Weights.mrmr: Computation of MRMR weights for gene selection

In BootMRMR: Bootstrap-MRMR Technique for Informative Gene Selection

Description

The MRMR weights associated with each gene in the dataset are computed by using the MRMR technique for informative gene selection.

Usage

Weights.mrmr(x, y)

Arguments

x	x is a N by p dataframe of gene expression, where, rows as genes and columns as samples (with row names as gene names/ids)
y	y is a p by 1 numeric vector with entries 1 and -1 representing sample labels, where 1 and -1 represents the sample label of subjects/ samples for stress and control condition respectively.

Details

This function returns a vector of MRMR weights for all genes in the dataset.

Author(s)

Samarendra Das

References

Ding, C and Peng, H (2005). Minimum redundancy feature selection from microarray gene expression data. J. Bioinformatics Comput Biol 3(2):185-205.

Examples

data(rice_salt)
x=as.data.frame(rice_salt[-1,])
y=as.numeric(rice_salt[1,])
Weights.mrmr(x, y)

BootMRMR documentation built on May 1, 2019, 7:49 p.m.