Abstract We present distinct, a statistical method to perform, via hierarchical permutation tests, differential analyses between groups of densities. distinct can be applied to a variety of datasets, and is particularly suitable to perform differential analyses on single cell data.
While most methods for differential expression target differences in the mean abundance between conditions, single-cell data can show more complex variations. distinct, by comparing full distributions, identifies, both, differential patterns involving changes in the mean, as well as more subtle variations that do not involve the mean.
We will present results, based on single cell RNA sequencing (scRNA-seq) and high-dimensional flow or mass cytometry(HDCyto) simulated and experimental datasets.
distinct is freely available as a Bioconductor R package.