IGST.weight.bootmrmrsvm {IGST}

Computation of weights for informative genes or gene set selection using SVM and MRMR feature selection technique with bootstrapping procedure

Description

The function computes the weights associated with each genes for a given dataset using SVM and MRMR feature selection technique with bootstrapping procedure.

Usage

IGST.weight.bootmrmrsvm (x, y, re, v)

Arguments

x

x is a n by p data frame of gene expression values where rows represent genes and columns represent samples. Each cell entry represents the expression level of a gene in a sample or subject (row names of x as gene names or gene ids).

y

y is a p by 1 numeric vector with entries 1 or -1 representing sample labels, where, 1\/-1 represents the sample label of subjects or samples for stress or control condition(for two class problems).

v

v is a scalar representing the weightage of a method and must be within 0 and 1.

re

re is a scalar representing the number of bootstrap generated, re must be sufficiently large (i.e. number of times bootstrap samples are generated.

Value

The function returns a vector of weights associated with each genes computed from SVM and MRMR feature selection technique with bootstrapping procedure for a given dataset.

Author(s)

Nitesh Kumar Sharma, Dwijesh Chandra Mishra, Neeraj Budhlakoti and Md. Samir Farooqi

References

Wang, J., Chen, L., Wang, Y., Zhang, J., Liang, Y., & Xu, D. (2013). A computational systems biology study for understanding salt tolerance mechanism in rice. PLoS One, 8(6), e64929.

Ding, C., & Peng, H. (2005). Minimum redundancy feature selection from microarray gene expression data. Journal of Bioinformatics and Computational Biology, 3(02), 185-205.

Mishra DC, Kumar S, Lal SB, Saha A, Chaturvedi KK, Budhlakoti N, et al.( 2018) TAGPT: A Web Server for Prediction of Trait Associated Genes using Gene Expression Data. Annals of Genetics and Genetic Disorder. 1(1): 1003.

Examples

#################################
library(IGST)
data(rice_cold)
x<-rice_cold[-1,]
y<-rice_cold[1,]
y<-as.matrix(y)
y<-as.vector(y)
#s<-10
#Q<-0.5
v<-0.5
re<-10
IGST.weight.bootmrmrsvm (x, y, re, v)