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dc.contributor.authorFinak, Gregen_US
dc.contributor.authorMcDavid, Andrewen_US
dc.contributor.authorYajima, Masanaoen_US
dc.contributor.authorDeng, Jingyuanen_US
dc.contributor.authorGersuk, Vivianen_US
dc.contributor.authorShalek, Alex K.en_US
dc.contributor.authorSlichter, Chloe K.en_US
dc.contributor.authorMiller, Hannah W.en_US
dc.contributor.authorMcElrath, M. Julianaen_US
dc.contributor.authorPrlic, Martinen_US
dc.contributor.authorLinsley, Peter S.en_US
dc.contributor.authorGottardo, Raphaelen_US
dc.coverage.spatialEnglanden_US
dc.date2015-11-24
dc.date.accessioned2018-03-29T17:41:36Z
dc.date.available2018-03-29T17:41:36Z
dc.date.issued2015-12-10
dc.identifierhttps://www.ncbi.nlm.nih.gov/pubmed/26653891
dc.identifier.citationGreg Finak, Andrew McDavid, Masanao Yajima, Jingyuan Deng, Vivian Gersuk, Alex K Shalek, Chloe K Slichter, Hannah W Miller, M Juliana McElrath, Martin Prlic, Peter S Linsley, Raphael Gottardo. 2015. "MAST: a flexible statistical framework for assessing transcriptional changes and characterizing heterogeneity in single-cell RNA sequencing data.." Genome Biol, Volume 16: 278.
dc.identifier.issn1474-760X
dc.identifier.urihttps://hdl.handle.net/2144/27896
dc.description.abstractSingle-cell transcriptomics reveals gene expression heterogeneity but suffers from stochastic dropout and characteristic bimodal expression distributions in which expression is either strongly non-zero or non-detectable. We propose a two-part, generalized linear model for such bimodal data that parameterizes both of these features. We argue that the cellular detection rate, the fraction of genes expressed in a cell, should be adjusted for as a source of nuisance variation. Our model provides gene set enrichment analysis tailored to single-cell data. It provides insights into how networks of co-expressed genes evolve across an experimental treatment. MAST is available at https://github.com/RGLab/MAST .en_US
dc.description.sponsorshipDP2 DE023321 - NIDCR NIH HHS; R01 EB008400 - NIBIB NIH HHS; UM1 AI068635 - NIAID NIH HHSen_US
dc.format.extentp. 278en_US
dc.languageeng
dc.relation.ispartofGenome Biol
dc.rightsThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.en_US
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectAnimalsen_US
dc.subjectData interpretation, statisticalen_US
dc.subjectDendritic cellsen_US
dc.subjectGene expression profilingen_US
dc.subjectGenetic variationen_US
dc.subjectHumansen_US
dc.subjectLinear modelsen_US
dc.subjectMiceen_US
dc.subjectSequence analysis, RNAen_US
dc.subjectSingle-cell analysisen_US
dc.subjectTranscriptomeen_US
dc.subjectDendritic cellsen_US
dc.subjectAnimalsen_US
dc.subjectEnvironmental sciencesen_US
dc.subjectBiological sciencesen_US
dc.subjectInformation and computing sciencesen_US
dc.subjectBioinformaticsen_US
dc.titleMAST: a flexible statistical framework for assessing transcriptional changes and characterizing heterogeneity in single-cell RNA sequencing dataen_US
dc.typeArticleen_US
dc.identifier.doi10.1186/s13059-015-0844-5
pubs.elements-sourcepubmeden_US
pubs.notesEmbargo: Not knownen_US
pubs.organisational-groupBoston Universityen_US
pubs.organisational-groupBoston University, College of Arts & Sciencesen_US
pubs.organisational-groupBoston University, College of Arts & Sciences, Department of Mathematics & Statisticsen_US
pubs.publication-statusPublished onlineen_US


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This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
Except where otherwise noted, this item's license is described as This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.