Cited by 1,852Single-cell and spatial transcriptomicsGenomics and Phylogenetic StudiesGene expression and cancer classification

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Scater: pre-processing, quality control, normalization and visualization of single-cell RNA-seq data in R

Open access

McCarthy, Davis J, Campbell, Kieran R, Lun, Aaron T L, Wills, Quin F·2017·Bioinformatics

DOI 10.1093/bioinformatics/btw777PMC PMC5408845

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Abstract

Motivation: 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. Supplementary information: Supplementary data are available at Bioinformatics online.

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Authors: McCarthy, D. J., Campbell, K. R., Lun, A. T. L., & Wills, Q. F.
Affiliations: European Bioinformatics Institute (GB)
Centre for Human Genetics (GB)
University of Cambridge (GB)
Corresponding author: McCarthy, Davis J
ORCID: McCarthy, Davis J
Campbell, Kieran R
Lun, Aaron T L
Venue: Bioinformatics
Publisher: Oxford University Press (OUP)
Published: 2017-01-14
Volume: 33
Issue: 8
Pages: 1179-1186
ISSN: 1367-4803 (ISSN-L)
1367-4811
DOI: 10.1093/bioinformatics/btw777
PMC: PMC5408845
OpenAlex: W2951158909
Cited by: 1,852
References: 63
Citations by year: 2026: 188 · 2025: 247 · 2024: 261 · 2023: 275 · 2022: 248 · 2021: 213 · 2020: 163 · 2019: 142 · 2018: 88 · 2017: 22
FWCI: 44.02
Type: journal article
Language: en
Open access: hybrid
doi.org
Licence: cc-by
creativecommons.org/licenses/by/4.0/ (vor, 1 d)
Locations: Bioinformatics (publishedVersion, cc-by)
bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
PubMed Central (publishedVersion, cc-by)
PubMed (publishedVersion)
Minerva Access (University of Melbourne) (submittedVersion, cc-by)
Apollo (University of Cambridge) (submittedVersion, cc-by)
Apollo (cc-by)
Funders: National Health and Medical Research Council (APP1112, 681 to D.J.M.)
European Molecular Biology Laboratory (D.J.M.)
Cancer Research UK (A1719, 7 to A.T.L.L.)
United Kingdom Medical Research Council (K.R.C.)
Oxford Single Cell Biology Consortium (Q.F.W.)
Topics: Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
Keywords: single-cell RNA sequencing, R/Bioconductor package, data preprocessing, quality control, normalization, data visualization, gene expression data, sequencing artifacts
MeSH terms: Cell Line, Humans, Programming Languages, Quality Control, RNA, Software, Statistics as Topic, Sequence Analysis, RNA, Principal Component Analysis, Single-Cell Analysis

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Scater: pre-processing, quality control, normalization and visualization of single-cell RNA-seq data in R

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