knn_trn {KnowSeq} | R Documentation

## knn_trn allows assessing the final DEGs through a machine learning step by using k-NN in a cross validation process.

### Description

knn_trn allows assessing the final DEGs through a machine learning step by using k-NN 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 the k neighbours is done at the start of the process.

### Usage

    knn_trn(data, labels, vars_selected, numFold = 10, LOOCV = FALSE)

### 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.
`LOOCV` | Logical parameter to choose between Loo-CV and KFold-CV.

### Value

A list that contains four objects. The confusion matrix for each fold, the accuracy, the sensitibity and the specificity for each fold and each genes, and the best k found for the knn algorithm after tuning.

### Examples

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

    knn_trn(t(DEGsMatrix)[,1:10],labels,rownames(DEGsMatrix)[1:10],3)

[Package KnowSeq version 1.6.3 Index]