Scater: Pre-processing, Quality Control, Normalization and Visualization of Single-cell RNA-seq Data in R

Overview

Journal Bioinformatics

Publisher Oxford University Press

Specialty Biology

Date 2017 Jan 16

PMID 28088763

Citations 813

Authors

Davis J McCarthy

Kieran R Campbell

Aaron T L Lun

Quin F Wills

Affiliations

Soon will be listed here.

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.

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References

Liao Y, Smyth G, Shi W . featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics. 2013; 30(7):923-30. DOI: 10.1093/bioinformatics/btt656. View

Amir E, Davis K, Tadmor M, Simonds E, Levine J, Bendall S . viSNE enables visualization of high dimensional single-cell data and reveals phenotypic heterogeneity of leukemia. Nat Biotechnol. 2013; 31(6):545-52. PMC: 4076922. DOI: 10.1038/nbt.2594. View

Kim J, Kolodziejczyk A, Ilicic T, Illicic T, Teichmann S, Marioni J . Characterizing noise structure in single-cell RNA-seq distinguishes genuine from technical stochastic allelic expression. Nat Commun. 2015; 6:8687. PMC: 4627577. DOI: 10.1038/ncomms9687. View

Chikina M, Zaslavsky E, Sealfon S . CellCODE: a robust latent variable approach to differential expression analysis for heterogeneous cell populations. Bioinformatics. 2015; 31(10):1584-91. PMC: 4426841. DOI: 10.1093/bioinformatics/btv015. View

Leek J . svaseq: removing batch effects and other unwanted noise from sequencing data. Nucleic Acids Res. 2014; 42(21). PMC: 4245966. DOI: 10.1093/nar/gku864. View

Kharchenko P, Silberstein L, Scadden D . Bayesian approach to single-cell differential expression analysis. Nat Methods. 2014; 11(7):740-2. PMC: 4112276. DOI: 10.1038/nmeth.2967. View

Robinson M, Oshlack A . A scaling normalization method for differential expression analysis of RNA-seq data. Genome Biol. 2010; 11(3):R25. PMC: 2864565. DOI: 10.1186/gb-2010-11-3-r25. View

Anders S, Huber W . Differential expression analysis for sequence count data. Genome Biol. 2010; 11(10):R106. PMC: 3218662. DOI: 10.1186/gb-2010-11-10-r106. View

Langmead B, Salzberg S . Fast gapped-read alignment with Bowtie 2. Nat Methods. 2012; 9(4):357-9. PMC: 3322381. DOI: 10.1038/nmeth.1923. View

10.

Scialdone A, Natarajan K, Saraiva L, Proserpio V, Teichmann S, Stegle O . Computational assignment of cell-cycle stage from single-cell transcriptome data. Methods. 2015; 85:54-61. DOI: 10.1016/j.ymeth.2015.06.021. View

11.

Lun A, Bach K, Marioni J . Pooling across cells to normalize single-cell RNA sequencing data with many zero counts. Genome Biol. 2016; 17:75. PMC: 4848819. DOI: 10.1186/s13059-016-0947-7. View

12.

Vallejos C, Richardson S, Marioni J . Beyond comparisons of means: understanding changes in gene expression at the single-cell level. Genome Biol. 2016; 17:70. PMC: 4832562. DOI: 10.1186/s13059-016-0930-3. View

13.

Anders S, Pyl P, Huber W . HTSeq--a Python framework to work with high-throughput sequencing data. Bioinformatics. 2014; 31(2):166-9. PMC: 4287950. DOI: 10.1093/bioinformatics/btu638. View

14.

Yates A, Akanni W, Amode M, Barrell D, Billis K, Carvalho-Silva D . Ensembl 2016. Nucleic Acids Res. 2015; 44(D1):D710-6. PMC: 4702834. DOI: 10.1093/nar/gkv1157. View

15.

Angerer P, Haghverdi L, Buttner M, Theis F, Marr C, Buettner F . destiny: diffusion maps for large-scale single-cell data in R. Bioinformatics. 2015; 32(8):1241-3. DOI: 10.1093/bioinformatics/btv715. View

16.

Grun D, van Oudenaarden A . Design and Analysis of Single-Cell Sequencing Experiments. Cell. 2015; 163(4):799-810. DOI: 10.1016/j.cell.2015.10.039. View

17.

Haghverdi L, Buettner F, Theis F . Diffusion maps for high-dimensional single-cell analysis of differentiation data. Bioinformatics. 2015; 31(18):2989-98. DOI: 10.1093/bioinformatics/btv325. View

18.

Ding B, Zheng L, Zhu Y, Li N, Jia H, Ai R . Normalization and noise reduction for single cell RNA-seq experiments. Bioinformatics. 2015; 31(13):2225-7. PMC: 4481848. DOI: 10.1093/bioinformatics/btv122. View

19.

Teng M, Love M, Davis C, Djebali S, Dobin A, Graveley B . A benchmark for RNA-seq quantification pipelines. Genome Biol. 2016; 17:74. PMC: 4842274. DOI: 10.1186/s13059-016-0940-1. View

20.

Macosko E, Basu A, Satija R, Nemesh J, Shekhar K, Goldman M . Highly Parallel Genome-wide Expression Profiling of Individual Cells Using Nanoliter Droplets. Cell. 2015; 161(5):1202-1214. PMC: 4481139. DOI: 10.1016/j.cell.2015.05.002. View