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

Min Cheol Kim; Rachel Gate; David S Lee; Andrew Tolopko; Andrew Lu; Erin Gordon; Eric Shifrut; Pablo E Garcia-Nieto; Alexander Marson; Vasilis Ntranos; Chun Jimmie Ye

doi:10.1016/j.cell.2024.09.044

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

Cell. 2024 Oct 31;187(22):6393-6410.e16. doi: 10.1016/j.cell.2024.09.044. Epub 2024 Oct 24.

Authors

Affiliations

¹ Medical Scientist Training Program, University of California, San Francisco, San Francisco, CA, USA; UC Berkeley-UCSF Graduate Program in Bioengineering, San Francisco, CA, USA; Institute for Human Genetics, University of California, San Francisco, San Francisco, CA, USA.
² Institute for Human Genetics, University of California, San Francisco, San Francisco, CA, USA.
³ Chan Zuckerberg Initiative, Redwood City, CA, USA.
⁴ Division of Pulmonary and Critical Care, University of California, San Francisco, San Francisco, CA, USA.
⁵ Diabetes Center, University of California, San Francisco, San Francisco, CA, USA; Division of Infectious Diseases, Department of Medicine, University of California, San Francisco, San Francisco, CA, USA.
⁶ Division of Infectious Diseases, Department of Medicine, University of California, San Francisco, San Francisco, CA, USA; Parker Institute for Cancer Immunotherapy, San Francisco, CA, USA; Gladstone-UCSF Institute of Genomic Immunology, San Francisco, CA, USA.
⁷ Diabetes Center, University of California, San Francisco, San Francisco, CA, USA; Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA.
⁸ Institute for Human Genetics, University of California, San Francisco, San Francisco, CA, USA; Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA; Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA, USA; Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, USA; Chan Zuckerberg Biohub, San Francisco, CA, USA; Parker Institute for Cancer Immunotherapy, San Francisco, CA, USA; Gladstone-UCSF Institute of Genomic Immunology, San Francisco, CA, USA; Division of Rheumatology, Department of Medicine, University of California, San Francisco, San Francisco, CA, USA. Electronic address: jimmie.ye@ucsf.edu.

PMID: 39454576
PMCID: PMC11556465 (available on 2025-10-31)
DOI: 10.1016/j.cell.2024.09.044

Abstract

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.

Keywords: Bootstrap method; CD4 T cells; CELLxGENE Discover; Differential expression; Gene expression variance; Gene-regulatory networks; Memento; Single-cell transcriptomics; Spatial genomics; scRNA-seq analysis.

MeSH terms

Animals
CRISPR-Cas Systems / genetics
Epithelial Cells / metabolism
Gene Expression Profiling / methods
Gene Expression Regulation
Gene Regulatory Networks
Humans
Leukocytes, Mononuclear / metabolism
Mice
Quantitative Trait Loci*
Sequence Analysis, RNA* / methods
Single-Cell Analysis* / methods
T-Lymphocytes / metabolism
Trachea / metabolism

Abstract

MeSH terms

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