{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,7]],"date-time":"2026-05-07T14:16:13Z","timestamp":1778163373431,"version":"3.51.4"},"reference-count":49,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2024,11,27]],"date-time":"2024-11-27T00:00:00Z","timestamp":1732665600000},"content-version":"vor","delay-in-days":5,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R01 GM134005"],"award-info":[{"award-number":["R01 GM134005"]}],"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":["R56 AG074015"],"award-info":[{"award-number":["R56 AG074015"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,11,22]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Motivation: Selecting representative genes or marker genes to distinguish cell types is an important task in single-cell sequencing analysis. Although many methods have been proposed to select marker genes, the genes selected may have redundancy and\/or do not show cell-type-specific expression patterns to distinguish cell types. Results: Here, we present a novel model, named CosGeneGate, to select marker genes for more effective marker selections. CosGeneGate is inspired by combining the advantages of selecting marker genes based on both cell-type classification accuracy and marker gene specific expression patterns. We demonstrate the better performance of the marker genes selected by CosGeneGate for various downstream analyses than the existing methods with both public datasets and newly sequenced datasets. The non-redundant marker genes identified by CosGeneGate for major cell types and tissues in human can be found at the website as follows: https:\/\/github.com\/VivLon\/CosGeneGate\/blob\/main\/marker gene list.xlsx.<\/jats:p>","DOI":"10.1093\/bib\/bbae626","type":"journal-article","created":{"date-parts":[[2024,11,13]],"date-time":"2024-11-13T23:24:50Z","timestamp":1731540290000},"source":"Crossref","is-referenced-by-count":4,"title":["CosGeneGate selects multi-functional and credible biomarkers for single-cell analysis"],"prefix":"10.1093","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9412-6573","authenticated-orcid":false,"given":"Tianyu","family":"Liu","sequence":"first","affiliation":[{"name":"Department of Biostatistics, Yale University , New Haven, CT, 06520,","place":["United States"]},{"name":"Interdepartmental Program in Computational Biology & Bioinformatics, Yale University , New Haven, CT, 06520,","place":["United States"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenxin","family":"Long","sequence":"additional","affiliation":[{"name":"Department of Biostatistics, Yale University , New Haven, CT, 06520,","place":["United States"]},{"name":"Department of Statistics , The Pennsylvania State University, , PA, 16820,","place":["United States"]},{"name":"University Park , The Pennsylvania State University, , PA, 16820,","place":["United States"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhiyuan","family":"Cao","sequence":"additional","affiliation":[{"name":"Department of Biostatistics, Yale University , New Haven, CT, 06520,","place":["United States"]},{"name":"Interdepartmental Program in Computational Biology & Bioinformatics, Yale University , New Haven, CT, 06520,","place":["United States"]},{"name":"Program of Health Informatics, Yale University , New Haven, CT, 06520,","place":["United States"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuge","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Biostatistics, Yale University , New Haven, CT, 06520,","place":["United States"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chuan Hua","family":"He","sequence":"additional","affiliation":[{"name":"Department of Neurology, Yale University School of Medicine , New Haven, CT, 06520,","place":["United States"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Le","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Neurology, Yale University School of Medicine , New Haven, CT, 06520,","place":["United States"]},{"name":"Department of Neuroscience, Yale University School of Medicine , New Haven, CT, 06520,","place":["United States"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Stephen M","family":"Strittmatter","sequence":"additional","affiliation":[{"name":"Department of Neurology, Yale University School of Medicine , New Haven, CT, 06520,","place":["United States"]},{"name":"Department of Neuroscience, Yale University School of Medicine , New Haven, CT, 06520,","place":["United States"]},{"name":"Cellular Neuroscience , Neurodegeneration and Repair Program, , New Haven, CT, 06520,","place":["United States"]},{"name":"Yale University School of Medicine , Neurodegeneration and Repair Program, , New Haven, CT, 06520,","place":["United States"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongyu","family":"Zhao","sequence":"additional","affiliation":[{"name":"Department of Biostatistics, Yale University , New Haven, CT, 06520,","place":["United States"]},{"name":"Interdepartmental Program in Computational Biology & Bioinformatics, Yale University , New Haven, CT, 06520,","place":["United States"]}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2024,11,25]]},"reference":[{"key":"2024112701160521600_ref1","doi-asserted-by":"publisher","first-page":"303","DOI":"10.1038\/s41586-020-2157-4","article-title":"Construction of a human cell landscape at single-cell level","volume":"581","author":"Han","year":"2020","journal-title":"Nature"},{"key":"2024112701160521600_ref2","doi-asserted-by":"publisher","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":"2024112701160521600_ref3","doi-asserted-by":"publisher","first-page":"332","DOI":"10.1038\/s41586-019-1195-2","article-title":"Single-cell transcriptomic analysis of Alzheimer\u2019s disease","volume":"570","author":"Mathys","year":"2019","journal-title":"Nature"},{"key":"2024112701160521600_ref4","doi-asserted-by":"publisher","first-page":"58","DOI":"10.1126\/science.aan6828","article-title":"Single-cell transcriptomics to explore the immune system in health and disease","volume":"358","author":"Stubbington","year":"2017","journal-title":"Science"},{"key":"2024112701160521600_ref5","article-title":"Signal recovery in single cell batch","author":"Zhang","year":"2023"},{"key":"2024112701160521600_ref6","doi-asserted-by":"publisher","first-page":"171","DOI":"10.1146\/annurev-genom-111320-090436","article-title":"Applications of single-cell DNA sequencing","volume":"22","author":"Evrony","year":"2021","journal-title":"Annu Rev Genom Hum Genet"},{"key":"2024112701160521600_ref7","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s12276-018-0071-8","article-title":"Single-cell RNA sequencing technologies and bioinformatics pipelines","volume":"50","author":"Hwang","year":"2018","journal-title":"Exp Mol Med"},{"key":"2024112701160521600_ref8","doi-asserted-by":"publisher","first-page":"14049","DOI":"10.1038\/ncomms14049","article-title":"Massively parallel digital transcriptional profiling of single cells","volume":"8","author":"Zheng","year":"2017","journal-title":"Nat Commun"},{"key":"2024112701160521600_ref9","doi-asserted-by":"publisher","first-page":"865","DOI":"10.1038\/nmeth.4380","article-title":"Simultaneous epitope and transcriptome measurement in single cells","volume":"14","author":"Stoeckius","year":"2017","journal-title":"Nat Methods"},{"key":"2024112701160521600_ref10","doi-asserted-by":"publisher","first-page":"910","DOI":"10.1126\/science.aab1601","article-title":"Multiplex single-cell profiling of chromatin accessibility by combinatorial cellular indexing","volume":"348","author":"Cusanovich","year":"2015","journal-title":"Science"},{"key":"2024112701160521600_ref11","doi-asserted-by":"publisher","first-page":"342","DOI":"10.1002\/rcm.8043","article-title":"Differentiation of isomeric methylanilines by imidization and gas chromatography\/mass spectrometry analysis","volume":"32","author":"Chen","year":"2018","journal-title":"Rapid Comm Mass Spectrometry"},{"key":"2024112701160521600_ref12","doi-asserted-by":"publisher","first-page":"600","DOI":"10.1126\/science.aan3351","article-title":"Single-cell methylomes identify neuronal subtypes and regulatory elements in mammalian cortex","volume":"357","author":"Luo","year":"2017","journal-title":"Science"},{"key":"2024112701160521600_ref13","doi-asserted-by":"publisher","first-page":"428","DOI":"10.1038\/nbt.4112","article-title":"Highly scalable generation of DNA methylation profiles in single cells","volume":"36","author":"Mulqueen","year":"2018","journal-title":"Nat Biotechnol"},{"key":"2024112701160521600_ref14","doi-asserted-by":"publisher","first-page":"137","DOI":"10.1038\/s41586-021-03705-x","article-title":"Spatially resolved cell atlas of the mouse primary motor cortex by MERFISH","volume":"598","author":"Zhang","year":"2021","journal-title":"Nature"},{"key":"2024112701160521600_ref15","doi-asserted-by":"publisher","first-page":"313","DOI":"10.1146\/annurev-biodatasci-020422-050645","article-title":"Single-cell multiomics","volume":"6","author":"Flynn","year":"2023","journal-title":"Annu Rev Biomed Data Sci"},{"key":"2024112701160521600_ref16","doi-asserted-by":"publisher","first-page":"733","DOI":"10.1126\/science.adf6162","article-title":"What is a cell type?","volume":"381","author":"Fleck","year":"2023","journal-title":"Science"},{"key":"2024112701160521600_ref17","doi-asserted-by":"publisher","first-page":"4685","DOI":"10.1364\/BOE.7.004685","article-title":"In-vivo monitoring of tissue oxygen saturation in deep brain structures using a single fiber optical system","volume":"7","author":"Yu","year":"2016","journal-title":"Biomed Opt Express"},{"key":"2024112701160521600_ref18","doi-asserted-by":"publisher","first-page":"466","DOI":"10.1038\/s41586-020-2797-4","article-title":"Cells of the adult human heart","volume":"588","author":"Litvi\u0148ukov\u00e1","year":"2020","journal-title":"Nature"},{"key":"2024112701160521600_ref19","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1186\/s13059-021-02544-3","article-title":"Feature selection revisited in the single-cell era","volume":"22","author":"Yang","year":"2021","journal-title":"Genome Biol"},{"key":"2024112701160521600_ref20","doi-asserted-by":"publisher","first-page":"836","DOI":"10.1038\/s41556-018-0121-4","article-title":"Single-cell characterization of haematopoietic progenitors and their trajectories in homeostasis and perturbed haematopoiesis","volume":"20","author":"Giladi","year":"2018","journal-title":"Nat Cell Biol"},{"key":"2024112701160521600_ref21","doi-asserted-by":"publisher","first-page":"285","DOI":"10.3389\/fimmu.2019.00285","article-title":"Maternal immune response during pregnancy and vertical transmission in human toxoplasmosis","volume":"10","author":"G\u00f3mez-Ch\u00e1vez","year":"2019","journal-title":"Front Immunol"},{"key":"2024112701160521600_ref22","doi-asserted-by":"publisher","first-page":"eabl5197","DOI":"10.1126\/science.abl5197","article-title":"Cross-tissue immune cell analysis reveals tissue-specific features in humans","volume":"376","author":"Dom\u00ednguez Conde","year":"2022","journal-title":"Science"},{"key":"2024112701160521600_ref23","doi-asserted-by":"publisher","first-page":"1888","DOI":"10.1016\/j.cell.2019.05.031","article-title":"Comprehensive integration of single-cell data","volume":"177","author":"Stuart","year":"2019","journal-title":"Cell"},{"key":"2024112701160521600_ref24","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1186\/s13059-017-1382-0","article-title":"SCANPY: Large-scale single-cell gene expression data analysis","volume":"19","author":"Wolf","year":"2018","journal-title":"Genome Biol"},{"key":"2024112701160521600_ref25","doi-asserted-by":"publisher","first-page":"P10008","DOI":"10.1088\/1742-5468\/2008\/10\/P10008","article-title":"Fast unfolding of communities in large networks","volume":"2008","author":"Blondel","year":"2008","journal-title":"J Stat Mech"},{"key":"2024112701160521600_ref26","doi-asserted-by":"publisher","first-page":"5233","DOI":"10.1038\/s41598-019-41695-z","article-title":"From Louvain to Leiden: guaranteeing well-connected communities","volume":"9","author":"Traag","year":"2019","journal-title":"Sci Rep"},{"key":"2024112701160521600_ref27","doi-asserted-by":"publisher","first-page":"1767","DOI":"10.1101\/gr.275569.121","article-title":"A machine learning method for the discovery of minimum marker gene combinations for cell type identification from single-cell RNA sequencing","volume":"31","author":"Aevermann","year":"2021","journal-title":"Genome Res"},{"key":"2024112701160521600_ref28","doi-asserted-by":"publisher","first-page":"1186","DOI":"10.1038\/s41467-021-21453-4","article-title":"Optimal marker gene selection for cell type discrimination in single cell analyses","volume":"12","author":"Dumitrascu","year":"2021","journal-title":"Nat Commun"},{"key":"2024112701160521600_ref29","doi-asserted-by":"publisher","first-page":"bbab579","DOI":"10.1093\/bib\/bbab579","article-title":"Accurate and fast cell marker gene identification with COSG","volume":"23","author":"Dai","year":"2022","journal-title":"Brief Bioinform"},{"key":"2024112701160521600_ref30","first-page":"10648","article-title":"Feature selection using stochastic gates","volume":"119","author":"Yamada","year":"2020","journal-title":"Proceedings of the 37th International Conference on Machine Learning"},{"key":"2024112701160521600_ref31","doi-asserted-by":"publisher","first-page":"56","DOI":"10.1186\/s13059-024-03183-0","article-title":"A comparison of marker gene selection methods for single-cell RNA sequencing data","volume":"25","author":"Pullin","year":"2024","journal-title":"Genome Biol"},{"key":"2024112701160521600_ref32","doi-asserted-by":"publisher","first-page":"3942","DOI":"10.1093\/bioinformatics\/btac427","article-title":"ResPAN: a powerful batch correction model for scRNA-seq data through residual adversarial networks","volume":"38","author":"Wang","year":"2022","journal-title":"Bioinformatics"},{"key":"2024112701160521600_ref33","doi-asserted-by":"publisher","first-page":"1458","DOI":"10.1038\/s41587-019-0332-7","article-title":"Single-cell multiomic analysis identifies regulatory programs in mixed-phenotype acute leukemia","volume":"37","author":"Granja","year":"2019","journal-title":"Nat Biotechnol"},{"key":"2024112701160521600_ref34","doi-asserted-by":"publisher","first-page":"e20210582","DOI":"10.1084\/jem.20210582","article-title":"Multi-omic profiling reveals widespread dysregulation of innate immunity and hematopoiesis in COVID-19","volume":"218","author":"Wilk","year":"2021","journal-title":"Journal of Experimental Medicine"},{"key":"2024112701160521600_ref35","doi-asserted-by":"publisher","first-page":"12","DOI":"10.1186\/s13059-019-1850-9","article-title":"A benchmark of batch-effect correction methods for single-cell RNA sequencing data","volume":"21","author":"Tran","year":"2020","journal-title":"Genome Biol"},{"key":"2024112701160521600_ref36","doi-asserted-by":"publisher","first-page":"59","DOI":"10.1186\/s13059-019-1663-x","article-title":"PAGA: graph abstraction reconciles clustering with trajectory inference through a topology preserving map of single cells","volume":"20","author":"Wolf","year":"2019","journal-title":"Genome Biol"},{"key":"2024112701160521600_ref37","doi-asserted-by":"crossref","DOI":"10.1016\/j.isci.2021.103292","article-title":"How many markers are needed to robustly determine a cell\u2019s type?","volume":"24","author":"Fischer","year":"2021","journal-title":"iScience"},{"key":"2024112701160521600_ref38","doi-asserted-by":"publisher","first-page":"773","DOI":"10.1038\/s41587-019-0114-2","article-title":"Determining cell type abundance and expression from bulk tissues with digital cytometry","volume":"37","author":"Newman","year":"2019","journal-title":"Nat Biotechnol"},{"key":"2024112701160521600_ref39","doi-asserted-by":"publisher","first-page":"380","DOI":"10.1038\/s41467-018-08023-x","article-title":"Bulk tissue cell type deconvolution with multi-subject single-cell expression reference","volume":"10","author":"Wang","year":"2019","journal-title":"Nat Commun"},{"key":"2024112701160521600_ref40","doi-asserted-by":"publisher","first-page":"385","DOI":"10.1038\/s41467-022-28020-5","article-title":"Advances in mixed cell deconvolution enable quantification of cell types in spatial transcriptomic data","volume":"13","author":"Danaher","year":"2022","journal-title":"Nat Commun"},{"key":"2024112701160521600_ref41","doi-asserted-by":"publisher","first-page":"416","DOI":"10.1093\/bib\/bbz166","article-title":"SCDC: bulk gene expression deconvolution by multiple single-cell RNA sequencing references","volume":"22","author":"Dong","year":"2021","journal-title":"Brief Bioinform"},{"key":"2024112701160521600_ref42","doi-asserted-by":"publisher","first-page":"1352","DOI":"10.1038\/s41592-021-01264-7","article-title":"Deep learning and alignment of spatially resolved single-cell transcriptomes with tangram","volume":"18","author":"Biancalani","year":"2021","journal-title":"Nat Methods"},{"key":"2024112701160521600_ref43","doi-asserted-by":"publisher","first-page":"e9389","DOI":"10.15252\/msb.20199389","article-title":"scClassify: sample size estimation and multiscale classification of cells using single and multiple reference","volume":"16","author":"Lin","year":"2020","journal-title":"Mol Syst Biol"},{"key":"2024112701160521600_ref44","doi-asserted-by":"publisher","first-page":"106634","DOI":"10.1016\/j.compbiomed.2023.106634","article-title":"scMAGS: marker gene selection from scRNA-seq data for spatial transcriptomics studies","volume":"155","author":"Baran","year":"2023","journal-title":"Comput Biol Med"},{"key":"2024112701160521600_ref45","doi-asserted-by":"publisher","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":"2024112701160521600_ref46","doi-asserted-by":"publisher","first-page":"1276","DOI":"10.1016\/j.cell.2017.05.018","article-title":"A unique microglia type associated with restricting development of Alzheimer\u2019s disease","volume":"169","author":"Keren-Shaul","year":"2017","journal-title":"Cell"},{"key":"2024112701160521600_ref47","doi-asserted-by":"publisher","first-page":"459","DOI":"10.1083\/jcb.201709069","article-title":"Microglia in Alzheimer\u2019s disease","volume":"217","author":"Hansen","year":"2018","journal-title":"J Cell Biol"},{"key":"2024112701160521600_ref48","doi-asserted-by":"publisher","first-page":"1117172","DOI":"10.3389\/fimmu.2023.1117172","article-title":"The effects of microglia-associated neuroinflammation on Alzheimer\u2019s disease","volume":"14","author":"Wang","year":"2023","journal-title":"Front Immunol"},{"key":"2024112701160521600_ref49","doi-asserted-by":"crossref","DOI":"10.1101\/2023.02.18.23286037","article-title":"Single-cell transcriptomic atlas of Alzheimer\u2019s disease middle temporal gyrus reveals region, cell type and sex specificity of gene expression with novel genetic risk for MERTK in","author":"Zhang","year":"2023"}],"container-title":["Briefings in Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/26\/1\/bbae626\/60811454\/bbae626.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/26\/1\/bbae626\/60811454\/bbae626.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,26]],"date-time":"2024-11-26T20:16:41Z","timestamp":1732652201000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bib\/article\/doi\/10.1093\/bib\/bbae626\/7908434"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,22]]},"references-count":49,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2024,11,22]]}},"URL":"https:\/\/doi.org\/10.1093\/bib\/bbae626","relation":{"has-preprint":[{"id-type":"doi","id":"10.1101\/2024.05.22.595428","asserted-by":"object"}]},"ISSN":["1467-5463","1477-4054"],"issn-type":[{"value":"1467-5463","type":"print"},{"value":"1477-4054","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2025,1]]},"published":{"date-parts":[[2024,11,22]]},"article-number":"bbae626"}}