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Separating measurement and expression models clarifies confusion in single-cell RNA sequencing analysis

Nature Genetics volume 53, pages 770–777 (2021)Cite this article

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Abstract

The high proportion of zeros in typical single-cell RNA sequencing datasets has led to widespread but inconsistent use of terminology such as dropout and missing data. Here, we argue that much of this terminology is unhelpful and confusing, and outline simple ideas to help to reduce confusion. These include: (1) observed single-cell RNA sequencing counts reflect both true gene expression levels and measurement error, and carefully distinguishing between these contributions helps to clarify thinking; and (2) method development should start with a Poisson measurement model, rather than more complex models, because it is simple and generally consistent with existing data. We outline how several existing methods can be viewed within this framework and highlight how these methods differ in their assumptions about expression variation. We also illustrate how our perspective helps to address questions of biological interest, such as whether messenger RNA expression levels are multimodal among cells.

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Fig. 1: Comparing single-gene expression models on scRNA-seq data.

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Data availability

Sorted immune cell and PBMC data were downloaded from https://10xgenomics.com/data. iPSC data were downloaded from the Gene Expression Omnibus (accession number GSE118723). Brain data were downloaded from the Genotype-Tissue Expression portal (https://www.gtexportal.org/home/datasets). Kidney and retina data were downloaded from the Human Cell Atlas Data Portal (https://data.humancellatlas.org/). Control data were downloaded from https://figshare.com/projects/Zero_inflation_in_negative_control_data/61292. All of the results generated in this study are available at https://zenodo.org/record/4543923 and all analysis notebooks have been published at https://aksarkar.github.io/singlecell-modes/.

Code availability

All of the code used to perform the analysis is available at https://zenodo.org/record/4543921 and https://zenodo.org/record/4543923.

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Acknowledgements

We thank members of the M.S. and Y. Gilad laboratories for helpful comments. This work was supported by NIH grant HG002585 and a Gut Cell Atlas grant from The Leona M. and Harry B. Helmsley Charitable Trust (both to M.S.).

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Authors and Affiliations

  1. Department of Human Genetics, University of Chicago, Chicago, IL, USA

    Abhishek Sarkar & Matthew Stephens

  2. Department of Statistics, University of Chicago, Chicago, IL, USA

    Matthew Stephens

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  1. Abhishek Sarkar
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A.S. and M.S. developed the theory. A.S. performed the analysis. A.S. and M.S. wrote the paper.

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Correspondence to Abhishek Sarkar or Matthew Stephens.

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Sarkar, A., Stephens, M. Separating measurement and expression models clarifies confusion in single-cell RNA sequencing analysis. Nat Genet 53, 770–777 (2021). https://doi.org/10.1038/s41588-021-00873-4

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