Document Type

Article

Publication Date

11-15-2019

Keywords

JMG

JAX Source

Nat Commun 2019 Nov 15; 10(1):5188.

PMID

31729374

DOI

https://doi.org/10.1038/s41467-019-13099-0

Grant

GM070683

Abstract

Allele-specific expression (ASE) at single-cell resolution is a critical tool for understanding the stochastic and dynamic features of gene expression. However, low read coverage and high biological variability present challenges for analyzing ASE. We demonstrate that discarding multi-mapping reads leads to higher variability in estimates of allelic proportions, an increased frequency of sampling zeros, and can lead to spurious findings of dynamic and monoallelic gene expression. Here, we report a method for ASE analysis from single-cell RNA-Seq data that accurately classifies allelic expression states and improves estimation of allelic proportions by pooling information across cells. We further demonstrate that combining information across cells using a hierarchical mixture model reduces sampling variability without sacrificing cell-to-cell heterogeneity. We applied our approach to re-evaluate the statistical independence of allelic bursting and track changes in the allele-specific expression patterns of cells sampled over a developmental time course.

Comments

We would like to thank Steven C. Munger and Daniel A. Skelly for their helpful comments on this manuscript.

This open access article is licensed under a Creative Commons Attribution 4.0 International Licens

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