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Scater: pre-processing, quality control, normalization and visualization of single-cell RNA-seq data in R
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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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“Cell level quality control and cell filtering was performed in scater (112).”
From Non-neuronal expression of SARS-CoV-2 entry genes in the olfactory system suggests mechanisms underlying COVID-19-associated anosmia (2020) · cites this paper as [112]
In the citing paper
“Inspection of all datasets for batch effects was performed using the scater package (version 1.10.1) (112).”
From Non-neuronal expression of SARS-CoV-2 entry genes in the olfactory system suggests mechanisms underlying COVID-19-associated anosmia (2020) · cites this paper as [112]
In the citing paper
“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
In the citing paper
“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
In the citing paper
“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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Metadata
- 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
- Publisher
- Oxford University Press (OUP)
- Published
- 2017-01-14
- Volume
- 33
- Issue
- 8
- Pages
- 1179-1186
- ISSN
- 1367-4803 (ISSN-L)
- 1367-4811
- 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
- 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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