SigBridgeR

🌐 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")

2. 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!

🗺️ Similar Projects

scSurvival: Single-cell expression matrix (log-normalized + HVG-selected) + survival data (optional clinical covariates and batch labels) -> Survival-associated cell subpopulations

CellPhenoX: Single-cell multi-omics data + bulk-level clinical variables, covariates (optional interaction effect terms) -> interpretable score per cell

scPrognosis: scRNA-seq count matrix (imputed by MAGIC + filtered for low coverage/expression) + bulk RNA-seq expression matrix (with matched survival time and event status) -> breast cancer prognostic gene signatures and Cox PH risk prediction model

SCellBOW: source scRNA-seq expression matrix + target scRNA-seq expression matrix + (optional) bulk RNA-seq expression matrix with paired survival data -> cell embeddings, cluster assignments, UMAP visualizations, and phenotype‑algebra‑derived risk scores with survival probability curves for individual cell subpopulations.

scPER: Single-cell RNA-seq data + bulk RNA-seq data + celltype annotation (optional batch labels) -> phenotype-associated cell populations