Analysis | Published:

Power analysis of single-cell RNA-sequencing experiments

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


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 ( 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.

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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.


  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


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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

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    Supplementary Software

    Umis version 0.3.0, which we used for processing all UMI data. See for updated versions

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