尽管转录组学数据通常用于分析成熟的剪接mRNA,最近的注意力集中在联合研究剪接和未剪接(或前体)mRNA,可用于研究基因调控和基因表达产生的变化。尽管如此,大多数剪接/未剪接推断方法(如RNA速度工具)集中在单个样本上,并且很少允许在样本组之间进行比较(例如,健康与病态)。此外,这种推论很有挑战性,因为剪接和未剪接的mRNA丰度具有高度的定量不确定性,由于多映射读取的普遍性,即与多个转录本(或基因)兼容的读段,和/或它们的拼接和未拼接版本。这里,我们提出了差异化监管,一种贝叶斯分层方法,用于发现实验条件之间关于未剪接mRNA相对丰度(相对于总mRNA)的变化。我们通过潜在变量方法对量化不确定性进行建模,其中读数被分配给他们的基因/转录本,和相应的拼接版本。我们设计了几个基准,我们的方法显示出良好的性能,在灵敏度和误差控制方面,vs.最先进的竞争对手。重要的是,我们的工具是灵活的,并使用批量和单细胞RNA测序数据。差异调节作为BioconductorR包分布。
Although transcriptomics data is typically used to analyze mature spliced mRNA, recent attention has focused on jointly investigating spliced and unspliced (or precursor-) mRNA, which can be used to study gene regulation and changes in gene expression production. Nonetheless, most methods for spliced/unspliced inference (such as RNA velocity tools) focus on individual samples, and rarely allow comparisons between groups of samples (e.g. healthy vs. diseased). Furthermore, this kind of inference is challenging, because spliced and unspliced mRNA abundance is characterized by a high degree of quantification uncertainty, due to the prevalence of multi-mapping reads, ie reads compatible with multiple transcripts (or genes), and/or with both their spliced and unspliced versions. Here, we present DifferentialRegulation, a Bayesian hierarchical method to discover changes between experimental conditions with respect to the relative abundance of unspliced mRNA (over the total mRNA). We model the quantification uncertainty via a latent variable approach, where reads are allocated to their gene/transcript of origin, and to the respective splice version. We designed several benchmarks where our approach shows good performance, in terms of sensitivity and error control, vs. state-of-the-art competitors. Importantly, our tool is flexible, and works with both bulk and single-cell RNA-sequencing data. DifferentialRegulation is distributed as a Bioconductor R package.