The ASURI (Analysis of SUrvival and patients RIsk prediction based on gene signatures) package discovers marker genes that are related to risk prediction capabilities and to a clinical variable of interest. It uses two main steps, including subsampling glmnet and unicox. The package implements robust functions to discover survival markers related to a clinical phenotype and to predict a risk score, allowing to study the patient’s risk based on the gene signatures. Several plots are provided to visualise the relevance of the genes, the risk score, and patient stratification, as well as a robust version of the Kaplan-Meier curves.
Modern medicine based on omic technologies provides a new approach to the study of diseases because it renders a way to interrogate about the role of the genes as biomolecular markers associated with the risk, prognosis, or outcome of the patients. In this regard, the discovery and validation of survival biomarkers associated with a given phenotype or with a specific clinical variable is a critical step to achieve better disease prognosis and prediction. Furthermore, accurate risk prediction and patients stratification based on such predictions will help to advance in personalized treatments and precision medicine. Currently, there are not many tools available to discover genetic survival markers or to assess the prognostic capacity of specific gene signatures. Moreover, it is common that gene signatures discovered are not reproducible and robust, and cannot be correlated well with clinical phenotypes, or with the stages and outcome of the disease. Besides, there are not easy integrated tools providing molecular based assessment of patients risk and survival.
The asuri package provides an integrated set of functions to analyse disease SURVIVAL and provide patient RISK predictions based on gene signatures. The tool allows: (1) Discovery of robust and reproducible gene lists associated with disease survival based on gene expression or on another gene-related activity signal (geneSurv); (2) Discovery of gene markers by identification of the significant association of gene expression (or other gene-related signal) with clinical variables or phenotypic characteristics (genePheno); (3) Construction of robust patient risk predictors based on gene signatures using univariate and multivariate approaches (patientRisk).
A. Berral-Gonzalez, S. Bueno-Fortes, N. Alonso-Moreda, J.M. Sanchez-Santos, M. Martin-Merino Acera and J. De Las Rivas
-Bioinformatics and Functional Genomics Group- Cancer Research Centre (CiC-IBMCC, USAL/CSIC/IBSAL) Salamanca (Spain)
Learn more at http://bioinfow.dep.usal.es/.
If you use asuri in published research, please cite:
- Bueno-Fortes S, Berral-Gonzalez A, Sanchez-Santos J, Martin-Merino M, De Las Rivas J (2023). Identification of a gene expression signature associated with breast cancer survival and risk that improves clinical genomic platforms.. Bioinformatics Advances, 3(1), vbad037. ISSN 26350041, doi:10.1093/BIOADV/VBAD037
You can install the stable version from Bioconductor:
if (!requireNamespace("BiocManager", quietly=TRUE))
install.packages("BiocManager")
BiocManager::install("BiocUpgrade") ## you may need this
BiocManager::install("asuri")or the development version of asuri from GitHub with:
install.packages("remotes")
remotes::install_github("jdelasrivas-lab/asuri")