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Bayesian inference of gene expression states from single-cell RNA-seq data

Nature Biotechnology volume 39, pages 1008–1016 (2021)Cite this article

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Abstract

Despite substantial progress in single-cell RNA-seq (scRNA-seq) data analysis methods, there is still little agreement on how to best normalize such data. Starting from the basic requirements that inferred expression states should correct for both biological and measurement sampling noise and that changes in expression should be measured in terms of fold changes, we here derive a Bayesian normalization procedure called Sanity (SAmpling-Noise-corrected Inference of Transcription activitY) from first principles. Sanity estimates expression values and associated error bars directly from raw unique molecular identifier (UMI) counts without any tunable parameters. Using simulated and real scRNA-seq datasets, we show that Sanity outperforms other normalization methods on downstream tasks, such as finding nearest-neighbor cells and clustering cells into subtypes. Moreover, we show that by systematically overestimating the expression variability of genes with low expression and by introducing spurious correlations through mapping the data to a lower-dimensional representation, other methods yield severely distorted pictures of the data.

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Fig. 1: Summary of the Sanity approach.
Fig. 2: Effects of Poisson fluctuations on gene expression variance.
Fig. 3: Accuracy of gene expression estimates as a function of depth of coverage.
Fig. 4: Correlations between inferred gene expression levels and library size and between pairs of genes.
Fig. 5: Accuracy of the k nearest-neighbor and clustering predictions.

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Data availability

The raw UMI count tables for each of the scRNA-seq datasets as well as all normalized expression values as inferred by each of the methods are freely available from https://doi.org/10.5281/zenodo.4009187.

Code availability

Sanity was implemented in C and is freely available for download at https://github.com/jmbreda/Sanity. Besides Sanity itself, we also provide code for estimating pairwise distances between cells. In addition, at the same GitHub site, we provide a collection of scripts and supplementary files that should allow other researchers to reproduce the results presented in this publication.

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Acknowledgements

This work was supported by the Swiss National Science Foundation, grant 310030_184937. Calculations were performed at sciCORE (http://scicore.unibas.ch/), the scientific computing core facility of the University of Basel.

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Authors and Affiliations

  1. Biozentrum, University of Basel, Basel, Switzerland

    Jérémie Breda, Mihaela Zavolan & Erik van Nimwegen

  2. Swiss Institute of Bioinformatics, Basel, Switzerland

    Jérémie Breda, Mihaela Zavolan & Erik van Nimwegen

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  1. Jérémie Breda
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  2. Mihaela Zavolan
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Contributions

E.v.N. developed the theoretical formalism. J.B. and E.v.N. developed the implementation and designed the benchmarking. J.B. performed all computations, analyses and simulations. J.B., M.Z. and E.v.N. interpreted the results and wrote the manuscript.

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Correspondence to Erik van Nimwegen.

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Supplementary Methods, Figs. 1–26 and Text 1.

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Breda, J., Zavolan, M. & van Nimwegen, E. Bayesian inference of gene expression states from single-cell RNA-seq data. Nat Biotechnol 39, 1008–1016 (2021). https://doi.org/10.1038/s41587-021-00875-x

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