Single-cell RNA sequencing (scRNA-seq) has become an empowering technology to profile the transcriptomes of individual cells on a large scale. Early analyses of differential expression have aimed at identifying differences between subpopulations to identify subpopulation markers. More generally, such methods compare expression levels across sets of cells, thus leading to cross-condition analyses. Given the emergence of replicated multi-condition scRNA-seq datasets, an area of increasing focus is making sample-level inferences, termed here as differential state analysis; however, it is not clear which statistical framework best handles this situation. Here, we surveyed methods to perform cross-condition differential state analyses, including cell-level mixed models and methods based on aggregated pseudobulk data. To evaluate method performance, we developed a flexible simulation that mimics multi-sample scRNA-seq data. We analyzed scRNA-seq data from mouse cortex cells to uncover subpopulation-specific responses to lipopolysaccharide treatment, and provide robust tools for multi-condition analysis within the muscat R package.

单细胞 RNA 测序 (scRNA-seq) 已成为大规模分析单个细胞转录组的赋能技术。差异表达的早期分析旨在识别亚群之间的差异，以识别亚群标记。更一般地，此类方法比较不同组细胞的表达水平，从而进行跨条件分析。鉴于复制的多条件 scRNA-seq 数据集的出现，一个日益受到关注的领域是进行样本级推断，此处称为差异状态分析；然而，尚不清楚哪种统计框架最能处理这种情况。在这里，我们调查了执行跨条件微分状态分析的方法，包括细胞级混合模型和基于聚合伪批量数据的方法。为了评估方法性能，我们开发了一种灵活的模拟来模拟多样本 scRNA-seq 数据。我们分析了来自小鼠皮质细胞的 scRNA-seq 数据，以揭示对脂多糖治疗的亚群特异性反应，并为muscat R包内的多条件分析提供强大的工具。