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dc.contributor.authorLun, Aaron
dc.contributor.authorMcCarthy, Davis J
dc.contributor.authorMarioni, John
dc.date.accessioned2018-11-07T00:30:36Z
dc.date.available2018-11-07T00:30:36Z
dc.date.issued2016
dc.identifier.issn2046-1402
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/284686
dc.description.abstractSingle-cell RNA sequencing (scRNA-seq) is widely used to profile the transcriptome of individual cells. This provides biological resolution that cannot be matched by bulk RNA sequencing, at the cost of increased technical noise and data complexity. The differences between scRNA-seq and bulk RNA-seq data mean that the analysis of the former cannot be performed by recycling bioinformatics pipelines for the latter. Rather, dedicated single-cell methods are required at various steps to exploit the cellular resolution while accounting for technical noise. This article describes a computational workflow for low-level analyses of scRNA-seq data, based primarily on software packages from the open-source Bioconductor project. It covers basic steps including quality control, data exploration and normalization, as well as more complex procedures such as cell cycle phase assignment, identification of highly variable and correlated genes, clustering into subpopulations and marker gene detection. Analyses were demonstrated on gene-level count data from several publicly available datasets involving haematopoietic stem cells, brain-derived cells, T-helper cells and mouse embryonic stem cells. This will provide a range of usage scenarios from which readers can construct their own analysis pipelines.
dc.format.mediumElectronic-eCollection
dc.languageeng
dc.publisherF1000 Research Ltd
dc.rightsAttribution 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.titleA step-by-step workflow for low-level analysis of single-cell RNA-seq data with Bioconductor.
dc.typeArticle
prism.publicationDate2016
prism.publicationNameF1000Res
prism.startingPage2122
prism.volume5
dc.identifier.doi10.17863/CAM.32059
dcterms.dateAccepted2016-10-24
rioxxterms.versionofrecord10.12688/f1000research.9501.2
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserved
rioxxterms.licenseref.startdate2016-01
dc.contributor.orcidLun, Aaron [0000-0002-3564-4813]
dc.contributor.orcidMarioni, John [0000-0001-9092-0852]
dc.identifier.eissn1759-796X
rioxxterms.typeJournal Article/Review
cam.issuedOnline2016-10-31


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