PredictioR is an R package designed for comprehensive biomarker discovery in Immuno-Oncology (IO) therapy response. It supports pan-cancer, cancer-specific, and treatment-specific analyses. The package also enables assessment of key clinical variables such as age and sex. Multiple algorithms are included for IO response prediction, along with methods for computing IO signature scores.
Resources:
- 🧬 IO Data: Clinical and molecular profiles – available at ORCESTRA
- 🧾 IO Signatures: Curated IO gene signatures – available from SignatureSets GitHub repository
The latest version of PredictioR is available at the PredictioR GitHub repository. The package is not yet on CRAN or Bioconductor.
✅ Requirements
- R version 4.4.1 or higher
📦 Install Dependencies
if (!requireNamespace("BiocManager", quietly = TRUE)) install.packages("BiocManager")
dependencies <- c('MultiAssayExperiment', 'survival', 'survcomp', 'GSVA', 'meta', 'ggplot2', 'ggrepel')
for (pkg in dependencies) {
if (!requireNamespace(pkg, quietly = TRUE)) BiocManager::install(pkg, update = FALSE)
}
📥 Install PredictioR from GitHub
devtools::install_github("bhklab/PredictioR")
library(PredictioR)
💻 Clone the Repository (optional)
git clone https://github.com/bhklab/PredictioR
cd PredictioR
More details about function usage and computational methods are provided in the package documentation and vignettes, or via the web application at predictio.ca.
PredictioR/
├── 📁 R/ – Core package functions
├── 📁 data/ – Selected and curated IO signatures and datasets
├── 📁 man/ – Function documentation (.Rd files)
├── 📁 vignettes/ – Workflows and usage examples
├── 📄 DESCRIPTION – Package metadata
└── 📄 README.md – Overview and setup instructions
If you use PredictioR or its datasets in your work, please cite the following papers:
📌 This is version: v1.0, corresponding to the release used in the paper.