Distinct: a novel approach to differential distribution analyses

Abstract

High-throughput single-cell data reveal an unprecedented view of cell identity and allow showing complex variations between conditions; nonetheless, most methods for differential expression target differences in the mean, and struggle to identify changes where the mean is not or marginally affected. Here, we present distinct, a general method for differential analysis of full distributions that is well suited to applications on single-cell data, such as single-cell RNA sequencing (scRNA-seq) and high-dimensional flow or mass cytometry (HDCyto) data. distinct is based on a hierarchical non-parametric permutation approach and, by comparing empirical cumulative distribution functions (ECDFs), identifies both differential patterns involving changes in the mean, as well as more subtle variations that do not involve the mean. We performed extensive benchmarks across both simulated and experimental data, where distinct shows very favourable performance, identifies more differential patterns than competitors, and displays good control of false positive and false discovery rates. distinct is available as a Bioconductor R package.