Analysis | Published:

Power analysis of single-cell RNA-sequencing experiments

Nature Methods volume 14, pages 381387 (2017) | Download Citation

Abstract

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.

Access optionsAccess options

Rent or Buy article

Get time limited or full article access on ReadCube.

from $8.99

All prices are NET prices.

Accessions

References

  1. 1.

    & Single cell genomics: advances and future perspectives. PLoS Genet. 10, e1004126 (2014).

  2. 2.

    , & Computational and analytical challenges in single-cell transcriptomics. Nat. Rev. Genet. 16, 133–145 (2015).

  3. 3.

    et al. Quantitative assessment of single-cell RNA-sequencing methods. Nat. Methods 11, 41–46 (2014).

  4. 4.

    et al. Comparative analysis of single-cell RNA sequencing methods. Preprint at (2016).

  5. 5.

    External RNA Controls Consortium. Proposed methods for testing and selecting the ERCC external RNA controls. BMC Genomics 6, 150 (2005).

  6. 6.

    et al. Synthetic spike-in standards for RNA-seq experiments. Genome Res. 21, 1543–1551 (2011).

  7. 7.

    et al. Assessing technical performance in differential gene expression experiments with external spike-in RNA control ratio mixtures. Nat. Commun. 5, 5125 (2014).

  8. 8.

    , , & CEL-Seq: single-cell RNA-Seq by multiplexed linear amplification. Cell Rep. 2, 666–673 (2012).

  9. 9.

    et al. Quantitative single-cell RNA-seq with unique molecular identifiers. Nat. Methods 11, 163–166 (2014).

  10. 10.

    , & Molecular dissection of mRNA poly(A) tail length control in yeast. Nucleic Acids Res. 36, 2418–2433 (2008).

  11. 11.

    , & Validation of noise models for single-cell transcriptomics. Nat. Methods 11, 637–640 (2014).

  12. 12.

    & Understanding equivalence and noninferiority testing. J. Gen. Intern. Med. 26, 192–196 (2011).

  13. 13.

    SEQC/MAQC-III Consortium. A comprehensive assessment of RNA-seq accuracy, reproducibility and information content by the Sequencing Quality Control Consortium. Nat. Biotechnol. 32, 903–914 (2014).

  14. 14.

    , , & Incorporation of non-natural nucleotides into template-switching oligonucleotides reduces background and improves cDNA synthesis from very small RNA samples. BMC Genomics 11, 413 (2010).

  15. 15.

    et al. Single-cell RNA sequencing reveals T helper cells synthesizing steroids de novo to contribute to immune homeostasis. Cell Rep. 7, 1130–1142 (2014).

  16. 16.

    et al. Computational analysis of cell-to-cell heterogeneity in single-cell RNA-sequencing data reveals hidden subpopulations of cells. Nat. Biotechnol. 33, 155–160 (2015).

  17. 17.

    et al. Low-coverage single-cell mRNA sequencing reveals cellular heterogeneity and activated signaling pathways in developing cerebral cortex. Nat. Biotechnol. 32, 1053–1058 (2014).

  18. 18.

    et al. Reconstructing lineage hierarchies of the distal lung epithelium using single-cell RNA-seq. Nature 509, 371–375 (2014).

  19. 19.

    et al. Smart-seq2 for sensitive full-length transcriptome profiling in single cells. Nat. Methods 10, 1096–1098 (2013).

  20. 20.

    et al. Brain structure. Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq. Science 347, 1138–1142 (2015).

  21. 21.

    et al. Massively parallel single-cell RNA-seq for marker-free decomposition of tissues into cell types. Science 343, 776–779 (2014).

  22. 22.

    et al. Silencing of odorant receptor genes by G protein βγ signaling ensures the expression of one odorant receptor per olfactory sensory neuron. Neuron 81, 847–859 (2014).

  23. 23.

    et al. Measuring absolute RNA copy numbers at high temporal resolution reveals transcriptome kinetics in development. Cell Rep. 14, 632–647 (2016).

  24. 24.

    et al. Single-cell transcriptomics reveals a population of dormant neural stem cells that become activated upon brain injury. Cell Stem Cell 17, 329–340 (2015).

  25. 25.

    et al. Single-cell RNA-seq transcriptome analysis of linear and circular RNAs in mouse preimplantation embryos. Genome Biol. 16, 148 (2015).

  26. 26.

    et al. Tracing the expression of circular RNAs in human pre-implantation embryos. Genome Biol. 17, 130 (2016).

  27. 27.

    et al. Single-cell polyadenylation site mapping reveals 3′ isoform choice variability. Mol. Syst. Biol. 11, 812 (2015).

  28. 28.

    et al. CEL-Seq2: sensitive highly-multiplexed single-cell RNA-Seq. Genome Biol. 17, 77 (2016).

  29. 29.

    et al. Transcriptional heterogeneity and lineage commitment in myeloid progenitors. Cell 163, 1663–1677 (2015).

  30. 30.

    et al. Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell 161, 1202–1214 (2015).

  31. 31.

    et al. Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells. Cell 161, 1187–1201 (2015).

  32. 32.

    et al. G&T-seq: parallel sequencing of single-cell genomes and transcriptomes. Nat. Methods 12, 519–522 (2015).

  33. 33.

    et al. Computational assignment of cell-cycle stage from single-cell transcriptome data. Methods 85, 54–61 (2015).

  34. 34.

    et al. Single mammalian cells compensate for differences in cellular volume and DNA copy number through independent global transcriptional mechanisms. Mol. Cell 58, 339–352 (2015).

  35. 35.

    et al. Population and single-cell genomics reveal the Aire dependency, relief from Polycomb silencing, and distribution of self-antigen expression in thymic epithelia. Genome Res. 24, 1918–1931 (2014).

  36. 36.

    et al. Combined single-cell functional and gene expression analysis resolves heterogeneity within stem cell populations. Cell Stem Cell 16, 712–724 (2015).

  37. 37.

    et al. Microfluidic single-cell whole-transcriptome sequencing. Proc. Natl. Acad. Sci. USA 111, 7048–7053 (2014).

  38. 38.

    et al. The transcriptome and DNA methylome landscapes of human primordial germ cells. Cell 161, 1437–1452 (2015).

  39. 39.

    et al. Massively parallel digital transcriptional profiling of single cells. Preprint at (2016).

  40. 40.

    et al. Single-cell transcriptome analysis reveals coordinated ectopic gene-expression patterns in medullary thymic epithelial cells. Nat. Immunol. 16, 933–941 (2015).

  41. 41.

    , , , & Salmon provides accurate, fast, and bias-aware transcript expression estimates using dual-phase inference. Preprint at (2015).

  42. 42.

    , , & RapMap: a rapid, sensitive and accurate tool for mapping RNA-seq reads to transcriptomes. Bioinformatics 32, i192–i200 (2016).

  43. 43.

    , , & Near-optimal probabilistic RNA-seq quantification. Nat. Biotechnol. 34, 525–527 (2016).

  44. 44.

    et al. Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011).

  45. 45.

    , , , & Stan: A probabilistic programming language. J. Stat. Softw. 76, 1–32 (2017).

Download references

Acknowledgements

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 information

Author notes

    • Valentine Svensson
    •  & Kedar Nath Natarajan

    These authors contributed equally to this work.

Affiliations

  1. European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridge, UK.

    • Valentine Svensson
    • , Kedar Nath Natarajan
    •  & Sarah A Teichmann
  2. Wellcome Trust Sanger Institute, Hinxton, Cambridge, UK.

    • Valentine Svensson
    • , Kedar Nath Natarajan
    • , Lam-Ha Ly
    • , Ricardo J Miragaia
    • , Charlotte Labalette
    • , Iain C Macaulay
    • , Ana Cvejic
    •  & Sarah A Teichmann
  3. Centre of Biological Engineering, University of Minho, Braga, Portugal.

    • Ricardo J Miragaia
  4. Wellcome Trust–Medical Research Council Cambridge Stem Cell Institute, Cambridge, UK.

    • Charlotte Labalette
    •  & Ana Cvejic
  5. Department of Haematology, University of Cambridge, Cambridge, UK.

    • Charlotte Labalette
    •  & Ana Cvejic

Authors

  1. Search for Valentine Svensson in:

  2. Search for Kedar Nath Natarajan in:

  3. Search for Lam-Ha Ly in:

  4. Search for Ricardo J Miragaia in:

  5. Search for Charlotte Labalette in:

  6. Search for Iain C Macaulay in:

  7. Search for Ana Cvejic in:

  8. Search for Sarah A Teichmann in:

Contributions

V.S. and S.A.T. conceived the study. V.S. and L.-H.L. annotated and processed all data. V.S. conceived and implemented the umis tool. V.S. conceived and performed the performance modeling of the data. V.S., R.J.M., and K.N.N. designed the in-house experiments. K.N.N. optimized and implemented the protocols. The degradation experiments were designed by V.S., I.C.M., R.J.M., and K.N.N., who performed the experiments. I.C.M. and C.L. performed zebrafish Smart-seq2 experiments under the supervision of A.C. V.S. and L.H.L. designed the degradation model, and L.H.L. implemented the model. V.S., K.N.N., and S.A.T. wrote the manuscript.

Competing interests

The authors declare no competing financial interests.

Corresponding authors

Correspondence to Valentine Svensson or Sarah A Teichmann.

Integrated supplementary information

Supplementary information

PDF files

  1. 1.

    Supplementary Text and Figures

    Supplementary Figures 1–3

Excel files

  1. 1.

    Supplementary Table 1

    Descriptive summaries of the public studies used for the comparison

CSV files

  1. 1.

    Supplementary Table 2

    Full data table of technical parameters for each sample used for comparison and generation of all figures

Zip files

  1. 1.

    Supplementary Software

    Umis version 0.3.0, which we used for processing all UMI data. See https://github.com/vals/umis for updated versions

About this article

Publication history

Received

Accepted

Published

DOI

https://doi.org/10.1038/nmeth.4220