PT  - JOURNAL ARTICLE
AU  - Davis J. McCarthy
AU  - Kieran R. Campbell
AU  - Aaron T. L. Lun
AU  - Quin F. Wills
TI  - <em>scater:</em> pre-processing, quality control, normalisation and visualisation of single-cell RNA-seq data in R
AID  - 10.1101/069633
DP  - 2016 Jan 01
TA  - bioRxiv
PG  - 069633
4099  - http://biorxiv.org/content/early/2016/08/15/069633.short
4100  - http://biorxiv.org/content/early/2016/08/15/069633.full
AB  - 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 normalisation.Results We have developed the R/Bioconductor package scater to facilitate rigorous pre-processing, quality control, normalisation and visualisation 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 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 material is available online at bioRxiv accompanying this manuscript, and all materials required to reproduce the results presented in this paper are available at dx.doi.org/10.5281/zenodo.60139.