Single-Sample Predictors for Breast Cancer
Single-Sample Predictors for Breast Cancer (sspbc) include functions and models to assign classes to breast cancer samples. It works on raw gene expression data.
The included single-sample predictor (SSP) models were developed using gene expression data from RNAseq generated using HiSat/StringTie. Detailed description on training and validation of the provided SSP models is available in the manuscript by Staaf et al. npj Breast Cancer 2022 (https://doi.org/10.1038/s41523-022-00465-3). Training was performed using the previously described AIMS method from Paquet and Hallett (2014) J Natl Cancer Inst 107(1):357 using scripts available from the AIMS GitHub repository (https://github.com/meoyo/AIMS).
Description on how to use sspbc with examples and testdata is provided with the R package.
For citation use: Staaf, J. et al. RNA sequencing-based single sample predictors of molecular subtype and risk of recurrence for clinical assessment of early-stage breast cancer. npj Breast Cancer 8, 94 (2022). https://doi.org/10.1038/s41523-022-00465-3
sspbc depends on R packages e1071, methods, stats, and grid. Make sure to install these dependencies before installing sspbc.
To use the sspbc R package download and install from the provided source package.
R CMD INSTALL sspbc_1.0.tar.gz
install.packages("~/Downloads/sspbc_1.0.tar.gz", repos = NULL, type="source")
Go to 'Packages and Data' and select 'Package Installer'.
Under Packages Repository select 'Local Source Package' and click 'Install...' then browse to find the downloaded source package file.