A0786
Title: A flexible model for correlated count data, with application to multi-condition differential gene expression analyses
Authors: Yusha Liu - University of North Carolina at Chapel Hill (United States) [presenting]
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). However, 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. It is shown 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. The potential of this new approach is illustrated by analyzing data from a single-cell experiment studying the effects of cytokine stimulation on gene expression.
