TopGenesSVMRMR: Selects the top ranked (differentially expressed) genes...
In sam-uofl/BSM: Gene Selection from Expression Data using 'BSM' Approach

Description Usage Arguments Details Value Author(s) Examples

This function returns the list of top ranked genes selected through Support Vector Machine-Maximum Relevance and Minimum Redundancy (SVM-MRMR) method.

1	TopGenesSVMRMR(x, y, method, beta, n)

`x`	Nxp data frame of gene expression values, where, N represents number of genes and p represents samples/time points generated in a case vs. control gene expression study.
`y`	px1 numeric vector with entries 1 and -1 representing sample/subject labels, where 1 and -1 represents the labels of subjects/ samples for case and control conditions respectively.
`method`	Character variable representing either 'Linear' or 'Quadratic' method for integrating the weights/scores computed through SVM and MRMR methods.
`beta`	scalar representing trade-off between SVM and MRMR weights.
`n`	Numeric constant (< N) representing the number of top ranked genes to be selected from the gene expression data.

Selects the top ranked (differentially expressed) genes through SVM-MRMR method. Take the gene expression data matrix (rows as genes and coloumns as samples) and vector of class labels of subjects (1: case and -1: control) as inputs

A list of differentially expressed specified number of genes through SVM-MRMR method.

Samarendra Das <samarendra4849 at gamil.com>

x=as.data.frame(matrix(runif(1000), 50))
row.names(x) = paste("Gene", 1:50)
colnames(x) = paste("Samp", 1:20)
y=as.numeric(c(rep(1, 10), rep(-1, 10)))
TopGenesSVMRMR(x, y, method="Linear", beta=0.6, n=5)