srdata

High dimensional protein gene expression data

datasets

Perform high dimensional Feature Selection in the presence of survival outcome.
Based on Feature Selection method and different survival analysis, it will obtain the best
markers with optimal threshold levels according to their effect on disease progression
and produce the most consistent level according to those threshold values.
The functions' methodology is based on by Sonabend et al (2021) <doi:10.1093/bioinformatics/btab039> and Bhattacharjee et al (2021) <arXiv:2012.02102>.

Atanu Bhattacharjee

highMLR

Feature Selection for High Dimensional Survival Data

Gajendra K. Vishwakarma

Souvik Banerjee

srdata function

A dataframe with 288 rows and 250 variables<dl>
<dt>ID</dt>
<dd>"Column/Variable name" consisting id of subjects</dd><dt>Visit</dt>
<dd>"Column/Variable name" consisting number of times observations recorded</dd><dt>event</dt>
<dd>"Column/Variable name" consisting survival event</dd><dt>OS</dt>
<dd>"Column/Variable name" consisting duration of overall survival</dd><dt>C6kine,.....,GFRalpha4</dt>
<dd>High dimensional covariates</dd></dl>

Format

High dimensional protein gene expression data — srdata

A dataframe with 288 rows and 250 variables<dl>
<dt>ID</dt>
<dd>"Column/Variable name" consisting id of subjects</dd>

<dt>Visit</dt>
<dd>"Column/Variable name" consisting number of times observations recorded</dd>

<dt>event</dt>
<dd>"Column/Variable name" consisting survival event</dd>

<dt>OS</dt>
<dd>"Column/Variable name" consisting duration of overall survival</dd>

<dt>C6kine,.....,GFRalpha4</dt>
<dd>High dimensional covariates</dd>

</dl>

srdata: High dimensional protein gene expression data

Description

Usage

Arguments

Format

Examples