# distinct: a method for differential analyses via hierarchical permutation tests <img src="inst/extdata/distinct.png" width="200" align="right"/> `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 (2023). > > distinct: a novel approach to differential distribution analyses. > > The Annals of Applied Statistics. > Available [here](https://www.e-publications.org/ims/submission/AOAS/user/submissionFile/52840?confirm=11abdc0f) ## Bioconductor installation `distinct` is available on [Bioconductor](https://bioconductor.org/packages/distinct) and can be installed with the command: ``` r if (!requireNamespace("BiocManager", quietly=TRUE)) install.packages("BiocManager") BiocManager::install("distinct") ``` ## Vignette The vignette illustrating how to use the package can be accessed on [Bioconductor](https://bioconductor.org/packages/distinct) or from R via: ``` r vignette("distinct") ``` or ``` r browseVignettes("distinct") ```