The ability to quantify cellular heterogeneity is a major advantage of single-cell technologies. Although understanding such heterogeneity is of primary interest in a number of studies, for convenience, statistical methods often treat cellular heterogeneity as a nuisance factor. A team led by researchers at the Dana-Farber Cancer Institute have developed a novel method to characterize differences in expression in the presence of distinct expression states within and among biological conditions. Using simulated and case study data, they demonstrate that the modeling framework is able to detect differential expression patterns of interest under a wide range of settings. Compared to existing approaches, scDD has higher power to detect subtle differences in gene expression distributions that are more complex than a mean shift, and is able to characterize those differences.
Schematic of the presence of two cell states within a cell population which 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 mean 1 and 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 freely available R package scDD that implements the approach is available at https://github.com/kdkorthauer/scDD.