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.

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 with all dependencies:

pak::pkg_install("WangLabCSU/SigBridgeR", dependencies = TRUE)

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", "rTiRank")
pak::pkg_install(file.path("Exceret", methods))

unnecessary but recommended:

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)

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

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
))

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

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

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

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!

This package is currently under development. Please cite the preprint if you use it in your research.

scSurvival: Single-cell data (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 data (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 data + target scRNA-seq data + (optional) bulk RNA-seq data 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

scSurv: scRNA-seq data + bulk RNA-seq expression matrix (with matched survival time and event status) -> per-cell hazard scores and prognostic gene sets

beyondcell: the expression matrix + a collection of drug signatures -> drug-related commonalities between cells/spots