{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,11]],"date-time":"2026-04-11T06:55:38Z","timestamp":1775890538442,"version":"3.50.1"},"reference-count":60,"publisher":"Oxford University Press (OUP)","issue":"4","license":[{"start":{"date-parts":[[2019,10,4]],"date-time":"2019-10-04T00:00:00Z","timestamp":1570147200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"UK Medical Research Council, a Leverhulme Research Project","award":["RPG-2014-408"],"award-info":[{"award-number":["RPG-2014-408"]}]},{"name":"EPCRC Centre for Mathematics of Precision Health"},{"name":"Roth Scholarship from the Department of Mathematics at Imperial College"},{"DOI":"10.13039\/501100000265","name":"UK Medical Research Council","doi-asserted-by":"crossref","award":["MR\/L01632X\/1"],"award-info":[{"award-number":["MR\/L01632X\/1"]}],"id":[{"id":"10.13039\/501100000265","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Imperial College Research Computing Service"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020,2,15]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Motivation<\/jats:title>\n                    <jats:p>Normalization of single-cell RNA-sequencing (scRNA-seq) data is a prerequisite to their interpretation. The marked technical variability, high amounts of missing observations and batch effect typical of scRNA-seq datasets make this task particularly challenging. There is a need for an efficient and unified approach for normalization, imputation and batch effect correction.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Here, we introduce bayNorm, a novel Bayesian approach for scaling and inference of scRNA-seq counts. The method\u2019s likelihood function follows a binomial model of mRNA capture, while priors are estimated from expression values across cells using an empirical Bayes approach. We first validate our assumptions by showing this model can reproduce different statistics observed in real scRNA-seq data. We demonstrate using publicly available scRNA-seq datasets and simulated expression data that bayNorm allows robust imputation of missing values generating realistic transcript distributions that match single molecule fluorescence in situ hybridization measurements. Moreover, by using priors informed by dataset structures, bayNorm improves accuracy and sensitivity of differential expression analysis and reduces batch effect compared with other existing methods. Altogether, bayNorm provides an efficient, integrated solution for global scaling normalization, imputation and true count recovery of gene expression measurements from scRNA-seq data.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and implementation<\/jats:title>\n                    <jats:p>The R package \u2018bayNorm\u2019 is publishd on bioconductor at https:\/\/bioconductor.org\/packages\/release\/bioc\/html\/bayNorm.html. The code for analyzing data in this article is available at https:\/\/github.com\/WT215\/bayNorm_papercode.<\/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\/btz726","type":"journal-article","created":{"date-parts":[[2019,9,28]],"date-time":"2019-09-28T00:13:29Z","timestamp":1569629609000},"page":"1174-1181","source":"Crossref","is-referenced-by-count":112,"title":["bayNorm: Bayesian gene expression recovery, imputation and normalization for single-cell RNA-sequencing data"],"prefix":"10.1093","volume":"36","author":[{"given":"Wenhao","family":"Tang","sequence":"first","affiliation":[{"name":"Department of Mathematics, Faculty of Natural Sciences, Imperial College , London SW7 2AZ, UK"}]},{"given":"Fran\u00e7ois","family":"Bertaux","sequence":"additional","affiliation":[{"name":"Department of Mathematics, Faculty of Natural Sciences, Imperial College , London SW7 2AZ, UK"},{"name":"MRC London Institute of Medical Sciences (LMS) , London W12 0NN, UK"},{"name":"Faculty of Medicine, Institute of Clinical Sciences (ICS), Imperial College London , London W12 0NN, UK"}]},{"given":"Philipp","family":"Thomas","sequence":"additional","affiliation":[{"name":"Department of Mathematics, Faculty of Natural Sciences, Imperial College , London SW7 2AZ, UK"}]},{"given":"Claire","family":"Stefanelli","sequence":"additional","affiliation":[{"name":"Department of Mathematics, Faculty of Natural Sciences, Imperial College , London SW7 2AZ, UK"}]},{"given":"Malika","family":"Saint","sequence":"additional","affiliation":[{"name":"MRC London Institute of Medical Sciences (LMS) , London W12 0NN, UK"},{"name":"Faculty of Medicine, Institute of Clinical Sciences (ICS), Imperial College London , London W12 0NN, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2402-3165","authenticated-orcid":false,"given":"Samuel","family":"Marguerat","sequence":"additional","affiliation":[{"name":"MRC London Institute of Medical Sciences (LMS) , London W12 0NN, UK"},{"name":"Faculty of Medicine, Institute of Clinical Sciences (ICS), Imperial College London , London W12 0NN, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4013-5458","authenticated-orcid":false,"given":"Vahid","family":"Shahrezaei","sequence":"additional","affiliation":[{"name":"Department of Mathematics, Faculty of Natural Sciences, Imperial College , London SW7 2AZ, UK"}]}],"member":"286","published-online":{"date-parts":[[2019,10,4]]},"reference":[{"key":"2023013110150578600_btz726-B1","volume-title":"Differential Expression of RNA-Seq Data at the Gene Levelathe Deseq Package","author":"Anders","year":"2012"},{"key":"2023013110150578600_btz726-B2","author":"Andrews","year":"2018"},{"key":"2023013110150578600_btz726-B3","doi-asserted-by":"crossref","first-page":"1740.","DOI":"10.12688\/f1000research.16613.1","article-title":"False signals induced by single-cell imputation","volume":"7","author":"Andrews","year":"2018","journal-title":"F1000Research"},{"key":"2023013110150578600_btz726-B4","doi-asserted-by":"crossref","first-page":"1293","DOI":"10.1016\/j.cell.2018.05.060","article-title":"Single-cell map of diverse immune phenotypes in the breast tumor microenvironment","volume":"174","author":"Azizi","year":"2018","journal-title":"Cell"},{"key":"2023013110150578600_btz726-B5","doi-asserted-by":"crossref","first-page":"63.","DOI":"10.1186\/s13059-016-0927-y","article-title":"Design and computational analysis of single-cell RNA-sequencing experiments","volume":"17","author":"Bacher","year":"2016","journal-title":"Genome Biol"},{"key":"2023013110150578600_btz726-B6","doi-asserted-by":"crossref","first-page":"584.","DOI":"10.1038\/nmeth.4263","article-title":"SCnorm: robust normalization of single-cell RNA-seq data","volume":"14","author":"Bacher","year":"2017","journal-title":"Nat. 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