distinct is a statistical method to perform differential testing between two or more groups of distributions; differential testing is performed via non-parametric permutation tests on the cumulative distribution functions (cdfs) of each sample. distinct is a general and flexible tool: due to its fully non-parametric nature, which makes no assumptions on how the data was generated, it can be applied to a variety of datasets. It is particularly suitable to perform differential state analyses on single cell data (i.e., differential analyses within sub-populations of cells), such as single cell RNA sequencing (scRNA-seq) and high-dimensional flow or mass cytometry (HDCyto) data. The method also allows for nuisance covariates (such as batch effects).

Simone Tiberi, Helena L Crowell, Pantelis Samartsidis, Lukas M Weber, and Mark D Robinson (2020).

distinct: a novel approach to differential distribution analyses.

bioRxiv doi: 10.1101/2020.11.24.394213

distinct is available on Bioconductor and can be installed with the command:

if (!requireNamespace("BiocManager", quietly=TRUE))
    install.packages("BiocManager")
BiocManager::install("distinct")

The vignette illustrating how to use the package can be accessed on the Bioconductor website or from R via:

vignette("distinct")

or

browseVignettes("distinct")