mrmr.cutoff: Informative geneset selection using MRMR weights

In BootMRMR: Bootstrap-MRMR Technique for Informative Gene Selection

Description

The function returns the informative genes/ geneset for the particular trait/condition under investigation using Maximum Relevance and Minimum Redundancy (MRMR) technique.

Usage

mrmr.cutoff(x, y, n)

Arguments

x	x is a N by p data frame of gene expression values where rows represent genes and columns represent samples/subject/time point. Each cell entry represents the expression level of a gene in a sample/subject (row names of x as gene names/gene 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.
n	n is a numeric constant represents the number of informative genes to be selected.

Value

An informative geneset is obtained, which is relevanit to the particular trait/condition and the genes within the selected geneset are minimum redundant using MRMR technique.

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,])
n=20
mrmr.cutoff(x, y, n)

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