{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T22:13:46Z","timestamp":1782944026990,"version":"3.54.5"},"reference-count":42,"publisher":"Oxford University Press (OUP)","issue":"6","license":[{"start":{"date-parts":[[2023,9,22]],"date-time":"2023-09-22T00:00:00Z","timestamp":1695340800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/pages\/standard-publication-reuse-rights"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62102319"],"award-info":[{"award-number":["62102319"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["2022M712595"],"award-info":[{"award-number":["2022M712595"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["G2021KY05112"],"award-info":[{"award-number":["G2021KY05112"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Young Elite Scientists Sponsorship Program","award":["095920221376"],"award-info":[{"award-number":["095920221376"]}]},{"DOI":"10.13039\/100000049","name":"National Institute on Aging","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000049","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["P01 AG03949"],"award-info":[{"award-number":["P01 AG03949"]}],"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":["R21 AG056920"],"award-info":[{"award-number":["R21 AG056920"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,9,22]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>The emergence of single-cell RNA sequencing (scRNA-seq) technology has revolutionized the identification of cell types and the study of cellular states at a single-cell level. Despite its significant potential, scRNA-seq data analysis is plagued by the issue of missing values. Many existing imputation methods rely on simplistic data distribution assumptions while ignoring the intrinsic gene expression distribution specific to cells. This work presents a novel deep-learning model, named scMultiGAN, for scRNA-seq imputation, which utilizes multiple collaborative generative adversarial networks (GAN). Unlike traditional GAN-based imputation methods that generate missing values based on random noises, scMultiGAN employs a two-stage training process and utilizes multiple GANs to achieve cell-specific imputation. Experimental results show the efficacy of scMultiGAN in imputation accuracy, cell clustering, differential gene expression analysis and trajectory analysis, significantly outperforming existing state-of-the-art techniques. Additionally, scMultiGAN is scalable to large scRNA-seq datasets and consistently performs well across sequencing platforms. The scMultiGAN code is freely available at https:\/\/github.com\/Galaxy8172\/scMultiGAN.<\/jats:p>","DOI":"10.1093\/bib\/bbad384","type":"journal-article","created":{"date-parts":[[2023,10,30]],"date-time":"2023-10-30T23:28:05Z","timestamp":1698708485000},"source":"Crossref","is-referenced-by-count":31,"title":["scMultiGAN: cell-specific imputation for single-cell transcriptomes with multiple deep generative adversarial networks"],"prefix":"10.1093","volume":"24","author":[{"given":"Tao","family":"Wang","sequence":"first","affiliation":[{"name":"School of Computer Science, Northwestern Polytechnical University , 1 Dongxiang Rd., 710072 Xi\u2019an , China"},{"name":"Key Laboratory of Big Data Storage and Management , Ministry of Industry and Information Technology, , 1 Dongxiang Rd., 710072 Xi\u2019an , China"},{"name":"Northwestern Polytechnical University , Ministry of Industry and Information Technology, , 1 Dongxiang Rd., 710072 Xi\u2019an , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hui","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Automation, Northwestern Polytechnical University , 1 Dongxiang Rd., 710072 Xi\u2019an , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yungang","family":"Xu","sequence":"additional","affiliation":[{"name":"Department of Cell Biology and Genetics , School of Basic Medical Sciences, Xi\u2019an , No.28, West Xianning Road, 710061 Xi\u2019an , China"},{"name":"Jiaotong University Health Science Center , School of Basic Medical Sciences, Xi\u2019an , No.28, West Xianning Road, 710061 Xi\u2019an , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yongtian","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer Science, Northwestern Polytechnical University , 1 Dongxiang Rd., 710072 Xi\u2019an , China"},{"name":"Key Laboratory of Big Data Storage and Management , Ministry of Industry and Information Technology, , 1 Dongxiang Rd., 710072 Xi\u2019an , China"},{"name":"Northwestern Polytechnical University , Ministry of Industry and Information Technology, , 1 Dongxiang Rd., 710072 Xi\u2019an , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xuequn","family":"Shang","sequence":"additional","affiliation":[{"name":"School of Computer Science, Northwestern Polytechnical University , 1 Dongxiang Rd., 710072 Xi\u2019an , China"},{"name":"Key Laboratory of Big Data Storage and Management , Ministry of Industry and Information Technology, , 1 Dongxiang Rd., 710072 Xi\u2019an , China"},{"name":"Northwestern Polytechnical University , Ministry of Industry and Information Technology, , 1 Dongxiang Rd., 710072 Xi\u2019an , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiajie","family":"Peng","sequence":"additional","affiliation":[{"name":"School of Computer Science, Northwestern Polytechnical University , 1 Dongxiang Rd., 710072 Xi\u2019an , China"},{"name":"Key Laboratory of Big Data Storage and Management , Ministry of Industry and Information Technology, , 1 Dongxiang Rd., 710072 Xi\u2019an , China"},{"name":"Northwestern Polytechnical University , Ministry of Industry and Information Technology, , 1 Dongxiang Rd., 710072 Xi\u2019an , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bing","family":"Xiao","sequence":"additional","affiliation":[{"name":"School of Automation, Northwestern Polytechnical University , 1 Dongxiang Rd., 710072 Xi\u2019an , China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2023,10,30]]},"reference":[{"issue":"5","key":"2024041009290541000_ref1","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1038\/s41576-018-0088-9","article-title":"Challenges in unsupervised clustering of single-cell RNA-seq data","volume":"20","author":"Kiselev","year":"2019","journal-title":"Nat Rev Genet"},{"issue":"6226","key":"2024041009290541000_ref2","doi-asserted-by":"crossref","first-page":"1138","DOI":"10.1126\/science.aaa1934","article-title":"Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq","volume":"347","author":"Zeisel","year":"2015","journal-title":"Science"},{"issue":"7568","key":"2024041009290541000_ref3","doi-asserted-by":"crossref","first-page":"251","DOI":"10.1038\/nature14966","article-title":"Single-cell messenger RNA sequencing reveals rare intestinal cell types","volume":"525","author":"Gr\u00fcn","year":"2015","journal-title":"Nature"},{"issue":"6335","key":"2024041009290541000_ref4","doi-asserted-by":"crossref","first-page":"eaah4573","DOI":"10.1126\/science.aah4573","article-title":"Single-cell RNA-seq reveals new types of human blood dendritic cells, monocytes, and progenitors","volume":"356","author":"Villani","year":"2017","journal-title":"Science"},{"issue":"9","key":"2024041009290541000_ref5","doi-asserted-by":"crossref","first-page":"1131","DOI":"10.1038\/nsmb.2660","article-title":"Single-cell RNA-seq profiling of human preimplantation embryos and embryonic stem cells","volume":"20","author":"Yan","year":"2013","journal-title":"Nat Struct Mol Biol"},{"issue":"6190","key":"2024041009290541000_ref6","doi-asserted-by":"crossref","first-page":"1396","DOI":"10.1126\/science.1254257","article-title":"Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma","volume":"344","author":"Patel","year":"2014","journal-title":"Science"},{"issue":"9","key":"2024041009290541000_ref7","doi-asserted-by":"crossref","first-page":"1334","DOI":"10.1038\/s41588-021-00911-1","article-title":"A single-cell and spatially resolved atlas of human breast cancers","volume":"53","author":"Wu","year":"2021","journal-title":"Nat Genet"},{"issue":"11","key":"2024041009290541000_ref8","doi-asserted-by":"crossref","first-page":"1798","DOI":"10.1038\/s12276-020-00528-0","article-title":"Single-cell network biology for resolving cellular heterogeneity in human diseases","volume":"52","author":"Cha","year":"2020","journal-title":"Exp Mol Med"},{"key":"2024041009290541000_ref9","doi-asserted-by":"crossref","first-page":"150","DOI":"10.1016\/j.csbj.2022.11.055","article-title":"scDrug: from single-cell RNA-seq to drug response prediction","volume":"21","author":"Hsieh","year":"2023","journal-title":"Comput Struct Biotechnol J"},{"issue":"3","key":"2024041009290541000_ref10","doi-asserted-by":"crossref","first-page":"e694","DOI":"10.1002\/ctm2.694","article-title":"Single-cell RNA sequencing technologies and applications: a brief overview","volume":"12","author":"Jovic","year":"2022","journal-title":"Clin Transl Med"},{"issue":"3","key":"2024041009290541000_ref11","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1038\/nrg3833","article-title":"Computational and analytical challenges in single-cell transcriptomics","volume":"16","author":"Stegle","year":"2015","journal-title":"Nat Rev Genet"},{"issue":"4","key":"2024041009290541000_ref12","doi-asserted-by":"crossref","first-page":"461","DOI":"10.1093\/bioinformatics\/bts714","article-title":"Data exploration, quality control and testing in single-cell QPCR-based gene expression experiments","volume":"29","author":"McDavid","year":"2013","journal-title":"Bioinformatics"},{"issue":"7","key":"2024041009290541000_ref13","doi-asserted-by":"crossref","first-page":"740","DOI":"10.1038\/nmeth.2967","article-title":"Bayesian approach to single-cell differential expression analysis","volume":"11","author":"Kharchenko","year":"2014","journal-title":"Nat Methods"},{"issue":"4","key":"2024041009290541000_ref14","doi-asserted-by":"crossref","first-page":"562","DOI":"10.1093\/biostatistics\/kxx053","article-title":"Missing data and technical variability in single-cell RNA-sequencing experiments","volume":"19","author":"Hicks","year":"2018","journal-title":"Biostatistics"},{"issue":"1","key":"2024041009290541000_ref15","doi-asserted-by":"crossref","first-page":"1169","DOI":"10.1038\/s41467-020-14976-9","article-title":"Embracing the dropouts in single-cell RNA-seq analysis","volume":"11","author":"Qiu","year":"2020","journal-title":"Nat Commun"},{"issue":"4","key":"2024041009290541000_ref16","doi-asserted-by":"crossref","first-page":"1051","DOI":"10.1111\/biom.13074","article-title":"Detection of differentially expressed genes in discrete single-cell RNA sequencing data using a hurdle model with correlated random effects","volume":"75","author":"Sekula","year":"2019","journal-title":"Biometrics"},{"issue":"11","key":"2024041009290541000_ref17","doi-asserted-by":"crossref","first-page":"1093","DOI":"10.1038\/nmeth.2645","article-title":"Accounting for technical noise in single-cell RNA-seq experiments","volume":"10","author":"Brennecke","year":"2013","journal-title":"Nat Methods"},{"issue":"5","key":"2024041009290541000_ref18","doi-asserted-by":"crossref","first-page":"bbab105","DOI":"10.1093\/bib\/bbab105","article-title":"Critical downstream analysis steps for single-cell RNA sequencing data","volume":"22","author":"Zhang","year":"2021","journal-title":"Brief Bioinform"},{"issue":"3","key":"2024041009290541000_ref19","doi-asserted-by":"crossref","first-page":"716","DOI":"10.1016\/j.cell.2018.05.061","article-title":"Recovering gene interactions from single-cell data using data diffusion","volume":"174","author":"Van Dijk","year":"2018","journal-title":"Cell"},{"key":"2024041009290541000_ref20","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s12859-018-2226-y","article-title":"DrImpute: imputing dropout events in single cell RNA sequencing data","volume":"19","author":"Gong","year":"2018","journal-title":"BMC Bioinform"},{"issue":"1","key":"2024041009290541000_ref21","doi-asserted-by":"crossref","first-page":"997","DOI":"10.1038\/s41467-018-03405-7","article-title":"An accurate and robust imputation method scImpute for single-cell RNA-seq data","volume":"9","author":"Li","year":"2018","journal-title":"Nat Commun"},{"key":"2024041009290541000_ref22","first-page":"655365","article-title":"Accurate denoising of single-cell RNA-seq data using unbiased principal component analysis","author":"Wagner","year":"2019"},{"issue":"1","key":"2024041009290541000_ref23","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41598-018-34688-x","article-title":"AutoImpute: autoencoder based imputation of single-cell RNA-seq data","volume":"8","author":"Talwar","year":"2018","journal-title":"Sci Rep"},{"key":"2024041009290541000_ref24","first-page":"837302","article-title":"scGAIN: single cell RNA-seq data imputation using generative adversarial networks","author":"Gunady","year":"2019"},{"issue":"15","key":"2024041009290541000_ref25","doi-asserted-by":"crossref","first-page":"e85","DOI":"10.1093\/nar\/gkaa506","article-title":"ScIGANs: single-cell RNA-seq imputation using generative adversarial networks","volume":"48","author":"Yungang","year":"2020","journal-title":"Nucleic Acids Res"},{"key":"2024041009290541000_ref26","article-title":"Wasserstein GAN,","volume-title":"Proceedings of the 34th International Conference on Machine Learning","author":"Arjovsky"},{"key":"2024041009290541000_ref27","article-title":"Improved training of Wasserstein GANs","volume":"30","author":"Gulrajani","year":"2017","journal-title":"Adv Neural Inform Process Syst"},{"issue":"5","key":"2024041009290541000_ref28","doi-asserted-by":"crossref","first-page":"483","DOI":"10.1038\/nmeth.4236","article-title":"sc3: consensus clustering of single-cell RNA-seq data","volume":"14","author":"Kiselev","year":"2017","journal-title":"Nat Methods"},{"issue":"1","key":"2024041009290541000_ref29","doi-asserted-by":"crossref","first-page":"174","DOI":"10.1186\/s13059-017-1305-0","article-title":"Splatter: simulation of single-cell RNA sequencing data","volume":"18","author":"Zappia","year":"2017","journal-title":"Genome Biol"},{"issue":"1","key":"2024041009290541000_ref30","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13059-017-1188-0","article-title":"CIDR: ultrafast and accurate clustering through imputation for single-cell RNA-seq data","volume":"18","author":"Lin","year":"2017","journal-title":"Genome Biol"},{"issue":"6","key":"2024041009290541000_ref31","doi-asserted-by":"crossref","first-page":"479","DOI":"10.1038\/s41592-019-0425-8","article-title":"Benchmarking single cell RNA-sequencing analysis pipelines using mixture control experiments","volume":"16","author":"Tian","year":"2019","journal-title":"Nat Methods"},{"issue":"5","key":"2024041009290541000_ref32","doi-asserted-by":"crossref","first-page":"1187","DOI":"10.1016\/j.cell.2015.04.044","article-title":"Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells","volume":"161","author":"Klein","year":"2015","journal-title":"Cell"},{"key":"2024041009290541000_ref33","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13059-016-1033-x","article-title":"Single-cell RNA-seq reveals novel regulators of human embryonic stem cell differentiation to definitive endoderm","volume":"17","author":"Chu","year":"2016","journal-title":"Genome Biol"},{"issue":"1","key":"2024041009290541000_ref34","doi-asserted-by":"crossref","first-page":"15081","DOI":"10.1038\/ncomms15081","article-title":"Single-cell RNA-seq enables comprehensive tumour and immune cell profiling in primary breast cancer","volume":"8","author":"Chung","year":"2017","journal-title":"Nat Commun"},{"issue":"6282","key":"2024041009290541000_ref35","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1126\/science.aad0501","article-title":"Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq","volume":"352","author":"Tirosh","year":"2016","journal-title":"Science"},{"key":"2024041009290541000_ref36","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1007\/978-981-13-6037-4_5","article-title":"Quantitation of MRNA transcripts and proteins using the BD Rhapsody$^{\\text{TM}}$ single-cell analysis system","volume":"1129","author":"Shum","year":"2019","journal-title":"Single Mol Single Cell Seq"},{"key":"2024041009290541000_ref37","first-page":"1","article-title":"High-throughput RNA isoform sequencing using programmed CDNA concatenation","author":"Al\u2019Khafaji","year":"2023","journal-title":"Nat Biotechnol"},{"key":"2024041009290541000_ref38","doi-asserted-by":"crossref","first-page":"216","DOI":"10.3389\/fimmu.2020.00216","article-title":"FB5P-seq: FACS-based 5-prime end single-cell RNA-seq for integrative analysis of transcriptome and antigen receptor repertoire in B and T cells","volume":"11","author":"Attaf","year":"2020","journal-title":"Front Immunol"},{"issue":"1","key":"2024041009290541000_ref39","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13059-019-1837-6","article-title":"DeepImpute: an accurate, fast, and scalable deep neural network method to impute single-cell rna-seq data","volume":"20","author":"Arisdakessian","year":"2019","journal-title":"Genome Biol"},{"key":"2024041009290541000_ref40","first-page":"2021","article-title":"Unsupervised spatially embedded deep representation of spatial transcriptomics.","author":"Huazhu","year":"2021"},{"issue":"6","key":"2024041009290541000_ref41","doi-asserted-by":"crossref","first-page":"628","DOI":"10.2174\/156720512801322573","article-title":"Overview and findings from the religious orders study","volume":"9","author":"Bennett","year":"2012","journal-title":"Curr Alzheimer Res"},{"issue":"6","key":"2024041009290541000_ref42","doi-asserted-by":"crossref","first-page":"646","DOI":"10.2174\/156720512801322663","article-title":"Overview and findings from the Rush Memory and Aging Project","volume":"9","author":"Bennett","year":"2012","journal-title":"Curr Alzheimer Res"}],"container-title":["Briefings in Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/24\/6\/bbad384\/57195846\/bbad384.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/24\/6\/bbad384\/57195846\/bbad384.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,4,10]],"date-time":"2024-04-10T09:38:25Z","timestamp":1712741905000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bib\/article\/doi\/10.1093\/bib\/bbad384\/7333676"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,9,22]]},"references-count":42,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2023,9,22]]}},"URL":"https:\/\/doi.org\/10.1093\/bib\/bbad384","relation":{},"ISSN":["1467-5463","1477-4054"],"issn-type":[{"value":"1467-5463","type":"print"},{"value":"1477-4054","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2023,11,1]]},"published":{"date-parts":[[2023,9,22]]},"article-number":"bbad384"}}