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

Journal article

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.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.The open-source code, along with installation instructions, vignettes and case studies, is available through Bioconductor at http://bioconductor.org/packages/scater [email protected] data are available at Bioinformatics online.

Authors: McCarthy, D; Campbell, K; Lun, A; Wills, Q

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Oxford University Press
• Journal: Bioinformatics More from this journal
• Volume: 33
• Issue: 8
• Pages: 1179-1186
• Publication date: 2017-01-14
• Acceptance date: 2016-12-07
• DOI: 10.1093/bioinformatics/btw777
• EISSN: 1367-4811
• ISSN: 1367-4803
• Pmid: 28088763
• Language: English
• Keywords: programming languages; sequence analysis, RNA; software; humans; single-cell analysis; cell line; statistics as topic; quality control; RNA; principal component analysis