论文标题
相关计数数据的灵活模型，并应用于单细胞RNA测序数据的多条件差分表达分析
A flexible model for correlated count data, with application to multi-condition differential expression analyses of single-cell RNA sequencing data
论文作者
论文摘要
检测基因表达的差异是单细胞RNA测序实验的重要组成部分，并且已经为此目的开发了许多统计方法。大多数差异表达分析集中于比较两组之间的表达（例如，治疗与对照）。但是，在多种条件差分表达分析中，在许多条件下进行表达的兴趣越来越大，其目的是准确检测和估计所有条件下的表达差异。我们表明，在所有情况下直接建模单细胞RNA-seq计数，同时也推断出表达差异是如何在条件上共享的，与现有方法相比，可以大大提高检测和估计表达差异的性能。我们通过分析来自研究细胞因子刺激对基因表达的影响的单细胞实验的数据来说明这种新方法的潜力。我们称我们的新方法为“ Poisson多元自适应收缩”，并在https://github.com/stephenslab/poisson.mash.alpha上在线提供的R软件包实现。
Detecting differences in gene expression is an important part of single-cell RNA sequencing experiments, and many statistical methods have been developed for this aim. Most differential expression analyses focus on comparing expression between two groups (e.g., treatment vs. control). But there is increasing interest in multi-condition differential expression analyses in which expression is measured in many conditions, and the aim is to accurately detect and estimate expression differences in all conditions. We show that directly modeling single-cell RNA-seq counts in all conditions simultaneously, while also inferring how expression differences are shared across conditions, leads to greatly improved performance for detecting and estimating expression differences compared to existing methods. We illustrate the potential of this new approach by analyzing data from a single-cell experiment studying the effects of cytokine stimulation on gene expression. We call our new method "Poisson multivariate adaptive shrinkage", and it is implemented in an R package available online at https://github.com/stephenslab/poisson.mash.alpha.
