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Repo StatusLicense: GPL3 SigBridgeR status badgeR CMD check registry status badge


🌐 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.

  1. 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")
  1. 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"
))

📓 Documentation

Get Started:

If you encounter problems, please check:

Let us know if you have ideas to make this project better!

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Screening for phenotype-associated cells at the single-cell level

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