Accounting for technical noise in single-cell RNA-seq experiments
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- 22 September 2013
- journal article
- research article
- Published by Springer Science and Business Media LLC in Nature Methods
- Vol. 10 (11), 1093-1095
- https://doi.org/10.1038/nmeth.2645
Abstract
A statistical method that uses spike-ins to model the dependence of technical noise on transcript abundance in single-cell RNA-seq experiments allows identification of genes wherein observed variability in read counts can be reliably interpreted as a signal of biological variability as opposed to the effect of technical noise. Single-cell RNA-seq can yield valuable insights about the variability within a population of seemingly homogeneous cells. We developed a quantitative statistical method to distinguish true biological variability from the high levels of technical noise in single-cell experiments. Our approach quantifies the statistical significance of observed cell-to-cell variability in expression strength on a gene-by-gene basis. We validate our approach using two independent data sets from Arabidopsis thaliana and Mus musculus.Keywords
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