Method of moments framework for differential expression analysis of single-cell RNA sequencing data

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. [Display omitted] •A statistical model for scRNA-seq decouples measurement and expression noise•Highly efficient resampling allows for well-calibrated hypothesis testing•Memento enables studying coordinated expression of genes in response to perturbations•Memento maps loci associated with gene expression mean, variability, and correlation Memento implements a statistical model and a fast resampling procedure to estimate and compare the mean, variability, and correlation of gene expression, allowing for the study of transcription in a deeper yet accurate fashion compared with traditional differential expression.