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.
This is a preview of subscription content, access via your institution
Access options
Access Nature and 54 other Nature Portfolio journals
Get Nature+, our best-value online-access subscription
$29.99 / 30 days
cancel any time
Subscribe to this journal
Receive 12 print issues and online access
$259.00 per year
only $21.58 per issue
Rent or buy this article
Prices vary by article type
from$1.95
to$39.95
Prices may be subject to local taxes which are calculated during checkout
Similar content being viewed by others
Accession codes
References
Conesa, A. et al. Genome Biol. 17, 13 (2016).
Robinson, M.D. & Oshlack, A. Genome Biol. 11, R25 (2010).
Anders, S. & Huber, W. Genome Biol. 11, R106 (2010).
Bacher, R. & Kendziorski, C. Genome Biol. 17, 63 (2016).
Lun, A.T., Bach, K. & Marioni, J.C. Genome Biol. 17, 75 (2016).
Vallejos, C.A., Marioni, J.C. & Richardson, S. PLoS Comput. Biol. 11, e1004333 (2015).
Li, B. & Dewey, C.N. BMC Bioinformatics 12, 323 (2011).
Kharchenko, P.V., Silberstein, L. & Scadden, D.T. Nat. Methods 11, 740–742 (2014).
Finak, G. et al. Genome Biol. 16, 278 (2015).
Leng, N. et al. Nat. Methods 12, 947–950 (2015).
Sakaue-Sawano, A. et al. Cell 132, 487–498 (2008).
Anders, S., Pyl, P.T. & Huber, W. Bioinformatics 31, 166–169 (2015).
Risso, D., Schwartz, K., Sherlock, G. & Dudoit, S. BMC Bioinformatics 12, 480 (2011).
Johnson, W.E., Li, C. & Rabinovic, A. Biostatistics 8, 118–127 (2007).
Leek, J.T. & Storey, J.D. PLoS Genet. 3, 1724–1735 (2007).
Lin, Y. et al. BMC Genomics 17, 28 (2016).
McDavid, A., Finak, G. & Gottardo, R. Nat. Biotechnol. 34, 591–593 (2016).
Sengupta, D., Rayan, N.A., Lim, M., Lim, B. & Prabhakar, S. Preprint at http://biorxiv.org/content/early/2016/04/22/049734 (2016).
Benjamini, Y. & Hochberg, Y. J. R. Stat. Soc. B Stat. Methodol. 57, 289–300 (1995).
Hou, Z. et al. Sci. Rep. 5, 9570 (2015).
Chen, G. et al. Nat. Methods 8, 424–429 (2011).
Fluidigm Corporation. Using the C1 Single-Cell Auto Prep System to Generate mRNA from Single Cells and Libraries for Sequencing (Fluidigm Corporation, 2017).
Langmead, B., Trapnell, C., Pop, M. & Salzberg, S.L. Genome Biol. 10, R25 (2009).
Santos, A., Wernersson, R. & Jensen, L.J. Nucleic Acids Res. 43, D1140–D1144 (2015).
Buettner, F. et al. Nat. Biotechnol. 33, 155–160 (2015).
Islam, S. et al. Genome Res. 21, 1160–1167 (2011).
Chu, L.-F. et al. Genome Biol. 17, 173 (2016).
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
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
Ethics declarations
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)
Rights and permissions
About this article
Cite this article
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
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1038/nmeth.4263
This article is cited by
-
The Poisson distribution model fits UMI-based single-cell RNA-sequencing data
BMC Bioinformatics (2023)
-
Transcriptomic changes in single yeast cells under various stress conditions
BMC Genomics (2023)
-
Spatial tumour gene signature discriminates neoplastic from non-neoplastic compartments in colon cancer: unravelling predictive biomarkers for relapse
Journal of Translational Medicine (2023)
-
Applications of single-cell RNA sequencing in drug discovery and development
Nature Reviews Drug Discovery (2023)
-
Removing unwanted variation from large-scale RNA sequencing data with PRPS
Nature Biotechnology (2023)