A statistical approach for identifying differential distributions in single-cell RNA-seq experiments

The ability to quantify cellular heterogeneity is a major advantage of single-cell technologies. However, statistical methods often treat cellular heterogeneity as a nuisance. Researchers from the Dana-Farber Cancer Institute and the University of Wisconsin, Madison present a novel method to characterize differences in expression in the presence of distinct expression states within and among biological conditions. They demonstrate that this framework can detect differential expression patterns under a wide range of settings. Compared to existing approaches, this method has higher power to detect subtle differences in gene expression distributions that are more complex than a mean shift, and can characterize those differences. The freely available R package scDD implements the approach.

Schematic of the presence of two cell states within a cell population
that can lead to bimodal expression distributions

a Time series of the underlying expression state of gene X in a population of unsynchronized single cells, which switches back and forth between a low and high state with means μ ₁ and μ ₂, respectively. The color of cells at each time point corresponds to the underlying expression state. b Population of individual cells shaded by expression state of gene X at a snapshot in time. c Histogram of the observed expression level of gene X for the cell population in (b)

Availability – The method is implemented using version 1.1.0 of the scDD R package, available at https://github.com/kdkorthauer/scDD.