Differential expression analysis of single-cell RNA sequencing (scRNA-seq) data is central for characterizing how experimental factors affect the distribution of gene expression. However, distinguishing between biological and technical sources of cell-cell variability and assessing the statistical significance of quantitative comparisons between cell groups remain challenging. We introduce Memento, a tool for robust and efficient differential analysis of mean expression, variability, and gene correlation from scRNA-seq data, scalable to millions of cells and thousands of samples. We applied Memento to 70,000 tracheal epithelial cells to identify interferon-responsive genes, 160,000 CRISPR-Cas9 perturbed T cells to reconstruct gene-regulatory networks, 1.2 million peripheral blood mononuclear cells (PBMCs) to map cell-type-specific quantitative trait loci (QTLs), and the 50-million-cell CELLxGENE Discover corpus to compare arbitrary cell groups. In all cases, Memento identified more significant and reproducible differences in mean expression compared with existing methods. It also identified differences in variability and gene correlation that suggest distinct transcriptional regulation mechanisms imparted by perturbations.

中文翻译：

---

单细胞 RNA 测序数据差异表达分析的矩量框架方法


单细胞 RNA 测序 （scRNA-seq） 数据的差异表达分析是表征实验因素如何影响基因表达分布的核心。然而，区分细胞间变异性的生物学和技术来源以及评估细胞组之间定量比较的统计意义仍然具有挑战性。我们介绍了 Memento，这是一种工具，用于对 scRNA-seq 数据的平均表达、变异性和基因相关性进行稳健高效的差异分析，可扩展至数百万个细胞和数千个样品。我们将 Memento 应用于 70,000 个气管上皮细胞以识别干扰素反应基因，将 160,000 个 CRISPR-Cas9 扰动的 T 细胞用于重建基因调控网络，将 120 万个外周血单核细胞 （PBMC） 用于绘制细胞类型特异性数量性状位点 （QTL），以及将 5000 万个细胞的 CELLxGENE Discover 语料库用于比较任意细胞组。在所有情况下，与现有方法相比，Memento 确定了更显着和可重复的平均表达差异。它还确定了变异性和基因相关性的差异，这表明扰动赋予了不同的转录调控机制。