Bioc package: The Smart Transcriptomic Pipeline

rf_trn

R Documentation

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

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)

CasedUgr/KnowSeq documentation built on Aug. 16, 2022, 6:19 a.m.