sigFeaturePvalue {sigFeature} | R Documentation |
This function will compute the p-value of those ranked features between the two classes by using t-statistic.
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 = "")