rf_test: rf_test allows assessing the final DEGs through a machine...
In KnowSeq: KnowSeq R/Bioc package: The Smart Transcriptomic Pipeline

Description Usage Arguments Value Examples

rf_test allows assessing the final DEGs through a machine learning step by using Random Forest with a test dataset.

1	rf_test(train, labelsTrain, test, labelsTest, vars_selected, bestParameters)

`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 `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 ntree and mtry parameters selected during the training phase.

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

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)]
bestParameters <- 30
rf_test(trainingMatrix, trainingLabels, testMatrix, testLabels,rownames(DEGsMatrix)[1:10], bestParameters = bestParameters)