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

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

Published version of a preprint

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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Crossref · 10.1093/bioinformatics/btw777

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  • In the manually sorted olfactory bulb dopaminergic neuron dataset, cell-level quality control and filtering was performed in scater.

    “Cell level quality control and cell filtering was performed in scater (112).”
  • In the analysis of scSeq datasets from various brain regions and sensory systems, inspection of all datasets for batch effects was performed using the scater package.

    “Inspection of all datasets for batch effects was performed using the scater package (version 1.10.1) (112).”
  • Popular platforms such as Seurat, Scater, or Scanpy limit themselves to tools developed in their respective programming languages.

    “Popular platforms such as Seurat (Butler et al, 2018), Scater (McCarthy et al, 2017), or Scanpy (Wolf et al, 2018) provide integrated environments to develop pipelines and contain large analysis toolboxes. However, out of necessity these platforms limit themselves to tools developed in their respective programming languages.”

    From Current best practices in single-cell RNA-seq analysis: a tutorial (2019) · cites this paper as McCarthy et al, 2017

  • Among command line platforms, Scater and Seurat easily interface with the large variety of analysis tools available via the R Bioconductor project.

    “Among command line platforms, Scater (McCarthy et al, 2017) and Seurat (Butler et al, 2018) easily interface with the large variety of analysis tools available via the R Bioconductor project (Huber et al, 2015).”

    From Current best practices in single-cell RNA-seq analysis: a tutorial (2019) · cites this paper as McCarthy et al, 2017

  • Currently available platforms exist on the command line in R or Python, and as local applications or Web servers with graphical user interfaces (GUIs).

    “Currently available platforms exist on the command line in R (McCarthy et al, 2017; Butler et al, 2018) or Python (Wolf et al, 2018), and as local applications (Patel, 2018; preprint: Scholz et al, 2018) or Web servers (Gardeux et al, 2017; Zhu et al, 2017) with graphical user interfaces (GUIs).”

    From Current best practices in single-cell RNA-seq analysis: a tutorial (2019) · cites this paper as McCarthy et al, 2017

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Research works printed but not linked: 2

  1. Campbell K., Yau C. (2016) Ouija: Incorporating prior knowledge in single-cell trajectory learning using Bayesian nonlinear factor analysis. bioRxiv, 060442.
  2. Hicks S.C. et al (2015) On the widespread and critical impact of systematic bias and batch effects in single-cell RNA-Seq data. bioRxiv, 025528.

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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
Venue
Bioinformatics
Published
2017-01-14
Volume
33
Issue
8
Pages
1179-1186
ISSN
  • 1367-4803 (ISSN-L)
  • 1367-4811
OpenAlex
W2951158909
Cited by
1,852
References
56
Citations by year
2026: 188 · 2025: 247 · 2024: 261 · 2023: 275 · 2022: 248 · 2021: 213 · 2020: 163 · 2019: 142 · 2018: 88 · 2017: 22 · 2016: 2 · 2014: 1 · 2012: 1
FWCI
44.02
Type
journal article
Language
en
Open access
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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