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.
Highlights
Allele-specific expression (ASE) at single-cell resolution is a critical tool for understanding the stochastic and dynamic features of gene expression
Our ability to discern gene expression dynamics is limited by low depth of sequencing coverage per cell[7–14] and it is critical to make full use of all information available in scRNA-Seq data
We apply scBASE methods to evaluate the statistical independence of allelic bursting
Summary
Allele-specific expression (ASE) at single-cell resolution is a critical tool for understanding the stochastic and dynamic features of gene expression. 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 propose an approach to classification and estimation of allele-specific gene expression in single cells (Fig. 1 and Methods). Partial pooling can be applied to either of the read counting results leading to four different methods for estimating allelic proportions: (a) unique reads, (b) weighted allocation, (c) unique reads with partial pooling, or (d) weighted allocation with partial pooling. These methods are implemented in our scBASE software. We illustrate the interpretive power of allelic expression analysis of scRNA-Seq using data from a development time course[8]
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