{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,12]],"date-time":"2026-08-12T03:27:42Z","timestamp":1786505262240,"version":"3.56.0"},"reference-count":11,"publisher":"Oxford University Press (OUP)","issue":"16","license":[{"start":{"date-parts":[[2018,12,24]],"date-time":"2018-12-24T00:00:00Z","timestamp":1545609600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100010269","name":"Wellcome Trust","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100010269","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Chan Zuckerberg Initiative DAF","award":["183501"],"award-info":[{"award-number":["183501"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019,8,15]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Motivation<\/jats:title>\n                    <jats:p>Most genomes contain thousands of genes, but for most functional responses, only a subset of those genes are relevant. To facilitate many single-cell RNASeq (scRNASeq) analyses the set of genes is often reduced through feature selection, i.e. by removing genes only subject to technical noise.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>We present M3Drop, an R package that implements popular existing feature selection methods and two novel methods which take advantage of the prevalence of zeros (dropouts) in scRNASeq data to identify features. We show these new methods outperform existing methods on simulated and real datasets.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and implementation<\/jats:title>\n                    <jats:p>M3Drop is freely available on github as an R package and is compatible with other popular scRNASeq tools: https:\/\/github.com\/tallulandrews\/M3Drop.<\/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\/bty1044","type":"journal-article","created":{"date-parts":[[2018,12,19]],"date-time":"2018-12-19T15:13:33Z","timestamp":1545232413000},"page":"2865-2867","source":"Crossref","is-referenced-by-count":236,"title":["M3Drop: dropout-based feature selection for scRNASeq"],"prefix":"10.1093","volume":"35","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1120-2196","authenticated-orcid":false,"given":"Tallulah S","family":"Andrews","sequence":"first","affiliation":[{"name":"Department of Cellular Genetics, Wellcome Trust Sanger Institute, Hinxton, Cambridgshire, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8895-5239","authenticated-orcid":false,"given":"Martin","family":"Hemberg","sequence":"additional","affiliation":[{"name":"Department of Cellular Genetics, Wellcome Trust Sanger Institute, Hinxton, Cambridgshire, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2018,12,24]]},"reference":[{"key":"2023062708551169800_bty1044-B1","doi-asserted-by":"crossref","first-page":"1765","DOI":"10.1038\/nprot.2013.099","article-title":"Count-based differential expression analysis of RNA sequencing data using R and Bioconductor","volume":"8","author":"Anders","year":"2013","journal-title":"Nat. Protoc"},{"key":"2023062708551169800_bty1044-B2","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"},{"key":"2023062708551169800_bty1044-B3","doi-asserted-by":"crossref","first-page":"637","DOI":"10.1038\/nmeth.2930","article-title":"Validation of noise models for single-cell transcriptomics","volume":"11","author":"Gr\u00fcn","year":"2014","journal-title":"Nat. Methods"},{"key":"2023062708551169800_bty1044-B4","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1038\/nmeth.2772","article-title":"Quantitative single-cell RNA-seq with unique molecular identifiers","volume":"11","author":"Islam","year":"2014","journal-title":"Nat. Methods"},{"key":"2023062708551169800_bty1044-B5","doi-asserted-by":"crossref","first-page":"144","DOI":"10.1186\/s13059-016-1010-4","article-title":"GiniClust: detecting rare cell types from single-cell gene expression data with Gini index","volume":"17","author":"Jiang","year":"2016","journal-title":"Genome Biol"},{"key":"2023062708551169800_bty1044-B6","doi-asserted-by":"crossref","first-page":"740","DOI":"10.1038\/nmeth.2967","article-title":"Bayesian approach to single-cell differential ex pression analysis","volume":"11","author":"Kharchenko","year":"2014","journal-title":"Nat. Methods"},{"key":"2023062708551169800_bty1044-B7","doi-asserted-by":"crossref","first-page":"359","DOI":"10.1038\/nmeth.4644","article-title":"scmap: projection of single-cell RNA-seq data across data sets","volume":"15","author":"Kiselev","year":"2018","journal-title":"Nat. Methods"},{"key":"2023062708551169800_bty1044-B8","doi-asserted-by":"crossref","first-page":"1202","DOI":"10.1016\/j.cell.2015.05.002","article-title":"Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets","volume":"161","author":"Macosko","year":"2015","journal-title":"Cell"},{"key":"2023062708551169800_bty1044-B9","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"},{"key":"2023062708551169800_bty1044-B10","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1186\/s13059-015-0805-z","article-title":"ZIFA: dimensionality reduction for zero-inflated single-cell gene expression analysis","volume":"16","author":"Pierson","year":"2015","journal-title":"Genome Biol"},{"key":"2023062708551169800_bty1044-B11","doi-asserted-by":"crossref","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"}],"container-title":["Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/35\/16\/2865\/50719160\/bty1044.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/35\/16\/2865\/50719160\/bty1044.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,6,27]],"date-time":"2023-06-27T04:55:54Z","timestamp":1687841754000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article\/35\/16\/2865\/5258099"}},"subtitle":[],"editor":[{"given":"Inanc","family":"Birol","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"editor"}]}],"short-title":[],"issued":{"date-parts":[[2018,12,24]]},"references-count":11,"journal-issue":{"issue":"16","published-print":{"date-parts":[[2019,8,15]]}},"URL":"https:\/\/doi.org\/10.1093\/bioinformatics\/bty1044","relation":{"has-preprint":[{"id-type":"doi","id":"10.1101\/065094","asserted-by":"object"}]},"ISSN":["1367-4803","1367-4811"],"issn-type":[{"value":"1367-4803","type":"print"},{"value":"1367-4811","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2019,8,15]]},"published":{"date-parts":[[2018,12,24]]}}}