Bioc package: The Smart Transcriptomic Pipeline

svm_test

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, bestParameters)

Arguments

`train`	The train parameter is an expression matrix or data.frame that contains the train dataset with the genes in the columns and the samples in the rows.
`labelsTrain`	A vector or factor that contains the train 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 `DEGsExtraction` or a custom vector of genes. Furthermore, the ranking achieved by `featureSelection` function can be used as input of this parameter.
`bestParameters`	Best values for C and gamma parameters selected during the training phase.

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)]
results_svm_cv <- svm_trn(trainingMatrix, trainingLabels, rownames(DEGsMatrix)[1:10], 2)
bestParameters <- results_svm_cv$bestParameters
svm_test(trainingMatrix, trainingLabels, testMatrix, testLabels,rownames(DEGsMatrix)[1:10], bestParameters)

CasedUgr/KnowSeq documentation built on Aug. 16, 2022, 6:19 a.m.