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

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          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@ 123456ebi.ac.uk

          Supplementary information: Supplementary data are available at Bioinformatics online.

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          Single-cell transcriptomics reveals bimodality in expression and splicing in immune cells

          Alex Shalek, Rahul Satija, Xian Adiconis … (2013)
          Recent molecular studies have revealed that, even when derived from a seemingly homogenous population, individual cells can exhibit substantial differences in gene expression, protein levels, and phenotypic output 1–5 , with important functional consequences 4,5 . Existing studies of cellular heterogeneity, however, have typically measured only a few pre-selected RNAs 1,2 or proteins 5,6 simultaneously because genomic profiling methods 3 could not be applied to single cells until very recently 7–10 . Here, we use single-cell RNA-Seq to investigate heterogeneity in the response of bone marrow derived dendritic cells (BMDCs) to lipopolysaccharide (LPS). We find extensive, and previously unobserved, bimodal variation in mRNA abundance and splicing patterns, which we validate by RNA-fluorescence in situ hybridization (RNA-FISH) for select transcripts. In particular, hundreds of key immune genes are bimodally expressed across cells, surprisingly even for genes that are very highly expressed at the population average. Moreover, splicing patterns demonstrate previously unobserved levels of heterogeneity between cells. Some of the observed bimodality can be attributed to closely related, yet distinct, known maturity states of BMDCs; other portions reflect differences in the usage of key regulatory circuits. For example, we identify a module of 137 highly variable, yet co-regulated, antiviral response genes. Using cells from knockout mice, we show that variability in this module may be propagated through an interferon feedback circuit involving the transcriptional regulators Stat2 and Irf7. Our study demonstrates the power and promise of single-cell genomics in uncovering functional diversity between cells and in deciphering cell states and circuits.
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            Accounting for technical noise in single-cell RNA-seq experiments.

            Philip Brennecke, Simon Anders, Jong Kim … (2013)
            Single-cell RNA-seq can yield valuable insights about the variability within a population of seemingly homogeneous cells. We developed a quantitative statistical method to distinguish true biological variability from the high levels of technical noise in single-cell experiments. Our approach quantifies the statistical significance of observed cell-to-cell variability in expression strength on a gene-by-gene basis. We validate our approach using two independent data sets from Arabidopsis thaliana and Mus musculus.
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              Classification of low quality cells from single-cell RNA-seq data

              Tomislav Ilicic, Jong Kim, Aleksandra A. Kolodziejczyk … (2016)
              Single-cell RNA sequencing (scRNA-seq) has broad applications across biomedical research. One of the key challenges is to ensure that only single, live cells are included in downstream analysis, as the inclusion of compromised cells inevitably affects data interpretation. Here, we present a generic approach for processing scRNA-seq data and detecting low quality cells, using a curated set of over 20 biological and technical features. Our approach improves classification accuracy by over 30 % compared to traditional methods when tested on over 5,000 cells, including CD4+ T cells, bone marrow dendritic cells, and mouse embryonic stem cells. Electronic supplementary material The online version of this article (doi:10.1186/s13059-016-0888-1) contains supplementary material, which is available to authorized users.
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                Author and article information

                Journal
                Journal ID (nlm-ta): Bioinformatics
                Journal ID (iso-abbrev): Bioinformatics
                Journal ID (publisher-id): bioinformatics
                Title: Bioinformatics
                Publisher: Oxford University Press
                ISSN (Print): 1367-4803
                ISSN (Electronic): 1367-4811
                Publication date (Print): 15 April 2017
                Publication date (Electronic): 14 January 2017
                Publication date PMC-release: 14 January 2017
                Volume: 33
                Issue: 8
                Pages: 1179-1186
                Affiliations
                [1 ]European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, CB10 1SD Hinxton, Cambridge, UK
                [2 ]Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK
                [3 ]St Vincent’s Institute of Medical Research, Fitzroy, Victoria 3065, Australia
                [4 ]Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford OX1 3QX, UK
                [5 ]CRUK Cambridge Institute, University of Cambridge, Cambridge CB2 0RE, UK
                [6 ]Weatherall Institute for Molecular Medicine, University of Oxford, John Radcliffe Hospital, Oxford OX3 9DS, UK
                Author notes
                [* ]To whom correspondence should be addressed.

                Associate Editor: Ivo Hofacker

                Article
                Publisher ID: btw777
                DOI: 10.1093/bioinformatics/btw777
                PMC ID: 5408845
                PubMed ID: 28088763
                SO-VID: f26c247d-d6eb-4285-a4ea-336530c112e4
                Copyright © © The Author 2017. Published by Oxford University Press.
                License:

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                Date received : 15 August 2016
                Date revision received : 7 November 2016
                Date accepted : 7 December 2016
                Page count
                Pages: 8
                Funding
                Funded by: National Health and Medical Research Council of Australia
                Award ID: APP1112
                Award ID: 681 to D.J.M.
                Funded by: European Molecular Biology Laboratory
                Award ID: D.J.M.
                Funded by: Cancer Research UK
                Award ID: A1719
                Award ID: 7 to A.T.L.L.
                Funded by: United Kingdom Medical Research Council
                Award ID: K.R.C.
                Funded by: Oxford Single Cell Biology Consortium
                Award ID: Q.F.W.
                Categories
                Subject: Original Papers
                Subject: Gene Expression

                ScienceOpen disciplines: Bioinformatics & Computational biology
                Data availability:
                ScienceOpen disciplines: Bioinformatics & Computational biology

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