sigFeaturePvalue: Find the p-value of those ranked features by using...
In pijush1285/sigFeature: sigFeature: Significant feature selection using SVM-RFE & t-statistic

Description Usage Arguments Details Value Author(s) References See Also Examples

This function will compute the p-value of those ranked features between the two classes by using t-statistic.

1	sigFeaturePvalue(x, y, NumberOfSignificantGene=0, SignificantGeneLilt=0)

`x`	n-by-d data matrix to train (n chips/patients, d clones/genes)
`y`	vector of class labels -1 or 1\'s (for n chips/patients )
`NumberOfSignificantGene`	Number of the selected features.
`SignificantGeneLilt`	Selected feature list.

This function will calculate the p-value.

returns p-value list.

Pijush Das <topijush@gmail.com>, et al.

Peng CH, Liao CT, Peng SC, Chen YJ et al. A novel molecular signature identified by systems genetics approach predicts prognosis in oral squamous cell carcinoma. PLoS One 2011;6(8):e23452. PMID: 21853135

svm, svm.fs

#Example for sigFeaturePvalue() function
#Data set taken from GSE2280
library(SummarizedExperiment)
data(ExampleRawData, package="sigFeature")

x  <- t(assays(ExampleRawData)$counts)
y  <- colData(ExampleRawData)$sampleLabels


#Claculating the p-value.
pvalues <- sigFeaturePvalue(x,y)
#Histogram plot of those p-values.
hist(unlist(pvalues),breaks=seq(0,0.08,0.0015),
xlab = "p-value", ylab = "Frequency", main = "")


#Load the process "sigfeatureRankedList" data.
data("sigfeatureRankedList")
#Claculating the p-value.
pvalues <- sigFeaturePvalue(x, y, 50, sigfeatureRankedList)
#Histogram plot of those p value.
hist(unlist(pvalues),breaks=seq(0,0.08,0.0015), xlab =
"p-value", ylab = "Frequency", main = "")


#Load the process "featureRankedList" data.
data("featureRankedList")
#Claculating the p-value.
pvalues <- sigFeaturePvalue(x, y, 50, featureRankedList)
#Histogram plot of those p value.
hist(unlist(pvalues),breaks=seq(0,0.08,0.0015), xlab =
"p-value", ylab = "Frequency", main = "")