Vallejos, CA;
Risso, D;
Scialdone, A;
Dudoit, S;
Marioni, JC;
(2017)
Normalizing single-cell RNA sequencing data: challenges and opportunities.
Nature Methods
, 14
(6)
pp. 565-571.
10.1038/nmeth.4292.
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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.
| Type: | Article |
|---|---|
| Title: | Normalizing single-cell RNA sequencing data: challenges and opportunities |
| Location: | United States |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1038/nmeth.4292 |
| Publisher version: | http://dx.doi.org/10.1038/nmeth.4292 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Computational models, Gene expression, Gene expression analysis, RNA sequencing, Statistical methods |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences |
| URI: | https://discovery.ucl.ac.uk/id/eprint/1558937 |
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