{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,18]],"date-time":"2026-09-18T00:23:12Z","timestamp":1789690992941,"version":"4.0.1"},"reference-count":45,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2023,8,1]],"date-time":"2023-08-01T00:00:00Z","timestamp":1690848000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2023,8,1]],"date-time":"2023-08-01T00:00:00Z","timestamp":1690848000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2023,8,1]],"date-time":"2023-08-01T00:00:00Z","timestamp":1690848000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2023,8,1]],"date-time":"2023-08-01T00:00:00Z","timestamp":1690848000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2023,8,1]],"date-time":"2023-08-01T00:00:00Z","timestamp":1690848000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2023,8,1]],"date-time":"2023-08-01T00:00:00Z","timestamp":1690848000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2023,8,1]],"date-time":"2023-08-01T00:00:00Z","timestamp":1690848000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62225209"],"award-info":[{"award-number":["62225209"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100019081","name":"Science and Technology Program of Hunan Province","doi-asserted-by":"publisher","award":["2019CB1007"],"award-info":[{"award-number":["2019CB1007"]}],"id":[{"id":"10.13039\/501100019081","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100019081","name":"Science and Technology Program of Hunan Province","doi-asserted-by":"publisher","award":["2021RC4008"],"award-info":[{"award-number":["2021RC4008"]}],"id":[{"id":"10.13039\/501100019081","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Methods"],"published-print":{"date-parts":[[2023,8]]},"DOI":"10.1016\/j.ymeth.2023.06.004","type":"journal-article","created":{"date-parts":[[2023,6,12]],"date-time":"2023-06-12T21:21:17Z","timestamp":1686604877000},"page":"21-38","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":11,"special_numbering":"C","title":["A posterior probability based Bayesian method for single-cell RNA-seq data imputation"],"prefix":"10.1016","volume":"216","author":[{"given":"Siqi","family":"Chen","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ruiqing","family":"Zheng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Luyi","family":"Tian","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fang-Xiang","family":"Wu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0188-1394","authenticated-orcid":false,"given":"Min","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"14","key":"10.1016\/j.ymeth.2023.06.004_b0005","doi-asserted-by":"crossref","first-page":"8845","DOI":"10.1093\/nar\/gku555","article-title":"Single-cell RNA-seq: advances and future challenges","volume":"42","author":"Saliba","year":"2014","journal-title":"Nucleic Acids Res."},{"key":"10.1016\/j.ymeth.2023.06.004_b0010","doi-asserted-by":"crossref","unstructured":"G. Chen, B. Ning, T. Shi, Single-cell RNA-seq technologies and related computational data analysis, Front. Genet. (2019) 317.","DOI":"10.3389\/fgene.2019.00317"},{"key":"10.1016\/j.ymeth.2023.06.004_b0015","doi-asserted-by":"crossref","first-page":"62","DOI":"10.3389\/fgene.2017.00062","article-title":"Single-cell RNA-sequencing: assessment of differential expression analysis methods","volume":"8","author":"Dal Molin","year":"2017","journal-title":"Front. Genet."},{"issue":"11","key":"10.1016\/j.ymeth.2023.06.004_b0020","doi-asserted-by":"crossref","first-page":"1096","DOI":"10.1038\/nmeth.2639","article-title":"Smart-seq2 for sensitive full-length transcriptome profiling in single cells","volume":"10","author":"Picelli","year":"2013","journal-title":"Nat. Methods"},{"issue":"8","key":"10.1016\/j.ymeth.2023.06.004_b0025","doi-asserted-by":"crossref","first-page":"777","DOI":"10.1038\/nbt.2282","article-title":"Full-length mRNA-Seq from single-cell levels of RNA and individual circulating tumor cells","volume":"30","author":"Ramsk\u00f6ld","year":"2012","journal-title":"Nat. Biotechnol."},{"key":"10.1016\/j.ymeth.2023.06.004_b0030","series-title":"CEL-Seq2\u2014Single-cell RNA sequencing by multiplexed linear amplification","first-page":"45","author":"Yanai","year":"2019"},{"key":"10.1016\/j.ymeth.2023.06.004_b0035","doi-asserted-by":"crossref","unstructured":"S.L. Goldman, M. MacKay, E. Afshinnekoo, A.M. Melnick, S. Wu, C.E. Mason, The impact of heterogeneity on single-cell sequencing, Front. Genet. (2019) 8.","DOI":"10.3389\/fgene.2019.00008"},{"key":"10.1016\/j.ymeth.2023.06.004_b0040","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13059-020-02132-x","article-title":"A systematic evaluation of single-cell RNA-sequencing imputation methods","volume":"21","author":"Hou","year":"2020","journal-title":"Genome Biol."},{"issue":"1","key":"10.1016\/j.ymeth.2023.06.004_b0045","doi-asserted-by":"crossref","DOI":"10.1186\/s13059-020-1926-6","article-title":"Eleven grand challenges in single-cell data science","volume":"21","author":"L\u00e4hnemann","year":"2020","journal-title":"Genome Biol."},{"issue":"5","key":"10.1016\/j.ymeth.2023.06.004_b0050","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":"1","key":"10.1016\/j.ymeth.2023.06.004_b0055","doi-asserted-by":"crossref","first-page":"4318","DOI":"10.1038\/s41467-020-17900-3","article-title":"A clustering-independent method for finding differentially expressed genes in single-cell transcriptome data","volume":"11","author":"Vandenbon","year":"2020","journal-title":"Nat. Commun."},{"issue":"5","key":"10.1016\/j.ymeth.2023.06.004_b0060","doi-asserted-by":"crossref","first-page":"547","DOI":"10.1038\/s41587-019-0071-9","article-title":"A comparison of single-cell trajectory inference methods","volume":"37","author":"Saelens","year":"2019","journal-title":"Nat. Biotechnol."},{"key":"10.1016\/j.ymeth.2023.06.004_b0065","article-title":"MAGIC: A diffusion-based imputation method reveals gene-gene interactions in single-cell RNA-sequencing data","author":"Dijk","year":"2017","journal-title":"BioRxiv"},{"key":"10.1016\/j.ymeth.2023.06.004_b0070","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 Bioinf."},{"key":"10.1016\/j.ymeth.2023.06.004_b0075","article-title":"Zero-preserving imputation of scRNA-seq data using low-rank approximation","author":"Linderman","year":"2018","journal-title":"BioRxiv"},{"issue":"1","key":"10.1016\/j.ymeth.2023.06.004_b0080","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."},{"issue":"1","key":"10.1016\/j.ymeth.2023.06.004_b0085","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."},{"issue":"7","key":"10.1016\/j.ymeth.2023.06.004_b0090","doi-asserted-by":"crossref","first-page":"539","DOI":"10.1038\/s41592-018-0033-z","article-title":"SAVER: gene expression recovery for single-cell RNA sequencing","volume":"15","author":"Huang","year":"2018","journal-title":"Nat. Methods"},{"issue":"1","key":"10.1016\/j.ymeth.2023.06.004_b0095","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."},{"issue":"1","key":"10.1016\/j.ymeth.2023.06.004_b0100","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13059-018-1575-1","article-title":"VIPER: variability-preserving imputation for accurate gene expression recovery in single-cell RNA sequencing studies","volume":"19","author":"Chen","year":"2018","journal-title":"Genome Biol."},{"issue":"4","key":"10.1016\/j.ymeth.2023.06.004_b0105","doi-asserted-by":"crossref","first-page":"1174","DOI":"10.1093\/bioinformatics\/btz726","article-title":"bayNorm: Bayesian gene expression recovery, imputation and normalization for single-cell RNA-sequencing data","volume":"36","author":"Tang","year":"2020","journal-title":"Bioinformatics"},{"key":"10.1016\/j.ymeth.2023.06.004_b0110","doi-asserted-by":"crossref","unstructured":"Z. Miao, J. Li, X. Zhang, scRecover: Discriminating true and false zeros in single-cell RNA-seq data for imputation, bioRxiv (2019) 665323.","DOI":"10.1101\/665323"},{"issue":"10","key":"10.1016\/j.ymeth.2023.06.004_b0115","doi-asserted-by":"crossref","first-page":"3131","DOI":"10.1093\/bioinformatics\/btaa108","article-title":"scTSSR: gene expression recovery for single-cell RNA sequencing using two-side sparse self-representation","volume":"36","author":"Jin","year":"2020","journal-title":"Bioinformatics"},{"issue":"1","key":"10.1016\/j.ymeth.2023.06.004_b0120","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13059-019-1681-8","article-title":"SCRABBLE: single-cell RNA-seq imputation constrained by bulk RNA-seq data","volume":"20","author":"Peng","year":"2019","journal-title":"Genome Biol."},{"issue":"23","key":"10.1016\/j.ymeth.2023.06.004_b0125","doi-asserted-by":"crossref","first-page":"5322","DOI":"10.1093\/bioinformatics\/btac684","article-title":"Ma, scGNN 2.0: a graph neural network tool for imputation and clustering of single-cell RNA-Seq data","volume":"38","author":"Gu","year":"2022","journal-title":"Bioinformatics"},{"issue":"2","key":"10.1016\/j.ymeth.2023.06.004_b0130","doi-asserted-by":"crossref","first-page":"451","DOI":"10.1016\/S0031-3203(02)00060-2","article-title":"The global k-means clustering algorithm","volume":"36","author":"Likas","year":"2003","journal-title":"Pattern Recogn."},{"issue":"1","key":"10.1016\/j.ymeth.2023.06.004_b0135","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13059-015-0844-5","article-title":"MAST: a flexible statistical framework for assessing transcriptional changes and characterizing heterogeneity in single-cell RNA sequencing data","volume":"16","author":"Finak","year":"2015","journal-title":"Genome Biol."},{"issue":"1","key":"10.1016\/j.ymeth.2023.06.004_b0140","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":"6226","key":"10.1016\/j.ymeth.2023.06.004_b0145","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"},{"key":"10.1016\/j.ymeth.2023.06.004_b0150","doi-asserted-by":"crossref","unstructured":"G. La Manno, D. Gyllborg, S. Codeluppi, K. Nishimura, C. Salto, A. Zeisel, L.E. Borm, S.R. Stott, E.M. Toledo, J.C. Villaescusa, Molecular diversity of midbrain development in mouse, human, and stem cells, Cell 167(2) (2016) 566-580. e19.","DOI":"10.1016\/j.cell.2016.09.027"},{"issue":"4","key":"10.1016\/j.ymeth.2023.06.004_b0155","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":"6","key":"10.1016\/j.ymeth.2023.06.004_b0160","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"},{"key":"10.1016\/j.ymeth.2023.06.004_b0165","doi-asserted-by":"crossref","unstructured":"E.P. Consortium, The ENCODE (ENCyclopedia of DNA elements) project, Science 306(5696) (2004) 636-640.","DOI":"10.1126\/science.1105136"},{"key":"10.1016\/j.ymeth.2023.06.004_b0170","unstructured":"S.R. Quake, T.S. Consortium, The Tabula Sapiens: a single cell transcriptomic atlas of multiple organs from individual human donors, Biorxiv (2021)."},{"issue":"19","key":"10.1016\/j.ymeth.2023.06.004_b0175","doi-asserted-by":"crossref","first-page":"3642","DOI":"10.1093\/bioinformatics\/btz139","article-title":"SinNLRR: a robust subspace clustering method for cell type detection by non-negative and low-rank representation","volume":"35","author":"Zheng","year":"2019","journal-title":"Bioinformatics"},{"issue":"2","key":"10.1016\/j.ymeth.2023.06.004_b0180","doi-asserted-by":"crossref","first-page":"282","DOI":"10.1016\/j.gpb.2020.09.004","article-title":"SSRE: cell type detection based on sparse subspace representation and similarity enhancement","volume":"19","author":"Liang","year":"2021","journal-title":"Genom. Proteom. Bioinform."},{"issue":"5","key":"10.1016\/j.ymeth.2023.06.004_b0185","doi-asserted-by":"crossref","first-page":"772","DOI":"10.26599\/TST.2020.9010028","article-title":"A data-driven clustering recommendation method for single-cell RNA-sequencing data","volume":"26","author":"Tian","year":"2021","journal-title":"Tsinghua Sci. Technol."},{"issue":"4","key":"10.1016\/j.ymeth.2023.06.004_b0190","doi-asserted-by":"crossref","first-page":"433","DOI":"10.1002\/wics.101","article-title":"Principal component analysis","volume":"2","author":"Abdi","year":"2010","journal-title":"Wiley Interdiscip. Rev. Comput. Stat."},{"key":"10.1016\/j.ymeth.2023.06.004_b0195","series-title":"2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","first-page":"302","article-title":"Deepci: a deep learning based clustering method for single cell rna-seq data","author":"Liang","year":"2021"},{"issue":"1","key":"10.1016\/j.ymeth.2023.06.004_b0200","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1038\/nbt.4314","article-title":"Dimensionality reduction for visualizing single-cell data using UMAP","volume":"37","author":"Becht","year":"2019","journal-title":"Nat. Biotechnol."},{"issue":"12","key":"10.1016\/j.ymeth.2023.06.004_b0205","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13059-014-0550-8","article-title":"Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2","volume":"15","author":"Love","year":"2014","journal-title":"Genome Biol."},{"issue":"1","key":"10.1016\/j.ymeth.2023.06.004_b0210","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1111\/j.2517-6161.1995.tb02031.x","article-title":"Controlling the false discovery rate: a practical and powerful approach to multiple testing","volume":"57","author":"Benjamini","year":"1995","journal-title":"J. Roy. Stat. Soc.: Ser. B (Methodol.)"},{"issue":"5","key":"10.1016\/j.ymeth.2023.06.004_b0215","doi-asserted-by":"crossref","first-page":"bbab085","DOI":"10.1093\/bib\/bbab085","article-title":"WEDGE: imputation of gene expression values from single-cell RNA-seq datasets using biased matrix decomposition","volume":"22","author":"Hu","year":"2021","journal-title":"Brief. Bioinform."},{"issue":"7677","key":"10.1016\/j.ymeth.2023.06.004_b0220","doi-asserted-by":"crossref","first-page":"451","DOI":"10.1038\/550451a","article-title":"The Human Cell Atlas: from vision to reality","volume":"550","author":"Rozenblatt-Rosen","year":"2017","journal-title":"Nature"},{"issue":"1","key":"10.1016\/j.ymeth.2023.06.004_b0225","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41467-018-07234-6","article-title":"Discovery of rare cells from voluminous single cell expression data","volume":"9","author":"Jindal","year":"2018","journal-title":"Nat. Commun."}],"container-title":["Methods"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1046202323000968?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1046202323000968?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,2,13]],"date-time":"2025-02-13T02:16:13Z","timestamp":1739412973000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1046202323000968"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,8]]},"references-count":45,"alternative-id":["S1046202323000968"],"URL":"https:\/\/doi.org\/10.1016\/j.ymeth.2023.06.004","relation":{},"ISSN":["1046-2023"],"issn-type":[{"value":"1046-2023","type":"print"}],"subject":[],"published":{"date-parts":[[2023,8]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"A posterior probability based Bayesian method for single-cell RNA-seq data imputation","name":"articletitle","label":"Article Title"},{"value":"Methods","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.ymeth.2023.06.004","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2023 Published by Elsevier Inc.","name":"copyright","label":"Copyright"}]}}