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

In KnowSeq: KnowSeq R/Bioc package: The Smart Transcriptomic Pipeline

Description

rf_trn allows assessing the final DEGs through a machine learning step by using Random Forest 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.

Usage

rf_trn(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 four objects. The confusion matrix for each fold, the accuracy, the sensitibity and the specificity for each fold and each genes.

Examples

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

rf_trn(t(DEGsMatrix)[,1:10],labels,rownames(DEGsMatrix)[1:10],2)

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