Normalization for Single-Cell RNA-Seq Data Analysis

  • Rhonda BacherEmail author
Part of the Methods in Molecular Biology book series (MIMB, volume 1935)


In this chapter, we describe a robust normalization method for single-cell RNA sequencing data. The procedure, SCnorm, is implemented in R and is part of Bioconductor. Also included in the package are diagnostic functions to visualize normalization performance. This chapter provides an overview of the methodology and provides example work-flows.

Key words

Single-cell RNA-seq Normalization Gene expression Read count High-throughput sequencing 


  1. 1.
    Robinson MD, Oshlack A (2010) A scaling normalization method for differential expression analysis of RNA-seq data. Genome Biol 11(3):R25CrossRefGoogle Scholar
  2. 2.
    Bacher R et al (2017) SCnorm: robust normalization of single-cell RNA-seq data. Nat Methods 14(6):584–586CrossRefGoogle Scholar
  3. 3.
    Anders S, Huber W (2010) Differential expression analysis for sequence count data. Genome Biol 11(10):R106CrossRefGoogle Scholar
  4. 4.
    Li B, Dewey CN (2011) RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome. BMC Bioinformatics 12:323CrossRefGoogle Scholar
  5. 5.
    Risso D, Ngai J, Speed TP, Dudoit S (2014) Normalization of RNA-seq data using factor analysis of control genes or samples. Nat Biotechnol 32(9):896–902CrossRefGoogle Scholar
  6. 6.
    Kaufman L, Rousseeuw P (1987) Clustering by means of medoids. In Statistical Data Analysis Based on the L1 Norm and Related Methods (pp. 405–416). North-Holland; Amsterdam.Google Scholar
  7. 7.
    Mächler M, Rousseeuw P, Struyf A, Hubert M, Hornik K (2012) Cluster: cluster analysis basics and extensions. R package version, 1(2), 56Google Scholar
  8. 8.
    Bacher R, Kendziorski C (2016) Design and computational analysis of single-cell RNA-sequencing experiments. Genome Biol 17(1):63CrossRefGoogle Scholar
  9. 9.
    Morgan M, Obenchain V, Hester J, Pagès H (2017) SummarizedExperiment: summarizedExperiment container.
  10. 10.
    Leng N et al (2013) EBSeq: an empirical Bayes hierarchical model for inference in RNA-seq experiments. Bioinformatics 29(8):1035–1043CrossRefGoogle Scholar
  11. 11.
    Love MI, Huber W, Anders S (2014) Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol 15(12):550CrossRefGoogle Scholar
  12. 12.
    Finak G et al (2015) MAST: a flexible statistical framework for assessing transcriptional changes and characterizing heterogeneity in single-cell RNA sequencing data. Genome Biol 16(1):278CrossRefGoogle Scholar
  13. 13.
    Korthauer KD et al (2016) A statistical approach for identifying differential distributions in single-cell RNA-seq experiments. Genome Biol 17(1):222CrossRefGoogle Scholar
  14. 14.
    Risso D, Schwartz K, Sherlock G, Dudoit S (2011) GC-content normalization for RNA-seq data. BMC Bioinformatics 12(1):480CrossRefGoogle Scholar
  15. 15.
    Stegle O, Teichmann SA, Marioni JC (2015) Computational and analytical challenges in single-cell transcriptomics. Nat Rev Genet 16(3):133–145CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of BiostatisticsUniversity of FloridaGainesvilleUSA

Personalised recommendations