hnscc

High dimensional head and neck cancer 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

hnscc function

A dataframe with 565 rows and 104 variables<dl>
<dt>ID</dt>
<dd>"Column/Variable name" consisting id of subjects</dd><dt>Death</dt>
<dd>"Column/Variable name" consisting survival event</dd><dt>OS</dt>
<dd>"Column/Variable name" consisting duration of overall survival</dd><dt>PFS</dt>
<dd>"Column/Variable name" consisting duration of progression free survival</dd><dt>Prog</dt>
<dd>"Column/Variable name" consisting progression event</dd><dt>GJB1,...,HMGCS2</dt>
<dd>High dimensional covariates</dd></dl>

Format

High dimensional head and neck cancer survival and gene expression data — hnscc

A dataframe with 565 rows and 104 variables<dl>
<dt>ID</dt>
<dd>"Column/Variable name" consisting id of subjects</dd>

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

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

<dt>PFS</dt>
<dd>"Column/Variable name" consisting duration of progression free survival</dd>

<dt>Prog</dt>
<dd>"Column/Variable name" consisting progression event</dd>

<dt>GJB1,...,HMGCS2</dt>
<dd>High dimensional covariates</dd>

</dl>

hnscc: High dimensional head and neck cancer survival and gene expression data

Description

Usage

Arguments

Format

Examples