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dc.contributor.authorEling, Nils
dc.contributor.authorRichard, Arianne
dc.contributor.authorRichardson, Sylvia
dc.contributor.authorMarioni, John
dc.contributor.authorVallejos, Catalina A
dc.date.accessioned2018-11-08T00:31:22Z
dc.date.available2018-11-08T00:31:22Z
dc.date.issued2018-09-26
dc.identifier.issn2405-4712
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/284768
dc.description.abstractCell-to-cell transcriptional variability in otherwise homogeneous cell populations plays an important role in tissue function and development. Single-cell RNA sequencing can characterize this variability in a transcriptome-wide manner. However, technical variation and the confounding between variability and mean expression estimates hinder meaningful comparison of expression variability between cell populations. To address this problem, we introduce an analysis approach that extends the BASiCS statistical framework to derive a residual measure of variability that is not confounded by mean expression. This includes a robust procedure for quantifying technical noise in experiments where technical spike-in molecules are not available. We illustrate how our method provides biological insight into the dynamics of cell-to-cell expression variability, highlighting a synchronization of biosynthetic machinery components in immune cells upon activation. In contrast to the uniform up-regulation of the biosynthetic machinery, CD4+ T cells show heterogeneous up-regulation of immune-related and lineage-defining genes during activation and differentiation.
dc.description.sponsorshipNE was funded by the European Molecular Biology Laboratory (EMBL) international PhD programme. ACR was funded by the MRC Skills Development Fellowship (MR/P014178/1). SR was funded by MRC grant MC_UP_0801/1. JCM was funded by core support of Cancer Research UK and EMBL. CAV was funded by The Alan Turing Institute, EPSRC grant EP/N510129/1.
dc.format.mediumPrint-Electronic
dc.languageeng
dc.publisherElsevier BV
dc.rightsAttribution 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectCD4-Positive T-Lymphocytes
dc.subjectAnimals
dc.subjectMice, Inbred C57BL
dc.subjectMice
dc.subjectSequence Analysis, RNA
dc.subjectLymphocyte Activation
dc.subjectCell Differentiation
dc.subjectImmunity
dc.subjectGene Expression Regulation
dc.subjectCell Lineage
dc.subjectModels, Theoretical
dc.subjectComputer Simulation
dc.subjectSingle-Cell Analysis
dc.subjectTranscriptome
dc.subjectBiological Variation, Population
dc.titleCorrecting the Mean-Variance Dependency for Differential Variability Testing Using Single-Cell RNA Sequencing Data.
dc.typeArticle
prism.endingPage294.e12
prism.issueIdentifier3
prism.publicationDate2018
prism.publicationNameCell Syst
prism.startingPage284
prism.volume7
dc.identifier.doi10.17863/CAM.32139
dcterms.dateAccepted2018-06-25
rioxxterms.versionofrecord10.1016/j.cels.2018.06.011
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserved
rioxxterms.licenseref.startdate2018-09
dc.contributor.orcidRichard, Arianne [0000-0002-8708-9997]
dc.contributor.orcidRichardson, Sylvia [0000-0003-1998-492X]
dc.contributor.orcidMarioni, John [0000-0001-9092-0852]
dc.identifier.eissn2405-4720
rioxxterms.typeJournal Article/Review
pubs.funder-project-idCancer Research UK (C14303/A17197)
pubs.funder-project-idMedical Research Council (MR/P014178/1)


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Attribution 4.0 International
Except where otherwise noted, this item's licence is described as Attribution 4.0 International