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Power analysis of single-cell RNA-sequencing experiments

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.

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Figure 1: Strategy for scRNA-seq protocol comparison.
Figure 2: Performance metrics for scRNA-seq protocols.
Figure 3: Performance metrics after accounting for sequencing depth.
Figure 4: Effects of various factors on performance metrics.

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ArrayExpress

Referenced accessions

ArrayExpress

European Nucleotide Archive

Gene Expression Omnibus

Sequence Read Archive

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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

Authors and Affiliations

Authors

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.

Corresponding authors

Correspondence to Valentine Svensson or Sarah A Teichmann.

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Competing interests

The authors declare no competing financial interests.

Integrated supplementary information

Supplementary Figure 1 Comparison and overview of spike-in sets.

ERCC spike-ins consist of 92 very distinct sequences based on bacterial genes logarithmically distributed across 22 abundance levels (in Mix 1), with poly-A tails ranging from 20 to 26 base pairs. SIRV spike-ins are 69 sequences, modeled after sequences and splicing patterns in 7 human genes. In Mix 2, which we used, the SIRV molecules are present at 4 abundance levels, with virtual alternative isoforms from each gene present at each abundance level. All SIRV molecules have 30 base pair long poly-A tails.

Supplementary Figure 2 UMI efficiency as an alternative metric of sensitivity.

(A) Assuming that UMI counts correspond to a count of the fraction of molecules successfully captured by the RNA-sequencing process, in log-log space the efficiency corresponds to the offset from perfect correspondence between input molecules and counted UMIs. (B) With the exception of data from the MARS-Seq protocol, spike-in detection limits correspond well with UMI efficiency measures. The spike-in detection limit can however also be used for coverage based data quantified by TPM. (C) The assumption with UMI counting as a quantitative measurement is that efficiency is the only factor determining differences between real counts and observed counts. However, fitting a model with a non-one exponent on the number of input molecules shows this is almost in all cases < 1. This means UMI counts underestimate expression of highly expressed genes. (D) The saturation of UMI counts can be partially explained by short UMIs. If an experiment uses too short UMIs, eventually the number of possible observable UMIs plateau. However, even for very long UMIs, such as 10 base pairs, the mean molecule exponent is 0.8, indicating some additional unexplained factor is causing a saturation of UMI counts. (E) Averaged efficiency comparison of endogenous genes and ERCC spike-ins. The data by Grun et al had smFISH measurements for 9 genes in the same experimental conditions as the single-cell RNA-seq data. Assuming 100% capture rate for smFISH, we can compare average smFISH counts with average UMI counts. Round markers correspond to median value across cells, and bars correspond to 95% confidence interval across cells. The smFISH counts suggest UMI counts for endogenous transcripts are on the order of 5-10% on average, while ERCC spike-in UMI counts correspond to 0.5-1% efficiency on average.

Supplementary Figure 3 Trace plots from Bayesian models of degradation.

The posterior samples from the model parameters in Stan41 for both the ERCC and SIRV analysis show very narrow confidence intervals and good correspondence between the different sampling chains. The SIRV based model is slightly noisier, which can be expected, as isoform-level expression when multiple isoforms are present is a harder quantification problem than quantifying expression of the unique ERCC sequences. For the ERCC model, the mode of the degradation rate parameter p is 19%, and for the SIRV model it is 18.5%.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–3 (PDF 417 kb)

Supplementary Table 1

Descriptive summaries of the public studies used for the comparison (XLSX 11 kb)

Supplementary Table 2

Full data table of technical parameters for each sample used for comparison and generation of all figures (CSV 9363 kb)

Supplementary Software

Umis version 0.3.0, which we used for processing all UMI data. See https://github.com/vals/umis for updated versions (ZIP 30 kb)

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Svensson, V., Natarajan, K., Ly, LH. et al. Power analysis of single-cell RNA-sequencing experiments. Nat Methods 14, 381–387 (2017). https://doi.org/10.1038/nmeth.4220

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