svm_test {KnowSeq}R Documentation

svm_test allows assessing the final DEGs through a machine learning step by using SVM with a test dataset.

Description

svm_test allows assessing the final DEGs through a machine learning step by using SVM with a test dataset. An optimization of C and G hiperparameters is done at the start of the process.

Usage

svm_test(train, labelsTrain, test, labelsTest, vars_selected)

Arguments

train

The train parameter is an expression matrix or data.frame that contains the training dataset with the genes in the columns and the samples in the rows.

labelsTrain

A vector or factor that contains the training labels for each of the samples in the train object.

test

The test parameter is an expression matrix or data.frame that contains the test dataset with the genes in the columns and the samples in the rows.

labelsTest

A vector or factor that contains the test labels for each of the samples in the test object.

vars_selected

The genes selected to classify by using them. It can be the final DEGs extracted with the function limmaDEGsExtraction or a custom vector of genes. Furthermore, the ranking achieved by featureSelection function can be used as input of this parameter.

Value

A list that contains four objects. The confusion matrix, the accuracy, the sensitibity and the specificity for each genes.

Examples

dir <- system.file("extdata", package="KnowSeq")
load(paste(dir,"/expressionExample.RData",sep = ""))

trainingMatrix <- t(DEGsMatrix)[c(1:4,6:9),]
trainingLabels <- labels[c(1:4,6:9)]
testMatrix <- t(DEGsMatrix)[c(5,10),]
testLabels <- labels[c(5,10)]

svm_test(trainingMatrix,trainingLabels,testMatrix,testLabels,rownames(DEGsMatrix)[1:10])

[Package KnowSeq version 1.0.0 Index]