Abstract
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.
Keywords
Normalization (sociology)VisualizationComputer scienceRNA-SeqSoftwareComputational biologyData miningBiologyTranscriptomeGeneticsGene expressionProgramming languageGene
MeSH Terms
Cell LineHumansPrincipal Component AnalysisProgramming LanguagesQuality ControlRNASequence AnalysisRNASingle-Cell AnalysisSoftwareStatistics as Topic
Affiliated Institutions
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Publication Info
- Year
- 2016
- Type
- article
- Volume
- 33
- Issue
- 8
- Pages
- 1179-1186
- Citations
- 1915
- Access
- Closed
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Cite This
Davis J. McCarthy,
Kieran R. Campbell,
Aaron T. L. Lun
et al.
(2016).
Scater: pre-processing, quality control, normalization and visualization of single-cell RNA-seq data in R.
Bioinformatics
, 33
(8)
, 1179-1186.
https://doi.org/10.1093/bioinformatics/btw777
Identifiers
- DOI
- 10.1093/bioinformatics/btw777
- PMID
- 28088763
- PMCID
- PMC5408845