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

TLDR

Single‑cell RNA sequencing is widely used to profile gene expression at single‑cell resolution, but raw data contain biases and artifacts that make preprocessing, quality control, and normalization laborious. scater is an R/Bioconductor package that provides a flexible workflow to preprocess, QC, normalize, visualize scRNA‑seq data, and offers a compatible data structure for downstream tools. Using scater, researchers can streamline the preprocessing, QC, normalization, and visualization steps, substantially reducing the time and effort required to produce high‑quality expression datasets. The open‑source code, vignettes, and case studies are available on Bioconductor (http://bioconductor.org/packages/scater), with supplementary data online and contact email [e-mail].

Abstract

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 [e-mail] data are available at Bioinformatics online.