GSSTDA is a new package for R that implements a new analysis for trascriptomic data, the Progression Analysis of Disease with Survival (PAD-S) by Fores-Martos et al. (2022), which allows to identify groups of samples differentiated by both survival and idiosyncratic biological features. Although it was designed for transcriptomic analysis, it can be used with other types of continuous omics data. The package implements the main algorithms associated with this methodology, which first removes the part of expression that is considered physiological using the Disease-Specific Genomic Analysis (DSGA) and then analyzes it using an unsupervised classification scheme based on Topological Data Analysis (TDA), the Mapper algorithm. The implementation includes code to perform the different steps of this analysis: data preprocessing by DSGA, the selection of genes for further analysis and a new filter function, which integrates information about genes related to survival, and the Mapper algorithm for generating a topological invariant Reeb graph. These functions can be used independently, although a function that performs the entire analysis is provided. This paper describes the methodology and implementation of these functions, and reports numerical results using an extract of real data base application.
Supplementary materials are available in addition to this article. It can be downloaded at RJ-2024-025.zip
rtracklayer, Rsubread, survcomp, scMappR, GSSTDA, fRMA
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For attribution, please cite this work as
Esteve, et al., "GSSTDA: Implementation in an R Package of the Progression of Disease with Survival Analysis (PAD-S) that Integrates Information on Genes Linked to Survival in the Mapper Filter Function", The R Journal, 2025
BibTeX citation
@article{RJ-2024-025,
author = {Esteve, Miriam and Bosch-Romeu, Raquel and Falco, Antonio and Fores, Jaume and Climent, Joan},
title = {GSSTDA: Implementation in an R Package of the Progression of Disease with Survival Analysis (PAD-S) that Integrates Information on Genes Linked to Survival in the Mapper Filter Function},
journal = {The R Journal},
year = {2025},
note = {https://doi.org/10.32614/RJ-2024-025},
doi = {10.32614/RJ-2024-025},
volume = {16},
issue = {3},
issn = {2073-4859},
pages = {90-114}
}