pval.mbmr {BootMRMR}

Computation of statistical significance values for genes using Modified Bootstrap MRMR technique for a particular trait/condition

Description

The statisical significance values (p-values) will be computed for all the genes in the dataset from the non-parametric test "H0: i-th gene is not informative against H1: i-th gene is informative" for selection of informative genes using Modified Bootstrap MRMR technique.

Usage

pval.mbmr(x, y, m, s, Q, plot)

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.

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.

plot

plot is a character string must either take logical value TRUE/FALSE representing whether to plot the statistical significance values of genes in the dataset.

Value

The function returns a vector of p-values for all the genes from the given statistical test in the gene space/dataset 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
pval.mbmr(x, y, m, s, Q, plot=FALSE)