Characterizing noise structure in single-cell RNA-seq distinguishes genuine from technical stochastic allelic expression

Jong Kyoung Kim, Aleksandra A. Kolodziejczyk, Tomislav Ilicic, Sarah A. Teichmann and John C. Marioni ()
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Jong Kyoung Kim: European Molecular Biology Laboratory-European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus
Aleksandra A. Kolodziejczyk: European Molecular Biology Laboratory-European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus
Tomislav Ilicic: European Molecular Biology Laboratory-European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus
Sarah A. Teichmann: European Molecular Biology Laboratory-European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus
John C. Marioni: European Molecular Biology Laboratory-European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus

Nature Communications, 2015, vol. 6, issue 1, 1-9

Abstract: Abstract Single-cell RNA-sequencing (scRNA-seq) facilitates identification of new cell types and gene regulatory networks as well as dissection of the kinetics of gene expression and patterns of allele-specific expression. However, to facilitate such analyses, separating biological variability from the high level of technical noise that affects scRNA-seq protocols is vital. Here we describe and validate a generative statistical model that accurately quantifies technical noise with the help of external RNA spike-ins. Applying our approach to investigate stochastic allele-specific expression in individual cells, we demonstrate that a large fraction of stochastic allele-specific expression can be explained by technical noise, especially for lowly and moderately expressed genes: we predict that only 17.8% of stochastic allele-specific expression patterns are attributable to biological noise with the remainder due to technical noise.

Date: 2015
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DOI: 10.1038/ncomms9687

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