A flexible model for correlated count data, with application to multicondition differential expression analyses of single-cell RNA sequencing data

Yusha Liu, Peter Carbonetto, Michihiro Takahama, Adam Gruenbaum, Dongyue Xie, Nicolas Chevrier, Matthew Stephens

The Annals of Applied Statistics · 2024 · Volume 18 · Issue 3

Abstract

Detecting differences in gene expression is an important part of single-cell 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.

Keywords

differential expression analysis, Empirical Bayes, multivariate count data, Poisson, single-cell RNA sequencing, variational inference

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