Power analysis of single-cell RNA-sequencing experiments
All data generated in this study have been deposited in the ArrayExpress database under accession codes E-MTAB-5480, E-MTAB-5481, E-MTAB-5482, E-MTAB-5483, E-MTAB-5484, E-MTAB-5485, and E-MTAB-5486. Summary tables are provided as supplementary files. Single-cell RNA sequencing (scRNA-seq) has become...
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| Published in | Nature methods Vol. 14; no. 4; pp. 381 - 387 |
|---|---|
| Main Authors | , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
New York
Springer Nature
01.04.2017
Nature Publishing Group US Nature Publishing Group |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1548-7091 1548-7105 1548-7105 |
| DOI | 10.1038/nmeth.4220 |
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| Abstract | All data generated in this study have been deposited in the ArrayExpress database under accession codes E-MTAB-5480, E-MTAB-5481, E-MTAB-5482, E-MTAB-5483, E-MTAB-5484, E-MTAB-5485, and E-MTAB-5486. Summary tables are provided as supplementary files.
Single-cell RNA sequencing (scRNA-seq) has become an established and powerful method to investigate transcriptomic cell-to-cell variation, thereby revealing new cell types and providing insights into developmental processes and transcriptional stochasticity. A key question is how the variety of available protocols compare in terms of their ability to detect and accurately quantify gene expression. Here, we assessed the protocol sensitivity and accuracy of many published data sets, on the basis of spike-in standards and uniform data processing. For our workflow, we developed a flexible tool for counting the number of unique molecular identifiers (https://github.com/vals/umis/). We compared 15 protocols computationally and 4 protocols experimentally for batch-matched cell populations, in addition to investigating the effects of spike-in molecular degradation. Our analysis provides an integrated framework for comparing scRNA-seq protocols.
We are grateful to O. Stegle and J.K. Kim for helpful discussions and comments on the manuscript. We thank M. Lynch for support with the C1 experiments, X. Chen for discussions on spike-ins, and M. Quail for help with 10× Chromium experiments. We extend our gratitude to S. Linnarsson and A. Zeisel for invaluable support in implementing STRT-seq in our laboratory and for help with sequencing the STRT library. We also thank D. Grün for sharing smFISH molecule counts. Finally we thank R. Kirchner for many improvements to the umis tool. This study was supported by Cancer Research UK grant C45041/A14953 to A.C. and C.L.; European Research Council project 677501–ZF_Blood to A.C.; a core support grant from the Wellcome Trust and MRC to the Wellcome Trust–Medical Research Council Cambridge Stem Cell Institute; ERC grant ThSWITCH to S.A.T. (grant 260507); and a Lister Institute Research Prize to S.A.T. K.N.N. was supported by the Wellcome Trust Strategic Award ‘Single cell genomics of mouse gastrulation’. We thank P. Liu (Wellcome Trust Sanger Institute) for providing cells. |
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| AbstractList | A comparison framework applied to 15 single-cell RNA-seq protocols reveals differences in accuracy and sensitivity and discusses the utility of RNA spike-in standards.
Single-cell RNA sequencing (scRNA-seq) has become an established and powerful method to investigate transcriptomic cell-to-cell variation, thereby revealing new cell types and providing insights into developmental processes and transcriptional stochasticity. A key question is how the variety of available protocols compare in terms of their ability to detect and accurately quantify gene expression. Here, we assessed the protocol sensitivity and accuracy of many published data sets, on the basis of spike-in standards and uniform data processing. For our workflow, we developed a flexible tool for counting the number of unique molecular identifiers (
https://github.com/vals/umis/
). We compared 15 protocols computationally and 4 protocols experimentally for batch-matched cell populations, in addition to investigating the effects of spike-in molecular degradation. Our analysis provides an integrated framework for comparing scRNA-seq protocols. Single cell RNA sequencing (scRNA-seq) has become an established and powerful method to investigate transcriptomic cell-to-cell variation, revealing new cell types, and providing insights into developmental processes and transcriptional stochasticity. The array of published scRNA-seq protocols allow one to sequence transcriptomes from minute amounts of starting material. A key question is how these various protocols compare in terms of sensitivity of detection of mRNA molecules, and accuracy of quantification of expression. Here, we present an assessment of sensitivity and accuracy of many published data sets by spike-in standards with uniform data processing, including development of a flexible Unique Molecular Identifier (UMI) counting tool ( https://github.com/vals/umis ). We computationally compare 15 protocols, and experimentally assess 4 protocols on batch-matched cell populations, as well as investigating the impact of spike-in molecule degradation on two types of spike-ins. Our analysis provides an integrated framework for comparing different scRNA-seq protocols. Single-cell RNA sequencing (scRNA-seq) has become an established and powerful method to investigate transcriptomic cell-to-cell variation, thereby revealing new cell types and providing insights into developmental processes and transcriptional stochasticity. A key question is how the variety of available protocols compare in terms of their ability to detect and accurately quantify gene expression. Here, we assessed the protocol sensitivity and accuracy of many published data sets, on the basis of spike-in standards and uniform data processing. For our workflow, we developed a flexible tool for counting the number of unique molecular identifiers (https://github.com/vals/umis/). We compared 15 protocols computationally and 4 protocols experimentally for batch-matched cell populations, in addition to investigating the effects of spike-in molecular degradation. Our analysis provides an integrated framework for comparing scRNA-seq protocols. Single-cell RNA sequencing (scRNA-seq) has become an established and powerful method to investigate transcriptomic cell-to-cell variation, thereby revealing new cell types and providing insights into developmental processes and transcriptional stochasticity. A key question is how the variety of available protocols compare in terms of their ability to detect and accurately quantify gene expression. Here, we assessed the protocol sensitivity and accuracy of many published data sets, on the basis of spike-in standards and uniform data processing. For our workflow, we developed a flexible tool for counting the number of unique molecular identifiers (https://github.com/vals/umis/). We compared 15 protocols computationally and 4 protocols experimentally for batch-matched cell populations, in addition to investigating the effects of spike-in molecular degradation. Our analysis provides an integrated framework for comparing scRNA-seq protocols.Single-cell RNA sequencing (scRNA-seq) has become an established and powerful method to investigate transcriptomic cell-to-cell variation, thereby revealing new cell types and providing insights into developmental processes and transcriptional stochasticity. A key question is how the variety of available protocols compare in terms of their ability to detect and accurately quantify gene expression. Here, we assessed the protocol sensitivity and accuracy of many published data sets, on the basis of spike-in standards and uniform data processing. For our workflow, we developed a flexible tool for counting the number of unique molecular identifiers (https://github.com/vals/umis/). We compared 15 protocols computationally and 4 protocols experimentally for batch-matched cell populations, in addition to investigating the effects of spike-in molecular degradation. Our analysis provides an integrated framework for comparing scRNA-seq protocols. All data generated in this study have been deposited in the ArrayExpress database under accession codes E-MTAB-5480, E-MTAB-5481, E-MTAB-5482, E-MTAB-5483, E-MTAB-5484, E-MTAB-5485, and E-MTAB-5486. Summary tables are provided as supplementary files. Single-cell RNA sequencing (scRNA-seq) has become an established and powerful method to investigate transcriptomic cell-to-cell variation, thereby revealing new cell types and providing insights into developmental processes and transcriptional stochasticity. A key question is how the variety of available protocols compare in terms of their ability to detect and accurately quantify gene expression. Here, we assessed the protocol sensitivity and accuracy of many published data sets, on the basis of spike-in standards and uniform data processing. For our workflow, we developed a flexible tool for counting the number of unique molecular identifiers (https://github.com/vals/umis/). We compared 15 protocols computationally and 4 protocols experimentally for batch-matched cell populations, in addition to investigating the effects of spike-in molecular degradation. Our analysis provides an integrated framework for comparing scRNA-seq protocols. We are grateful to O. Stegle and J.K. Kim for helpful discussions and comments on the manuscript. We thank M. Lynch for support with the C1 experiments, X. Chen for discussions on spike-ins, and M. Quail for help with 10× Chromium experiments. We extend our gratitude to S. Linnarsson and A. Zeisel for invaluable support in implementing STRT-seq in our laboratory and for help with sequencing the STRT library. We also thank D. Grün for sharing smFISH molecule counts. Finally we thank R. Kirchner for many improvements to the umis tool. This study was supported by Cancer Research UK grant C45041/A14953 to A.C. and C.L.; European Research Council project 677501–ZF_Blood to A.C.; a core support grant from the Wellcome Trust and MRC to the Wellcome Trust–Medical Research Council Cambridge Stem Cell Institute; ERC grant ThSWITCH to S.A.T. (grant 260507); and a Lister Institute Research Prize to S.A.T. K.N.N. was supported by the Wellcome Trust Strategic Award ‘Single cell genomics of mouse gastrulation’. We thank P. Liu (Wellcome Trust Sanger Institute) for providing cells. |
| Author | Teichmann, Sarah A. Cvejic, Ana Ly, Lam-Ha Miragaia, Ricardo J. Svensson, Valentine Labalette, Charlotte Macaulay, Iain C. Natarajan, Kedar Nath |
| AuthorAffiliation | 3 Wellcome Trust – Medical Research Council Cambridge Stem Cell Institute, Cambridge, UK 2 Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, UK 5 Centre of Biological Engineering, University of Minho, Campus de Gualtar, Braga, Portugal 1 European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, UK 4 Department of Haematology, University of Cambridge, Cambridge, UK |
| AuthorAffiliation_xml | – name: 2 Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, UK – name: 5 Centre of Biological Engineering, University of Minho, Campus de Gualtar, Braga, Portugal – name: 3 Wellcome Trust – Medical Research Council Cambridge Stem Cell Institute, Cambridge, UK – name: 1 European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, UK – name: 4 Department of Haematology, University of Cambridge, Cambridge, UK |
| Author_xml | – sequence: 1 fullname: Svensson, Valentine – sequence: 2 fullname: Natarajan, Kedar Nath – sequence: 3 fullname: Ly, Lam-Ha – sequence: 4 fullname: Miragaia, Ricardo J. – sequence: 5 fullname: Labalette, Charlotte – sequence: 6 fullname: Macaulay, Iain C. – sequence: 7 fullname: Cvejic, Ana – sequence: 8 fullname: Teichmann, Sarah A. |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/28263961$$D View this record in MEDLINE/PubMed |
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| Snippet | All data generated in this study have been deposited in the ArrayExpress database under accession codes E-MTAB-5480, E-MTAB-5481, E-MTAB-5482, E-MTAB-5483,... A comparison framework applied to 15 single-cell RNA-seq protocols reveals differences in accuracy and sensitivity and discusses the utility of RNA spike-in... Single-cell RNA sequencing (scRNA-seq) has become an established and powerful method to investigate transcriptomic cell-to-cell variation, thereby revealing... Single cell RNA sequencing (scRNA-seq) has become an established and powerful method to investigate transcriptomic cell-to-cell variation, revealing new cell... |
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| SubjectTerms | 45/91 631/208/199 631/337/2019 631/61/514/1949 analysis Animals Bioinformatics Biological Microscopy Biological Techniques Biomedical Engineering/Biotechnology Data processing Embryonic Stem Cells - physiology Freezing Gene expression Gene sequencing Life Sciences Mice Poly A Proteomics Protocol (computers) Ribonucleic acid RNA RNA sequencing RNA, Messenger Science & Technology Sensitivity analysis Sensitivity and Specificity Sequence Analysis, RNA - methods Sequence Analysis, RNA - standards Sequence Analysis, RNA - statistics & numerical data Single-Cell Analysis - methods Single-Cell Analysis - standards Single-Cell Analysis - statistics & numerical data Stochasticity Transcription Transcriptomics Workflow |
| Title | Power analysis of single-cell RNA-sequencing experiments |
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