"issue":"5","key":"2022092013222739400_ref1","doi-asserted-by":"crossref","first-page":"377","DOI":"10.1038\/nmeth.1315","article-title":"mRNA-Seq whole-transcriptome analysis of a single cell","volume":"6","author":"Tang","year":"2009","journal-title":"Nat Methods"},{"issue":"5","key":"2022092013222739400_ref2","doi-asserted-by":"crossref","first-page":"1232","DOI":"10.1016\/j.cell.2020.07.017","article-title":"Therapy-induced evolution of human lung cancer revealed by single-cell RNA 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