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Bioinformatics. 2017 Apr 15;33(8):1179-1186. doi: 10.1093/bioinformatics/btw777.

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

Author information

1
European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, CB10 1SD Hinxton, Cambridge, UK.
2
Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK.
3
St Vincent's Institute of Medical Research, Fitzroy, Victoria 3065, Australia.
4
Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford OX1 3QX, UK.
5
CRUK Cambridge Institute, University of Cambridge, Cambridge CB2 0RE, UK.
6
Weatherall Institute for Molecular Medicine, University of Oxford, John Radcliffe Hospital, Oxford OX3 9DS, UK.

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 .

Contact:

davis@ebi.ac.uk.

Supplementary information:

Supplementary data are available at Bioinformatics online.

PMID:
28088763
PMCID:
PMC5408845
DOI:
10.1093/bioinformatics/btw777
[Indexed for MEDLINE]
Free PMC Article

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