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

Nature Methods volume 14, pages 584–586 (2017)Cite this article

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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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References

  1. Conesa, A. et al. Genome Biol. 17, 13 (2016).

    Article  Google Scholar 

  2. Robinson, M.D. & Oshlack, A. Genome Biol. 11, R25 (2010).

    Article  Google Scholar 

  3. Anders, S. & Huber, W. Genome Biol. 11, R106 (2010).

    CAS  Article  Google Scholar 

  4. Bacher, R. & Kendziorski, C. Genome Biol. 17, 63 (2016).

    Article  Google Scholar 

  5. Lun, A.T., Bach, K. & Marioni, J.C. Genome Biol. 17, 75 (2016).

    Article  Google Scholar 

  6. Vallejos, C.A., Marioni, J.C. & Richardson, S. PLoS Comput. Biol. 11, e1004333 (2015).

    Article  Google Scholar 

  7. Li, B. & Dewey, C.N. BMC Bioinformatics 12, 323 (2011).

    CAS  Article  Google Scholar 

  8. Kharchenko, P.V., Silberstein, L. & Scadden, D.T. Nat. Methods 11, 740–742 (2014).

    CAS  Article  Google Scholar 

  9. Finak, G. et al. Genome Biol. 16, 278 (2015).

    Article  Google Scholar 

  10. Leng, N. et al. Nat. Methods 12, 947–950 (2015).

    CAS  Article  Google Scholar 

  11. Sakaue-Sawano, A. et al. Cell 132, 487–498 (2008).

    CAS  Article  Google Scholar 

  12. Anders, S., Pyl, P.T. & Huber, W. Bioinformatics 31, 166–169 (2015).

    CAS  Article  Google Scholar 

  13. Risso, D., Schwartz, K., Sherlock, G. & Dudoit, S. BMC Bioinformatics 12, 480 (2011).

    CAS  Article  Google Scholar 

  14. Johnson, W.E., Li, C. & Rabinovic, A. Biostatistics 8, 118–127 (2007).

    Article  Google Scholar 

  15. Leek, J.T. & Storey, J.D. PLoS Genet. 3, 1724–1735 (2007).

    CAS  Article  Google Scholar 

  16. Lin, Y. et al. BMC Genomics 17, 28 (2016).

    Article  Google Scholar 

  17. McDavid, A., Finak, G. & Gottardo, R. Nat. Biotechnol. 34, 591–593 (2016).

    CAS  Article  Google Scholar 

  18. Sengupta, D., Rayan, N.A., Lim, M., Lim, B. & Prabhakar, S. Preprint at http://biorxiv.org/content/early/2016/04/22/049734 (2016).

  19. Benjamini, Y. & Hochberg, Y. J. R. Stat. Soc. B Stat. Methodol. 57, 289–300 (1995).

    Google Scholar 

  20. Hou, Z. et al. Sci. Rep. 5, 9570 (2015).

    Article  Google Scholar 

  21. Chen, G. et al. Nat. Methods 8, 424–429 (2011).

    CAS  Article  Google Scholar 

  22. Fluidigm Corporation. Using the C1 Single-Cell Auto Prep System to Generate mRNA from Single Cells and Libraries for Sequencing (Fluidigm Corporation, 2017).

  23. Langmead, B., Trapnell, C., Pop, M. & Salzberg, S.L. Genome Biol. 10, R25 (2009).

    Article  Google Scholar 

  24. Santos, A., Wernersson, R. & Jensen, L.J. Nucleic Acids Res. 43, D1140–D1144 (2015).

    CAS  Article  Google Scholar 

  25. Buettner, F. et al. Nat. Biotechnol. 33, 155–160 (2015).

    CAS  Article  Google Scholar 

  26. Islam, S. et al. Genome Res. 21, 1160–1167 (2011).

    CAS  Article  Google Scholar 

  27. Chu, L.-F. et al. Genome Biol. 17, 173 (2016).

    Article  Google Scholar 

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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.

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Author notes

  1. Rhonda Bacher and Li-Fang Chu: These authors contributed equally to this work.

Authors and Affiliations

  1. Department of Statistics, University of Wisconsin–Madison, Madison, Wisconsin, USA

    Rhonda Bacher & Michael Newton

  2. Morgridge Institute for Research, Madison, Wisconsin, USA

    Li-Fang Chu, Ning Leng, James A Thomson & Ron M Stewart

  3. Laboratory of Genetics, University of Wisconsin–Madison, Madison, Wisconsin, USA

    Audrey P Gasch

  4. Department of Biostatistics and Medical Informatics, University of Wisconsin–Madison, Madison, Wisconsin, USA

    Michael Newton & Christina Kendziorski

Authors
  1. Rhonda Bacher
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  2. Li-Fang Chu
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  3. Ning Leng
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  4. Audrey P Gasch
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  5. James A Thomson
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  6. Ron M Stewart
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  7. Michael Newton
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  8. Christina Kendziorski
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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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