TopGenesPvalSVMRMR: Selection of genes based on statistical significance values...
In sam-uofl/BSM: Gene Selection from Expression Data using 'BSM' Approach

Description Usage Arguments Details Value Author(s) Examples

The function selects the top ranked genes from the high dimensional gene expression data using the statistical significance values computed through Bootstrap-Support Vector Machine-Maximum Relevance and Minimum Redundancy (BSM) approach.

1	TopGenesPvalSVMRMR(x, y, method, beta, nboot, p.adjust.method, 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.
`nboot`	Scalar representing the number of bootsrap samples to be drawn from the data using simple random sampling with replacement (Bootstrap) procedure.
`p.adjust.method`	Character representing the method used for multiple hypothesis correction and computation of adjusted p-values. It can be any method out of "holm", "hochberg", "hommel", "bonferroni", "BH", "BY", "fdr".
`n`	Numeric constant (< N) representing the number of top ranked genes to be selected from the high dimensional gene expression data.

Selection of genes based on statistical significance values computed through BSM approach. Takes 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 BSM 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)))
TopGenesPvalSVMRMR(x, y, method="Linear", beta=0.6, nboot=20, p.adjust.method = "BH", n=5)