Normalizing single-cell RNA sequencing data: challenges and opportunities

Catalina A Vallejos, Davide Risso, Antonio Scialdone, Sandrine Dudoit, John C Marioni

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

Single-cell transcriptomics is becoming an important component of the molecular biologist's toolkit. A critical step when analyzing data generated using this technology is normalization. However, normalization is typically performed using methods developed for bulk RNA sequencing or even microarray data, and the suitability of these methods for single-cell transcriptomics has not been assessed. We here discuss commonly used normalization approaches and illustrate how these can produce misleading results. Finally, we present alternative approaches and provide recommendations for single-cell RNA sequencing users.

Original languageEnglish
Pages (from-to)565-571
Number of pages7
JournalNature Methods
Issue number6
Early online date15 May 2017
Publication statusPublished - Jun 2017

Keywords / Materials (for Non-textual outputs)

  • Algorithms
  • Data Interpretation, Statistical
  • High-Throughput Nucleotide Sequencing/methods
  • RNA/genetics
  • Reference Values
  • Sequence Analysis, RNA/standards
  • Single-Cell Analysis/standards
  • Transcriptome/genetics


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