knn_test: knn_test allows assessing the final DEGs through a machine...

Description Usage Arguments Value Examples

View source: R/knn_test.R

Description

knn_test allows assessing the final DEGs through a machine learning step by using k-NN with a test dataset. An optimization of the k neighbours is done at the start of the process.

Usage

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knn_test(train, labelsTrain, test, labelsTest, vars_selected, bestK)

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.

bestK

Best K 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

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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)]
bestK <- 3 # the one that has been selected
results_test_knn <- knn_test(trainingMatrix, trainingLabels, testMatrix, testLabels, rownames(DEGsMatrix)[1:10], bestK)

KnowSeq documentation built on April 16, 2021, 6:01 p.m.