diffcyt: R package for differential discovery in high-dimensional cytometry via high-resolution clustering
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.
For details on the statistical methodology and comparisons with existing approaches, see our paper introducing the
diffcyt framework, available as a preprint from bioRxiv:
diffcyt package is freely available from Bioconductor. The stable release version can be installed using the Bioconductor installer as follows. Note that installation requires R version 3.4.0 or later.
# 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
CATALYST packages from Bioconductor are also required.
If required, the development version of the
diffcyt package can be installed through the
devel version of Bioconductor or from GitHub. The development version may include additional updates that have not yet been included in the release version. Note that we recommend using the release version in most cases, since this has been more thoroughly tested.
To set up the
devel version of Bioconductor, see the Bioconductor help pages. To install the development version of the
diffcyt package directly from GitHub, use the
devtools package as follows. When installing from GitHub, dependency packages will also need to be installed separately from CRAN and Bioconductor.
# Install 'devtools' package from CRAN install.packages("devtools") # Install development version of 'diffcyt' package from GitHub library(devtools) install_github("lmweber/diffcyt")
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