Modelling capture efficiency of single-cell RNA-sequencing data improves inference of transcriptome-wide burst kinetics.
Modelling capture efficiency of single-cell RNA-sequencing data improves inference of transcriptome-wide burst kinetics.
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DOI：
10.1093/bioinformatics/btad395
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发表时间：
2023-07-01
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基因表达的特征是在启动子活动的短暂且随机的时期发生随机转录爆发。基因表达突发的动力学在整个基因组中有所不同，并且取决于启动子序列等因素。单细胞 RNA 测序 (scRNA-seq) 使得在全球基因组水平上量化细胞间转录变异成为可能。然而，scRNA-seq 数据容易出现技术变异，包括单个细胞转录本的捕获效率低且可变。在这里，我们针对 scRNA-seq 数据中观察到的变异性提出了一种新颖的数学理论。我们的方法捕获了细胞大小和捕获效率的爆发动力学和变异性，这使我们能够提出几种基于可能性和基于模拟的方法，用于从 scRNA-seq 数据推断爆发动力学。使用合成数据和真实数据，我们表明基于模拟的方法为从 scRNA-seq 数据推断爆发动力学提供了准确、稳健和灵活的工具。特别是，以监督方式，基于神经网络的基于模拟的推理方法被证明在应用于等位基因和非等位基因特异性 scRNA-seq 数据时是准确且有用的。神经网络和近似贝叶斯计算推理的代码分别位于 https://github.com/WT215/nnRNA 和 https://github.com/WT215/Julia_ABC。
Gene expression is characterized by stochastic bursts of transcription that occur at brief and random periods of promoter activity. The kinetics of gene expression burstiness differs across the genome and is dependent on the promoter sequence, among other factors. Single-cell RNA sequencing (scRNA-seq) has made it possible to quantify the cell-to-cell variability in transcription at a global genome-wide level. However, scRNA-seq data are prone to technical variability, including low and variable capture efficiency of transcripts from individual cells. Here, we propose a novel mathematical theory for the observed variability in scRNA-seq data. Our method captures burst kinetics and variability in both the cell size and capture efficiency, which allows us to propose several likelihood-based and simulation-based methods for the inference of burst kinetics from scRNA-seq data. Using both synthetic and real data, we show that the simulation-based methods provide an accurate, robust and flexible tool for inferring burst kinetics from scRNA-seq data. In particular, in a supervised manner, a simulation-based inference method based on neural networks proves to be accurate and useful when applied to both allele and nonallele-specific scRNA-seq data. The code for Neural Network and Approximate Bayesian Computation inference is available at https://github.com/WT215/nnRNA and https://github.com/WT215/Julia_ABC, respectively.
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