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

TL;DR

This paper introduces a new statistical model for multi-condition differential gene expression analysis in single-cell RNA sequencing data, improving detection accuracy by modeling counts across all conditions simultaneously.

Contribution

The authors develop 'Poisson multivariate adaptive shrinkage', a novel method that enhances detection and estimation of expression differences across multiple conditions in single-cell data.

Findings

Improved detection of differential expression across multiple conditions.

Effective sharing of information across conditions enhances accuracy.

Method outperforms existing approaches in real data analysis.

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 multi-condition 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 online at https://github.com/stephenslab/poisson.mash.alpha.