Single-cell RNA-seq (scRNA-seq) provides a comprehensive measurement of stochasticity in transcription, but the limitations of the technology have prevented its application to dissect variability in RNA processing events such as splicing. Here, we present BRIE (Bayesian regression for isoform estimation), a Bayesian hierarchical model that resolves these problems by learning an informative prior distribution from sequence features. We show that BRIE yields reproducible estimates of exon inclusion ratios in single cells and provides an effective tool for differential isoform quantification between scRNA-seq data sets. BRIE, therefore, expands the scope of scRNA-seq experiments to probe the stochasticity of RNA processing.

译文

:单细胞RNA-seq(scRNA-seq)提供了转录随机性的全面测量方法,但该技术的局限性使其无法用于分析RNA加工事件(例如剪接)中的变异性。在这里,我们介绍BRIE(用于同等型估计的贝叶斯回归),它是一种贝叶斯层次模型,可以通过从序列特征中学习有用的先验分布来解决这些问题。我们表明,BRIE产生单细胞中外显子包含率的可再现估计,并为scRNA-seq数据集之间的差异同工型定量提供了有效的工具。因此,BRIE扩大了scRNA-seq实验的范围,以探测RNA加工的随机性。

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