pvalsvmmrmr: Computation statistical significance values for gene...
In sam-uofl/BSM: Gene Selection from Expression Data using 'BSM' Approach

Description Usage Arguments Details Value Author(s) Examples

The function computes ths rank scores, statisical significance values, adjusted p-values of gene informativeness to a particular condition using the Bootstrap-Support Vector Machine (SVM)-Maximum Relevance and Minimum Redundancy (MRMR) (BSM) approach from high dimensional gene expression data .

1	pvalsvmmrmr(x, y, method, beta, nboot, p.adjust.method, plot)

`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".
`plot`	Character representing whether the plots need to be drawn or not. It can take value either TRUE or FALSE.

Computation statistical significance values for gene selection 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.

This function returns returns a vector of rank scores, p-values and adjusted p-values for all genes in the gene expression data using BSM approach.

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)))
pvalsvmmrmr(x, y, method="Linear", beta=0.6, nboot=20, p.adjust.method = "BH", plot=FALSE)