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Nat Methods. 2017 Jun;14(6):565-571. doi: 10.1038/nmeth.4292. Epub 2017 May 15.

Normalizing single-cell RNA sequencing data: challenges and opportunities.

Author information

1
MRC Biostatistics Unit, Cambridge Institute of Public Health, Cambridge, UK.
2
EMBL-European Bioinformatics Institute, Wellcome Genome Campus, Cambridge, UK.
3
The Alan Turing Institute, British Library, London, UK.
4
Department of Statistical Science, University College London, London, UK.
5
Division of Biostatistics, School of Public Health, University of California, Berkeley, Berkeley, California, USA.
6
Department of Statistics, University of California, Berkeley, Berkeley, California, USA.
7
Cancer Research UK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Cambridge, UK.
8
Wellcome Trust Sanger Institute, Wellcome Genome Campus, Cambridge, UK.

Abstract

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.

PMID:
28504683
PMCID:
PMC5549838
DOI:
10.1038/nmeth.4292
[Indexed for MEDLINE]
Free PMC Article

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