Package: SigBridgeR 3.2.0

Yuxi Yang

SigBridgeR: Integrative Toolkit for Linking Phenotypes to Cell Subpopulations via scRNA-seq and Bulk Data

Identifies phenotype-associated cell subpopulations by integrating phenotypic data (e.g., survival, drug sensitivity), bulk gene expression, and single-cell RNA-seq (scRNA-seq) profiles. It employs multiple algorithms to reliably connect cellular features with clinical or functional phenotypes, offering a unified pipeline for multi-modal analysis that uncovers biologically and clinically relevant cell states in heterogeneous datasets.

Authors:Yuxi Yang [cre, aut], Zeyu Yan [ctb]

SigBridgeR_3.2.0.tar.gz
SigBridgeR_3.2.0.zip(r-4.6)SigBridgeR_3.2.0.zip(r-4.5)
SigBridgeR_3.2.0.tgz(r-4.6-any)SigBridgeR_3.2.0.tgz(r-4.5-any)
SigBridgeR_3.2.0.tar.gz(r-4.6-any)SigBridgeR_3.2.0.tar.gz(r-4.5-any)
SigBridgeR_3.2.0.tgz(r-4.5-emscripten)
SigBridgeR.pdf |SigBridgeR.html✨
SigBridgeR/json (API)

# Install 'SigBridgeR' in R:

install.packages('SigBridgeR', repos = c('https://wanglabcsu.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/wanglabcsu/sigbridger/issues

On CRAN:

phenotype-mapping single-cell

5.88 score 5 stars 7 scripts 32 exports 216 dependencies

Last updated from:fe078fca08. Checks:7 NOTE, 2 OK. Indexed: yes.

The latest version of this package failed to build. Look at thebuild logs for more information.

Target	Result	Time
linux-devel-x86_64	NOTE	345
source / vignettes	OK	427
linux-release-x86_64	NOTE	323
macos-devel-arm64	NOTE	615
macos-release-arm64	NOTE	163
windows-devel	NOTE	153
windows-release	NOTE	158
windows-oldrel	NOTE	194
wasm-release	OK	323

Exports:AddMetaFeature AddMisc AggregateDupCols AggregateDupRows AggregateDups BulkPreProcess DoDEGAS DoLP_SGL DoPIPET DoscAB DoScissor DoscPAS DoscPP FindRobustElbow getFuncOption ListPyEnv LoadRefData MergeResult Pattern2Colname QCFilter QCPatternDetect RegisterScreenMethod SCIntegrate SCPreProcess Screen ScreenFractionPlot ScreenStrategy ScreenUpset setFuncOption SetupPyEnv SymbolConvert ValidateScreenFunc

Dependencies:abind annotate AnnotationDbi askpass AUCell base64enc BH Biobase BiocGenerics BiocParallel Biostrings bit bit64 bitops blob bslib cachem caTools chk cli cluster codetools commonmark cowplot cpp11 crayon crosstalk curl data.table DBI DEGAS DelayedArray DelayedMatrixStats deldir DESeq2 digest diptest dotCall64 dplyr dqrng edgeR evaluate farver fastDummies fastmap fastmatch fgsea fitdistrplus FNN fontawesome formatR fs futile.logger futile.options future future.apply generics GenomicRanges ggplot2 ggrepel ggridges globals glue goftest gplots graph gridExtra GSEABase gtable gtools here highr htmltools htmlwidgets httpuv httr ica IDConverter igraph IRanges irlba isoband jquerylib jsonlite KEGGREST kernlab KernSmooth knitr ks labeling lambda.r later lattice lazyeval leidenAlg lifecycle limma listenv lmtest locfit LPSGL magrittr MASS Matrix MatrixGenerics matrixStats mclust memoise mgcv mime miniUI mixtools multicool multimode mvtnorm nlme openssl otel parallelly patchwork pbapply pbmcapply pillar PIPET pkgconfig plogr plotly plyr png polyclip pracma pROC processx progressr promises ps purrr R.methodsS3 R.oo R.utils R6 RANN rappdirs RColorBrewer Rcpp RcppAnnoy RcppArmadillo RcppEigen RcppHNSW RcppProgress RcppTOML reshape2 reticulate rlang rmarkdown ROCR rootSolve rprojroot RSpectra RSQLite Rtsne S4Arrays S4Vectors S7 sass scAB scales scattermore sccore Scissor scPAS ScPP sctransform segmented Seqinfo Seurat SeuratObject SGL shiny SigBridgeRUtils sitmo snow sourcetools sp spam SparseArray sparseMatrixStats spatstat.data spatstat.explore spatstat.geom spatstat.random spatstat.sparse spatstat.univar spatstat.utils statmod stringi stringr SummarizedExperiment survival sys tensor tibble tidyr tidyselect tinytex utf8 uwot vctrs viridisLite withr xfun XML xtable XVector yaml zoo

Auxiliary Utils

Rendered fromOther_Function_Details.Rmdusingknitr::rmarkdownon Jan 24 2026.

Last update: 2026-01-20
Started: 2025-12-23

Extending SigBridgeR: A Guide for Custom Extensions

Rendered fromExtending.Rmdusingknitr::rmarkdownon Jan 24 2026.

Last update: 2026-01-20
Started: 2026-01-20

Find Optimal Parameters for My Screening

Rendered fromOptimal_Params.Rmdusingknitr::rmarkdownon Jan 24 2026.

Last update: 2025-12-23
Started: 2025-12-23

Full Tutorial

Rendered fromFull_Tutorial.Rmdusingknitr::rmarkdownon Jan 24 2026.

Last update: 2026-01-15
Started: 2025-07-07

Quick Start Guide for SigBridgeR

Rendered fromQuick_Start.Rmdusingknitr::rmarkdownon Jan 24 2026.

Last update: 2026-01-20
Started: 2025-07-07

Troubleshooting

Exceret

Rendered fromTroubleshooting.Rmdusingknitr::rmarkdownon Jan 24 2026.

Last update: 2025-12-24
Started: 2025-07-07

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
Add Gene-Level Metadata to Seurat Object (Vectorized, ...-based)	AddMetaFeature
Safely Add Miscellaneous Data to Seurat Object	AddMisc
Aggregate Rows or Columns with Duplicate Names	aggregate-dups AggregateDupCols AggregateDupRows AggregateDups
Bulk RNA-seq Data Preprocessing and Quality Control Function	BulkPreProcess
Run DEGAS Analysis for Single-Cell and Bulk RNA-seq Data Integration	DoDEGAS
Perform LP-SGL Screening Analysis	DoLP_SGL
Perform PIPET Screening Analysis	DoPIPET
Perform scAB Screening Analysis	DoscAB
Perform Scissor Screening Analysis	DoScissor
Perform scPAS Screening Analysis	DoscPAS
Perform scPP screening analysis	DoscPP
Automatically determine optimal PCA dimensions using multiple robust methods	FindRobustElbow
Configuration Functions for SigBridgeR Package	getFuncOption
List Available Python Environments	ListPyEnv
Download & Load Reference Data	LoadRefData
Merge Multiple Screening Analysis Results	MergeResult
convert regex patterns to column names (internal)	Pattern2Colname
Filter Seurat object cells by QC metrics	QCFilter
Calculate Percentage of Features Matching Patterns	QCPatternDetect
Register a Custom Screening Method for Phenotype-Driven Analysis	RegisterScreenMethod
Single-Cell RNA-seq Preprocessing Pipeline	SCPreProcess SCPreProcess.default SCPreProcess.R6 SCPreProcess.Seurat
Single-Cell Data Screening	Screen
Visualization of Cell Screening Fractions	ScreenFractionPlot
Registry of Phenotype-Associated Cell Screening Methods	ScreenStrategy
ScreenUpset - Visualize cell type intersections from screened Seurat object	ScreenUpset
Configuration Functions for SigBridgeR Package	setFuncOption
Create or Use Python Environment with Required Packages	SetupPyEnv
Convert Ensembles Version IDs & TCGA Version IDs to Genes in Bulk Expression Data	SymbolConvert
Validate Custom Screening Function Compliance	ValidateScreenFunc