# diffcyt [](https://github.com/lmweber/diffcyt/actions) ## Introduction `diffcyt`: R package for differential discovery in high-dimensional cytometry via high-resolution clustering The `diffcyt` package implements statistical methods for differential discovery analyses in high-dimensional cytometry data (including flow cytometry, mass cytometry or CyTOF, and oligonucleotide-tagged cytometry), based on a combination of high-resolution clustering and empirical Bayes moderated tests adapted from transcriptomics. <p> <img src="vignettes/diffcyt.png" width="130"/> </p> ## Details and citation For details on the statistical methodology and comparisons with existing approaches, see our paper: - [Weber et al. (2019), *diffcyt: Differential discovery in high-dimensional cytometry via high-resolution clustering*, Communications Biology, 2, 183](https://www.nature.com/articles/s42003-019-0415-5) ## Tutorial and examples For a tutorial and examples of usage, see the Bioconductor [package vignette](http://bioconductor.org/packages/release/bioc/vignettes/diffcyt/inst/doc/diffcyt_workflow.html) (link also available via the main Bioconductor page for the [diffcyt package](http://bioconductor.org/packages/diffcyt)). ## Installation The `diffcyt` package is available from [Bioconductor](http://bioconductor.org/packages/diffcyt), and can be installed as follows: ```{r} # Install Bioconductor installer from CRAN install.packages("BiocManager") # Install 'diffcyt' package from Bioconductor BiocManager::install("diffcyt") ``` To run the examples in the package vignette and generate additional visualizations, the `HDCytoData` and `CATALYST` packages from Bioconductor are also required. ```{r} BiocManager::install(c("HDCytoData", "CATALYST")) ```