distinct: a novel approach to differential distribution analyses

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 and high-dimensional flow or mass cytometry data. High-throughput single-cell data reveal an unprecedented view of cell identity and allow complex variations between conditions to be discovered; nonetheless, most methods for differential expression target differences in the mean and struggle to identify changes where the mean is only marginally affected. distinct is based on a hierarchical non-parametric permutation ap- proach and, by comparing empirical cumulative distribution functions, iden- tifies both differential patterns involving changes in the mean, as well as more subtle variations that do not involve the mean. We performed extensive bench- marks across both simulated and experimental datasets from single-cell RNA sequencing and mass cytometry data, where distinct shows favourable per- formance, 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.