BreastSubtypeR

BreastSubtypeR is an assumption-aware, multi-method R/Bioconductor package with a local Shiny app. It consolidates published intrinsic subtyping methods under one API and lets you run multiple classifiers at once. AUTO inspects cohort diagnostics to select compatible methods and reduce misclassification.

Research use only; in clinical practice, intrinsic molecular subtyping is standardised via approved diagnostics (e.g., Prosigna®).

📄 Publication: NAR Genomics and Bioinformatics (2025), Editor’s Choice → doi:10.1093/nargab/lqaf131

How to cite (plain text & BibTeX)

Plain text

Yang Q, Hartman J, Sifakis EG. BreastSubtypeR: a unified R/Bioconductor package for intrinsic molecular subtyping in breast cancer research. NAR Genomics and Bioinformatics. 2025. https://doi.org/10.1093/nargab/lqaf131

BibTeX

@article{Yang2025BreastSubtypeR,
  author  = {Yang, Qiao and Hartman, Johan and Sifakis, Emmanouil G.},
  title   = {BreastSubtypeR: a unified R/Bioconductor package for intrinsic molecular subtyping in breast cancer research},
  journal = {NAR Genomics and Bioinformatics},
  year    = {2025},
  doi     = {10.1093/nargab/lqaf131},
  url     = {https://doi.org/10.1093/nargab/lqaf131}
}

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Features

• Unified interface for published methods: consolidates PAM50 variants, AIMS, ssBC/sspbc, and others under one consistent API.
• Run multiple methods at once (BS_Multi): execute several classifiers in a single call and compare results side by side.
• AUTO (cohort-aware selection): checks ER/HER2 distribution, subtype purity, and subgroup sizes; disables incompatible classifiers.
• Method-specific pre-processing: automatically routes raw RNA-seq counts, precomputed FPKM, or log2-processed microarray/nCounter matrices.
• Robust mapping: Entrez ID–based gene mapping with conflict resolution.
• Local Shiny app (iBreastSubtypeR): point-and-click analysis; data stay on your machine.
• Reproducibility: Bioconductor distribution, unit tests, vignettes, and SummarizedExperiment compatibility.

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Methods included (single-method implementations)

Method id	Short description	Group	Reference
parker.original	Original PAM50 by Parker et al., 2009	NC-based	Parker et al., 2009
genefu.scale	PAM50 implementation as in the genefu R package (scaled version)	NC-based	Gendoo et al., 2016
genefu.robust	PAM50 implementation as in the genefu R package (robust version)	NC-based	Gendoo et al., 2016
cIHC	Conventional ER-balancing using immunohistochemistry (IHC)	NC-based	Ciriello et al., 2015
cIHC.itr	Iterative version of cIHC	NC-based	Curtis et al., 2012
PCAPAM50	Selects IHC-defined ER subsets, then uses Principal Component Analysis (PCA) to create ESR1 expression-based ER-balancing	NC-based	Raj-Kumar et al., 2019
ssBC	Subgroup-specific gene-centering PAM50	NC-based	Zhao et al., 2015
ssBC.v2	Updated subgroup-specific gene-centering PAM50 with refined quantiles	NC-based	Fernandez-Martinez et al., 2020
AIMS	Absolute Intrinsic Molecular Subtyping (AIMS) method	SSP-based	Paquet & Hallett, 2015
sspbc	Single-Sample Predictors for Breast Cancer (AIMS adaptation)	SSP-based	Staaf et al., 2022

(See the vignette for implementation details.)

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Installation

Install the released version from Bioconductor:

# Requires R >= 4.5.0
if (!require("BiocManager", quietly = TRUE)) install.packages("BiocManager")
BiocManager::install("BreastSubtypeR")

# Devel:
BiocManager::install("BreastSubtypeR", version = "devel")

Or install from GitHub:

if (!require("remotes", quietly = TRUE)) install.packages("remotes")
remotes::install_github("yqkiuo/BreastSubtypeR")

Quick start

These examples use datasets shipped with the package. For your own data, provide a SummarizedExperiment with clinical metadata in colData (e.g., PatientID, ER, HER2; for ROR: TSIZE, NODE).

library(BreastSubtypeR)

# Example data
data("BreastSubtypeRobj")
data("OSLO2EMIT0obj")

1) Map & prepare (method-specific pre-processing + mapping)

data_input <- Mapping(OSLO2EMIT0obj$se_obj, RawCounts = FALSE, method = "max", impute = TRUE)

2) Multi-method run (user-defined)

res <- BS_Multi(data_input = data_input, methods = c("parker.original","PCAPAM50","sspbc"))
head(res$res_subtypes, 5)

3) AUTO mode (cohort-aware selection) + visualize

res_auto <- BS_Multi(data_input = data_input, methods = "AUTO")
Vis_Multi(res_auto$res_subtypes)

4) Launch the local Shiny app

BreastSubtypeR::iBreastSubtypeR() # interactive GUI (local)

Notes:

• The app runs locally; no data leave your machine.
• If you see a missing UI dependency:

install.packages(c("shiny","bslib"))

Vignette & documentation

A comprehensive usage guide (input types, AUTO details, full method descriptions) is included as a vignette.

See function help pages for specifics (e.g., ?BS_Multi, ?Mapping, ?iBreastSubtypeR).

Contributing & issues

Contributions and issue reports are welcome: https://github.com/yqkiuo/BreastSubtypeR/issues.

License

GPL-3