The FAUST package implements the FAUST method described in New interpretable machine-learning method for single-cell data reveals correlates of clinical response to cancer immunotherapy.
Full Annotation Using Shape-constrained Trees (FAUST) is a method to perform discovery and annotation of phenotypes in single-cell data from flow and mass cytometry experiments.
This implementation targets the gating set data structure in the flowWorkspace Bioconductor package.
When applied to a cytometry study stored in a gating set, the faust R function creates a directory called "faustData" located at the setting of a parameter called "projectPath".
The principal output is an annotated count matrix faustCountMatrix.rds which is written to the "faustData" directory.
- Rows in the count matrix correspond to samples collected in the single cell experiment.
- The columns in the count matrix are cell populations discovered by the pipeline.
- The columns are annotated by a selected subset of markers used in conducting the experiment.
- The column annotations define, in terms of these markers, the phenotypes of all cell populations discovered by the pipeline.
- Entries in the count matrix correspond the number of cells in a sample that belong to a discovered cell population.
This count matrix can be loaded into R using the readRDS function.
There are three vignettes in this packages.
faustIntrois a quick introduction to the main faust functionfaustTuninghas a discussion about how to tune different parameters available in the packagefaustPFDAprovides an example of how to fit a PFDA model to the output count matrix
The FAUST package requires Rcpp and devtools, and that a C++11 compiler is available. Building the vignettes requires the ability to generate png images.
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. An installation (without vignettes) can be performed by first installing:
tryCatch(installed.packages()["BiocManager","Version"],
error = function(e){
install.packages("BiocManager")
})
library(BiocManager)
if (BiocManager::version()!='3.13') {
BiocManager::install(version="3.13")
}
BiocManager::install("Biobase", update = FALSE)
BiocManager::install("flowCore", update = FALSE)
BiocManager::install("flowWorkspace", update = FALSE)
tryCatch(installed.packages()["devtools","Version"],
error = function(e){
install.packages("devtools")
})
library(devtools)
devtools::install_github("RGLab/scamp")
Once these preliminary libraries are installed, run the command:
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")
})
tryCatch(installed.packages()["remotes","Version"],
error = function(e){
install.packages("remotes")
})
tryCatch(installed.packages()["lme4","Version"],
error = function(e){
install.packages("lme4")
})
tryCatch(installed.packages()["multcomp","Version"],
error = function(e){
install.packages("multcomp")
})
remotes::install_github("RGLab/FAUST", force = TRUE, build_vignettes = TRUE)
This takes longer since the vignettes must be built from source. Note that the vignettes will attemp to use 4 threads in order to reduce build time.
After loading FAUST, type vignette('faustIntro') to read a vignette discussing how to use the FAUST function in R.
MacOS users should make sure developer tools and XCode are installed. If you run into installation issues, the function pkgbuild::check_build_tools() may be useful in debugging them. Additionally, the information about R compiler tools for MacOS found here may also be helpful.
If you are trying to install FAUST on Linux with GCC version greater than 8, and you encounter installation errors, the following note may help resolve these errors: protocol buffer issue link.
If you end up using FAUST to analyze cytometry datasets, please consider citing New interpretable machine-learning method for single-cell data reveals correlates of clinical response to cancer immunotherapy.