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Correcting the Mean-Variance Dependency for Differential Variability Testing Using Single-Cell RNA Sequencing Data.

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cam.issuedOnline2018-08-29
dc.contributor.authorEling, Nils
dc.contributor.authorRichard, Arianne C
dc.contributor.authorRichardson, Sylvia
dc.contributor.authorMarioni, John C
dc.contributor.authorVallejos, Catalina A
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.date.accessioned2018-11-08T00:31:22Z
dc.date.available2018-11-08T00:31:22Z
dc.date.issued2018-09-26
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.identifier.doi10.17863/CAM.32139
dc.identifier.eissn2405-4720
dc.identifier.issn2405-4712
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/284768
dc.languageeng
dc.language.isoeng
dc.publisherElsevier BV
dc.publisher.urlhttp://dx.doi.org/10.1016/j.cels.2018.06.011
dc.rightsAttribution 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectBayesian
dc.subjectimmune activation
dc.subjectsingle-cell RNA sequencing
dc.subjectstatistics
dc.subjecttranscriptional noise
dc.subjectvariability
dc.subjectAnimals
dc.subjectBiological Variation, Population
dc.subjectCD4-Positive T-Lymphocytes
dc.subjectCell Differentiation
dc.subjectCell Lineage
dc.subjectComputer Simulation
dc.subjectGene Expression Regulation
dc.subjectImmunity
dc.subjectLymphocyte Activation
dc.subjectMice
dc.subjectMice, Inbred C57BL
dc.subjectModels, Theoretical
dc.subjectSequence Analysis, RNA
dc.subjectSingle-Cell Analysis
dc.subjectTranscriptome
dc.titleCorrecting the Mean-Variance Dependency for Differential Variability Testing Using Single-Cell RNA Sequencing Data.
dc.typeArticle
dcterms.dateAccepted2018-06-25
prism.endingPage294.e12
prism.issueIdentifier3
prism.publicationDate2018
prism.publicationNameCell Syst
prism.startingPage284
prism.volume7
pubs.funder-project-idCancer Research UK (C14303/A17197)
pubs.funder-project-idMedical Research Council (MR/P014178/1)
rioxxterms.licenseref.startdate2018-09
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserved
rioxxterms.typeJournal Article/Review
rioxxterms.versionVoR
rioxxterms.versionofrecord10.1016/j.cels.2018.06.011

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