X_proteomic

Toy dataset from BREAST CANCER. Protein data. (https://github.com/pilargmarch/multiomics2.0/tree/main)

datasets

This software package provides Cox survival analysis for high-dimensional and multiblock datasets.
It encompasses a suite of functions dedicated from the classical Cox regression to newest analysis,
including Cox proportional hazards model, Stepwise Cox regression, and Elastic-Net Cox regression,
Sparse Partial Least Squares Cox regression (sPLS-COX) incorporating three distinct strategies,
and two Multiblock-PLS Cox regression (MB-sPLS-COX) methods. This tool is designed to adeptly handle
high-dimensional data, and provides tools for cross-validation, plot generation, and additional resources
for interpreting results. While references are available within the corresponding functions,
key literature is mentioned below.
Terry M Therneau (2024) <https://CRAN.R-project.org/package=survival>,
Noah Simon et al. (2011) <doi:10.18637/jss.v039.i05>,
Philippe Bastien et al. (2005) <doi:10.1016/j.csda.2004.02.005>,
Philippe Bastien (2008) <doi:10.1016/j.chemolab.2007.09.009>,
Philippe Bastien et al. (2014) <doi:10.1093/bioinformatics/btu660>,
Kassu Mehari Beyene and Anouar El Ghouch (2020) <doi:10.1002/sim.8671>,
Florian Rohart et al. (2017) <doi:10.1371/journal.pcbi.1005752>.

Pedro Salguero Garcia

Coxmos

Cox MultiBlock Survival

Pedro Salguero García

Sonia Tarazona Campos

Ana Conesa Cegarra

Kassu Mehari Beyene

Luis Meira Machado

Marta Sestelo

Artur Araújo

X_proteomic function

A data frame with 150 observations and 369 features:<dl></dl>

Format

X_proteomic Data — X_proteomic

A data frame with 150 observations and 369 features:<dl>

</dl>

X_proteomic Data

X_proteomic: X_proteomic Data

Description

Usage

Arguments

Format