Accounting for technical noise in single-cell RNA-seq experiments.

Brennecke, Philip; Anders, Simon; Kim, Jong Kyoung; Kołodziejczyk, Aleksandra A; Zhang, Xiuwei; Proserpio, Valentina; Baying, Bianka; Benes, Vladimir et al. · Nat Methods · 2013

basic_science · Level V

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Abstract

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.

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