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dc.contributor.authorMcCarthy, DJ
dc.contributor.authorCampbell, KR
dc.contributor.authorLun, ATL
dc.contributor.authorWills, QF
dc.date.accessioned2020-12-10T00:18:38Z
dc.date.available2020-12-10T00:18:38Z
dc.date.issued2017-04-15
dc.identifierpii: btw777
dc.identifier.citationMcCarthy, D. J., Campbell, K. R., Lun, A. T. L. & Wills, Q. F. (2017). Scater: pre-processing, quality control, normalization and visualization of single-cell RNA-seq data in R. BIOINFORMATICS, 33 (8), pp.1179-1186. https://doi.org/10.1093/bioinformatics/btw777.
dc.identifier.issn1367-4803
dc.identifier.urihttp://hdl.handle.net/11343/253455
dc.description.abstractMotivation: Single-cell RNA sequencing (scRNA-seq) is increasingly used to study gene expression at the level of individual cells. However, preparing raw sequence data for further analysis is not a straightforward process. Biases, artifacts and other sources of unwanted variation are present in the data, requiring substantial time and effort to be spent on pre-processing, quality control (QC) and normalization. Results: We have developed the R/Bioconductor package scater to facilitate rigorous pre-processing, quality control, normalization and visualization of scRNA-seq data. The package provides a convenient, flexible workflow to process raw sequencing reads into a high-quality expression dataset ready for downstream analysis. scater provides a rich suite of plotting tools for single-cell data and a flexible data structure that is compatible with existing tools and can be used as infrastructure for future software development. Availability and Implementation: The open-source code, along with installation instructions, vignettes and case studies, is available through Bioconductor at http://bioconductor.org/packages/scater . Contact: davis@ebi.ac.uk. Supplementary information: Supplementary data are available at Bioinformatics online.
dc.languageEnglish
dc.publisherOXFORD UNIV PRESS
dc.rights.urihttps://creativecommons.org/licenses/by/4.0
dc.titleScater: pre-processing, quality control, normalization and visualization of single-cell RNA-seq data in R
dc.typeJournal Article
dc.identifier.doi10.1093/bioinformatics/btw777
melbourne.affiliation.departmentSchool of Mathematics and Statistics
melbourne.source.titleBioinformatics
melbourne.source.volume33
melbourne.source.issue8
melbourne.source.pages1179-1186
dc.rights.licenseCC BY
melbourne.elementsid1365136
melbourne.contributor.authorMcCarthy, Davis
dc.identifier.eissn1460-2059
melbourne.accessrightsOpen Access


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