A FLEXIBLE MODEL FOR CORRELATED COUNT DATA, WITH APPLICATION TO MULTICONDITION DIFFERENTIAL EXPRESSION ANALYSES OF SINGLE-CELL RNA SEQUENCING DATA

• 成果类型: Article
• 署名作者: Liu, Yusha; Carbonetto, Peter; Takahama, Michihiro; Gruenbaum, Adam; Xie, Dongyue; Chevrier, Nicolas; Stephens, Matthew
• 署名单位: University of North Carolina; University of North Carolina Chapel Hill; University of North Carolina School of Medicine; University of Chicago; University of Chicago; Institute for Health Metrics & Evaluation; University of Washington; University of Washington Seattle; University of Chicago
• 刊物名称: ANNALS OF APPLIED STATISTICS
• ISSN/ISSBN: 1932-6157
• DOI: 10.1214/24-AOAS1894
• 发表日期: 2024
• 页码: 2551-2575
• 关键词: gene-expression; t-cells; gamma production; fold-change; seq data; inflammation; dispersion; induction; inference; il-1

摘要

Detecting differences in gene expression is an important part of singlecell 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 multicondition 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 at https://github.com/stephenslab/poisson. mash.alpha.