diffcyt

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.

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

Tutorial and examples

For a tutorial and examples of usage, see the Bioconductor package vignette (link also available via the main Bioconductor page for the diffcyt package).

Installation

The diffcyt package is available from Bioconductor, and can be installed as follows:

# 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.

BiocManager::install(c("HDCytoData", "CATALYST"))