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dc.contributor.authorLun, Aaronen
dc.contributor.authorMarioni, Johnen
dc.date.accessioned2018-09-05T12:51:01Z
dc.date.available2018-09-05T12:51:01Z
dc.date.issued2017-07-01en
dc.identifier.issn1465-4644
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/279637
dc.description.abstractAn increasing number of studies are using single-cell RNA-sequencing (scRNA-seq) to characterize the gene expression profiles of individual cells. One common analysis applied to scRNA-seq data involves detecting differentially expressed (DE) genes between cells in different biological groups. However, many experiments are designed such that the cells to be compared are processed in separate plates or chips, meaning that the groupings are confounded with systematic plate effects. This confounding aspect is frequently ignored in DE analyses of scRNA-seq data. In this article, we demonstrate that failing to consider plate effects in the statistical model results in loss of type I error control. A solution is proposed whereby counts are summed from all cells in each plate and the count sums for all plates are used in the DE analysis. This restores type I error control in the presence of plate effects without compromising detection power in simulated data. Summation is also robust to varying numbers and library sizes of cells on each plate. Similar results are observed in DE analyses of real data where the use of count sums instead of single-cell counts improves specificity and the ranking of relevant genes. This suggests that summation can assist in maintaining statistical rigour in DE analyses of scRNA-seq data with plate effects.
dc.description.sponsorshipThis work was supported by the University of Cambridge, Cancer Research UK (award no. A17197) and Hutchison Whampoa Limited. J.C.M. was also supported by core funding from the European Molecular Biology Laboratory.
dc.publisherOxford University Press
dc.rightsAttribution 4.0 International (CC BY 4.0)
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.titleOvercoming confounding plate effects in differential expression analyses of single-cell RNA-seq dataen
dc.typeArticle
prism.endingPage464
prism.issueIdentifier3en
prism.publicationDate2017en
prism.publicationNameBiostatisticsen
prism.startingPage451
prism.volume18en
dc.identifier.doi10.17863/CAM.27006
dcterms.dateAccepted2016-11-30en
rioxxterms.versionofrecord10.1093/biostatistics/kxw055en
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserveden
rioxxterms.licenseref.startdate2017-07-01en
dc.contributor.orcidLun, Aaron [0000-0002-3564-4813]
dc.contributor.orcidMarioni, John [0000-0001-9092-0852]
rioxxterms.typeJournal Article/Reviewen
cam.issuedOnline2017-02-06en


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