mbmr.pval.cutoff {BootMRMR}

Selection of informative geneset based on statistical significance value using Modified Bootstrap MRMR technique

Description

The informative geneset which has maximum relevance with target class/trait and minimum redundancy among genes are selected based on p-values obtained from Modified Bootstrap MRMR technique.

Usage

mbmr.pval.cutoff(x, y, m, s, Q, 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/-1 representing sample labels, where 1/-1 represents the sample label of subjects/ samples for stress/control condition (for two class problems).

m

m is a scalar representing the size of the Modified Bootstrap Sample (i.e. Out of p samples/subjects, m samples/subjects are randomly drawn with replacement, which constitutes one Modified Bootstrap Sample).

s

s is a scalar representing the number of Modified Bootstrap samples (i.e. number of times each of the m samples/subjects will be resampled from p samples/subjects).

Q

Q is a scalar representing the quartile value of the gene rankscores (lies within 1/N to 1), usually the second quartile, i.e. 0.5 or third quartile i.e. 0.75

n

n is a scalar representing the size of the informative gene set to be obtained.

Value

The function returns a list of the genes/ geneset which are highly informative to the particular trait/condition under investigation using Modified Bootstrap MRMR technique.

Author(s)

Samarendra Das

Examples

data(rice_salt)
x=as.data.frame(rice_salt[-1,])
y=as.numeric(rice_salt[1,])
m=36
s=80
Q=0.5
n=20
mbmr.pval.cutoff(x, y, m, s, Q, n)