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SCnorm: robust normalization of single-cell RNA-seq data

Abstract

The normalization of RNA-seq data is essential for accurate downstream inference, but the assumptions upon which most normalization methods are based are not applicable in the single-cell setting. Consequently, applying existing normalization methods to single-cell RNA-seq data introduces artifacts that bias downstream analyses. To address this, we introduce SCnorm for accurate and efficient normalization of single-cell RNA-seq data.

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Figure 1: Count–depth relationships in bulk and single-cell data sets before and after normalization.
Figure 2: SCNorm removes bias from fold-change estimates.
Figure 3: Normalization by SCnorm improves researchers' ability to resolve cell populations.

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Acknowledgements

This work was supported by NIH GM102756 (C.K.), NIH U54 AI117924 (C.K. and M.N.), 1T32LM012413-01A1 (M.N.), NIH 5U01HL099773 (J.A.T.), and the Morgridge Institute for Research. We thank J. Bolin, A. Elwell, and B.K. Nguyen for the preparation and sequencing of the RNA-seq samples and P. Jiang and S. Swanson for performing the RNA-seq read processing.

Author information

Authors and Affiliations

Authors

Contributions

R.B. and C.K. designed the research, developed the method, and wrote the first version of the manuscript. L.-F.C. performed experiments and quality control on scRNA-seq data generated from H1 and H9 hESCs. R.B. analyzed all data sets. L.C., N.L., A.P.G., J.A.T., R.M.S., and M.N. analyzed results from early versions of the method, which helped during method refinement. All authors contributed to the writing of the manuscript.

Corresponding author

Correspondence to Christina Kendziorski.

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Competing interests

The authors declare no competing financial interests.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–19 and Supplementary Notes 1–3. (PDF 4518 kb)

Supplementary Software

SCnorm R package and vignette. (ZIP 40557 kb)

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Bacher, R., Chu, LF., Leng, N. et al. SCnorm: robust normalization of single-cell RNA-seq data. Nat Methods 14, 584–586 (2017). https://doi.org/10.1038/nmeth.4263

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  • DOI: https://doi.org/10.1038/nmeth.4263

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