| mrmr.cutoff {BootMRMR} | R Documentation |
Informative geneset selection using MRMR weights
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)