svm_CV {KnowSeq} | R Documentation

## svm_CV allows assessing the final DEGs through a machine learning step by using svm in a cross validation process.

### Description

svm_CV allows assessing the final DEGs through a machine learning step by using svm in a cross validation process. This function applies a cross validation of n folds with representation of all classes in each fold. The 80% of the data are used for training and the 20% for test. An optimization of C and G hiperparameters is done at the start of the process.

### Usage

    svm_CV(data, labels, vars_selected, numFold = 10)

### Arguments

`data` | The data parameter is an expression matrix or data.frame that contains the genes in the columns and the samples in the rows.
`labels` | A vector or factor that contains the labels for each of the samples in the data 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.
`numFold` | The number of folds to carry out in the cross validation process.

### Value

A list that contains five objects. The confusion matrix for each fold, the accuracy, the sensitibity and the specificity for each fold and each genes, and a vector with the best parameters found for the SVM algorithm after tuning.

### Examples

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

    svm_CV(t(DEGsMatrix), labels, rownames(DEGsMatrix), 2)

[Package KnowSeq version 1.1.27 Index]