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

• Published in: Nature methods Vol. 10; no. 11; pp. 1093 - 1095
• Main Authors: Brennecke, Philip, Anders, Simon, Kim, Jong Kyoung, Kołodziejczyk, Aleksandra A, Zhang, Xiuwei, Proserpio, Valentina, Baying, Bianka, Benes, Vladimir, Teichmann, Sarah A, Marioni, John C, Heisler, Marcus G
• Format: Journal Article
• Language: English
• Published: New York Nature Publishing Group US 01.11.2013; Nature Publishing Group
• Subjects: 631/114/2415; 631/208/199; 631/449/1659; 631/553; Analysis; Arabidopsis thaliana; Bioinformatics; Biological Microscopy; Biological Techniques; Biomedical Engineering/Biotechnology; brief-communication; Cells; Experiments; Gene expression; Life Sciences; Mus musculus; Physiological aspects; Proteomics; Ribonucleic acid; RNA; Sequence Analysis, RNA - methods; Single-Cell Analysis; Statistical methods; Unicellular organisms
• ISSN: 1548-7091; 1548-7105; 1548-7105
• DOI: 10.1038/nmeth.2645

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 .