DifferentialRegulation: a Bayesian hierarchical approach to identify differentially regulated genes.
DifferentialRegulation: a Bayesian hierarchical approach to identify differentially regulated genes.
复制标题
DifferentialRegulation：一种贝叶斯分层方法，用于识别差异调节基因。
DOI：
10.1101/2023.08.17.553679
复制
发表时间：
2023
期刊：
影响因子：
--
通讯作者：
Robinson,MarkD
中科院分区：
文献类型：
--
作者：
Tiberi,Simone;Meili,Joël;Cai,Peiying;Soneson,Charlotte;He,Dongze;Sarkar,Hirak;Avalos-Pacheco,Alejandra;Patro,Rob;Robinson,MarkD
虽然转录组学数据通常用于分析成熟剪接的mRNA，但近年来人们越来越关注剪接和未剪接（或前体）mRNA的联合研究，这些研究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/非剪接推断方法（如RNA速度工具）都侧重于单个样本，很少允许样本组之间的比较（例如健康与患病）。此外，这种推断是具有挑战性的，因为剪接和未剪接的mRNA丰度具有高度的定量不确定性，这是由于多定位读取的普遍存在，即与多个转录本（或基因）兼容的读取，和/或与它们的剪接和未剪接版本兼容的读取。在这里，我们提出了差分调节，一种贝叶斯分层方法，用于发现不同实验条件下相对于未剪接mRNA（超过总mRNA）的相对丰度的变化。我们通过潜在变量方法对定量不确定性进行建模，其中读数被分配到其基因/转录本的起源，以及各自的剪接版本。我们设计了几个基准测试，其中我们的方法在灵敏度和误差控制方面表现出良好的性能。最先进的竞争对手。重要的是，我们的工具是灵活的，可用于批量和单细胞rna测序数据。差分调节分布作为一个生物导体R包。
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. healthyvs.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 presentDifferentialRegulation, 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.DifferentialRegulationis distributed as a Bioconductor R package.