The FAUST package implements the FAUST method described in A new data-driven cell population discovery and annotation method for single-cell data, FAUST, reveals correlates of clinical response to cancer immunotherapy.
The FAUST package requires Rcpp and devtools, and that a C++11 compiler is available.
Currently faust must be installed from its source. It depends on the scamp package.
The most recent version can be installed from github using devtools in R. A quick installation (without vignettes) can be performed by:
tryCatch(installed.packages()["BiocManager","Version"],
error = function(e){
install.packages("BiocManager")
})
library(BiocManager)
BiocManager::install("Biobase", update = FALSE)
BiocManager::install("flowCore", update = FALSE)
BiocManager::install("flowWorkspace", update = FALSE)
BiocManager::install("flowWorkspaceData", update = FALSE)
BiocManager::install("cytolib", update = FALSE)
BiocManager::install("CytoML", update = FALSE)
library(devtools)
devtools::install_github("RGLab/scamp")
devtools::install_github("RGLab/FAUST")
To build the vignettes during installation, instead run:
tryCatch(installed.packages()["knitr","Version"],
error = function(e){
install.packages("knitr")
})
tryCatch(installed.packages()["rmarkdown","Version"],
error = function(e){
install.packages("rmarkdown")
})
tryCatch(installed.packages()["ggdendro","Version"],
error = function(e){
install.packages("ggdendro")
})
devtools::install_github("RGLab/FAUST", build = TRUE, build_opts = c("--no-resave-data", "--no-manual"))
This takes longer since the vignettes must be built from source.
After loading FAUST, type vignette('faustIntro') to read a vignette discussing how to use the FAUST function in R.
If you end up using FAUST to analyze cytometry datasets,
please consider citing A new data-driven cell population discovery and annotation method for single-cell data, FAUST, reveals correlates of clinical response to cancer immunotherapy.
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