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.

翻译：检测基因表达差异是单细胞RNA测序实验的重要环节，为此已发展出多种统计方法。大多数差异表达分析专注于两组间的比较（如处理组与对照组）。然而，多条件差异表达分析正日益受到关注——此类分析需在多种条件下测量基因表达，并旨在准确检测和估计所有条件下的表达差异。本研究表明，与现有方法相比，通过直接同步建模所有条件下的单细胞RNA测序计数数据，同时推断表达差异在条件间的共享模式，可显著提升表达差异的检测与估计性能。我们通过分析一项研究细胞因子刺激对基因表达影响的单细胞实验数据，展示了这一新方法的潜力。我们将此新方法命名为"泊松多元自适应收缩"，其实现代码已封装为R软件包，可通过https://github.com/stephenslab/poisson.mash.alpha在线获取。