Journal
NATURE METHODS
Volume 14, Issue 6, Pages 565-571Publisher
NATURE PORTFOLIO
DOI: 10.1038/NMETH.4292
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Funding
- EMBL
- MRC [MRC MC UP 0801/1]
- Alan Turing Institute under the EPSRC [EP/N510129/1]
- CRUK
- Wellcome Trust Strategic Award [105031/D/14/Z]
- US National Institutes of Health BRAIN Initiative [U01 MH105979]
- Wellcome Trust [105031/D/14/Z] Funding Source: Wellcome Trust
- Alan Turing Institute [TU/A/000014] Funding Source: researchfish
- Cancer Research UK [22231] Funding Source: researchfish
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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.
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