| Literature DB >> 24056876 |
Philip Brennecke1, Simon Anders, Jong Kyoung Kim, Aleksandra A Kołodziejczyk, Xiuwei Zhang, Valentina Proserpio, Bianka Baying, Vladimir Benes, Sarah A Teichmann, John C Marioni, Marcus G Heisler.
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.Entities:
Mesh:
Year: 2013 PMID: 24056876 DOI: 10.1038/nmeth.2645
Source DB: PubMed Journal: Nat Methods ISSN: 1548-7091 Impact factor: 28.547