GitHub - WangLabCSU/SigBridgeR: SigBridgeR: Integrative Framework and Toolkit for Single-Cell Screening of Phenotype-Associated Cells

🌐 Overview

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.

🔧 Installation

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:

SigBridgeR includes the Scissor and scAB algorithms by default. In addition to these, installing the following packages allows you to use additional algorithms.

methods <- c("scPAS", "scPP", "DEGAS", "LPSGL", "PIPET", "rSIDISH", "SCIPAC")
pak::pkg_install(file.path("Exceret", methods))

unnecessary but recommended:

For better performance:

pak::pkg_install(c(
  # faster computation
  "sparseMatrixStats",
  "matrixStats",
  "preprocessCore",
  "tidyr",
  "matrixTests",
  "KernSmooth",
  "cheapr",
  # better gene symbol conversion
  "scCustomize",
  # parallel computation
  "furrr",
  "future"
))

if (!requireNamespace("BiocManager")) {
  install.packages("BiocManager")
}
# faster computation
BiocManager::install("WGCNA)

For seamless integration with single-cell RNA-seq data stored in `.h5ad`:

pak::pkg_install("anndata")
# or
pak::pkg_install("anndataR") # both are supported

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 use the built-in cell annotation methods:

pak::pkg_install(c(
  # SingleR
  "SingleR-inc/SingleR",
  "celldex",
  # mLLMCelltype
  "mLLMCelltype",
  "plyr",
  # CellTypist
  "reticulate",
  "AnnDataR"
))

To add custom extension functions to SigBridgeR:

pak::pkg_install(c(
  "tictoc",
  "codetools",
  "knitr",
  "lintr",
  "rstudioapi",
  "yonicd/tidycheckUsage"
))

To reproduce the tutorial to learn more usage:

pak::pkg_install(c(
  "zeallot",
  "here",
  "org.Hs.eg.db",
  "processx"
))

📓 Documentation

Get Started:

View Github Webpage
A Quick Started Guide
Start from spatial transcriptome
Full Tutorial for more details
Use ?SigBridgeR::function_name to 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. Pull requests are welcome!