mbmr.weight.cutoff: Identification of informative geneset based on weights...

Description Usage Arguments Value Author(s) Examples

Description

The function enables to find set of informative genes based on weights which are obtained by maximising the relevancy of genes with classes/condition/trait and minimising the redundancy among genes using Modified Bootstrap-MRMR technique

Usage

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mbmr.weight.cutoff(x, y, m, s, 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).

n

n is a numeric constant representing the number of informative genes to be selected from the large gene space.

Value

The function returns a set of genes, which are highly informative to the trait or condition under consideration based Modified Bootstrap-MRMR weights.

Author(s)

Samarendra Das

Examples

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

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