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

By Davis J. McCarthy, Kieran R. Campbell, Aaron T. L. Lun, Quin F. Wills. Bioinform., 2017

1,604 citations recorded by the source corpus.

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.

Subfields: Biophysics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/98b9fde16d450f858bae75dfc466f2009083a9f0

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Citations in the corpus

Current best practices in single‐cell RNA‐seq analysis: a tutorial (2019, background): “Finally, it has been argued that variation in cell size accounts for the transcriptomic effect generally attributed to the cell cycle (McDavid et al , 2016).”
Structural Remodeling of the Human Colonic Mesenchyme in Inflammatory Bowel Disease (2018, methodology): “These and other QC outputs generated by the pipeline tools above were collated using multiQC, imported together with gene count tables into R, and assembled into an SCESet object using the package scater (McCarthy et al., 2017).”
Single-Cell RNA-Seq Technologies and Related Computational Data Analysis (2019, methodology): “Some QC methods for scRNA-seq have been proposed (Stegle et al., 2015; Ilicic et al., 2016), including SinQC (Jiang et al., 2016) and Scater (McCarthy et al., 2017), these tools are useful for QC of scRNA-seq data.”
Splatter: simulation of single-cell RNA sequencing data (2017, background): “Scater is a low-level analysis package that provides various functions 5 for quality control, visualization and preprocessing of scRNA-seq data [17].”
edgeR v4: powerful differential analysis of sequencing data with expanded functionality and improved support for small counts and larger datasets (2024, neutral): “Single-cell technology has revolutionized biomedical research, and many R packages and software tools have been developed for analyzing scRNA-seq data in the past few years [ 49 , 71 , 72 ].”
Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches (2022, neutral): “PZM6J Software and algorithms Adobe Illustrator Adobe www.adobe.com Cell Ranger 10X Genomics https://support.10xgenomics.com/single- cell-gene-expression/software/pipelines/ latest/what-is-cell-ranger FastCAR R Package N/A https://github.com/LungCellAtlas/FastCAR Seurat R Package V3 (Stuart et al., ”
Benchmarking spatial and single-cell transcriptomics integration methods for transcript distribution prediction and cell type deconvolution (2022, methodology): “To simulate expression matrices with ‘high sparsity,’ we adopted Splatter 65 and Scuttle 66 to down-sample the non-zero elements from the original expression matrices to varying extents (Methods).”