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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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and Implementation<\/jats:title>\n                    <jats:p>The open-source code, along with installation instructions, vignettes and case studies, is available through Bioconductor at http:\/\/bioconductor.org\/packages\/scater.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Supplementary information<\/jats:title>\n                    <jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btw777","type":"journal-article","created":{"date-parts":[[2016,12,8]],"date-time":"2016-12-08T07:05:45Z","timestamp":1481180745000},"page":"1179-1186","source":"Crossref","is-referenced-by-count":1747,"title":["Scater: pre-processing, quality control, normalization and visualization of single-cell RNA-seq data in R"],"prefix":"10.1093","volume":"33","author":[{"given":"Davis J","family":"McCarthy","sequence":"first","affiliation":[{"name":"European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK"},{"name":"Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK"},{"name":"St Vincent\u2019s Institute of Medical Research, Fitzroy, Victoria, Australia"}]},{"given":"Kieran R","family":"Campbell","sequence":"additional","affiliation":[{"name":"Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK"},{"name":"Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford, UK"}]},{"given":"Aaron T L","family":"Lun","sequence":"additional","affiliation":[{"name":"CRUK Cambridge Institute, University of Cambridge, Cambridge, UK"}]},{"given":"Quin F","family":"Wills","sequence":"additional","affiliation":[{"name":"Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK"},{"name":"Weatherall Institute for Molecular Medicine, University of Oxford, John Radcliffe Hospital, Oxford, UK"}]}],"member":"286","published-online":{"date-parts":[[2017,1,14]]},"reference":[{"key":"2023020205012088800_btw777-B1","doi-asserted-by":"crossref","first-page":"545","DOI":"10.1038\/nbt.2594","article-title":"viSNE enables visualization of high dimensional single-cell data and reveals phenotypic heterogeneity of leukemia","volume":"31","author":"Amir","year":"2013","journal-title":"Nat. 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