SigBridgeR integrates multiple algorithms, using single-cell RNA sequencing data, bulk expression data, and sample-related phenotypic data, to identify the cells most closely associated with the phenotypic data, performing as a bridge to existing tools.
Usually we recommend installing the latest release from GitHub because of the latest features and bug fixes.
- Install the development version from GitHub:
if (!requireNamespace("pak")) {
install.packages(
"pak",
repos = sprintf(
"https://r-lib.github.io/p/pak/stable/%s/%s/%s",
.Platform$pkgType,
R.Version()$os,
R.Version()$arch
)
)
}
pak::pkg_install("WangLabCSU/SigBridgeR")
- Install from r-universe:
install.packages("SigBridgeR", repos = "https://wanglabcsu.r-universe.dev")
It is recommended to install the following packages:
For better performance:
pak::pkg_install(c(
# faster computation
"sparseMatrixStats",
"matrixStats",
"preprocessCore",
"WGCNA",
"tidyr",
"matrixTests",
"KernSmooth",
"cheapr",
# better gene symbol conversion
"scCustomize",
# parallel computation
"furrr",
"future"
))
For seamless integration with other file types such as .h5ad
pak::pkg_install("anndata")
# or
pak::pkg_install("anndataR") # both are supported, but anndataR is recommended
For visualization:
pak::pkg_install(c(
"ggplot2",
"randomcoloR", # or RColorBrewer
"ggupset", # for upset plot
"patchwork", # for fraction stack plot
"ggforce", # for pca plot
"ggVennDiagram" # for venn diagram
))
To reproduce the tutorial to learn more usage
pak::pkg_install(c(
"zeallot",
"here",
"org.Hs.eg.db"
))
Get Started:
- A Quick Started Guide
- Full Tutorial for more details
- View Github Webpage
- Use
?SigBridgeR::function_nameto access the help documents in R.
If you encounter problems, please check:
- the Troubleshooting Guide, or
- the Github issues page if you want to file bug reports or feature requests
Let us know if you have ideas to make this project better!
