knn_trn | R Documentation |
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.
knn_trn(data, labels, vars_selected, numFold = 10, LOOCV = FALSE)
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 |
numFold |
The number of folds to carry out in the cross validation process. |
LOOCV |
Logical parameter to choose between Loo-CV and KFold-CV. |
A list that contains seven objects. The confusion matrix for each fold, the accuracy, the sensitivity, the specificity and the F1-Scores for each fold and each genes, the best k found for the knn algorithm after tuning, and the predictions made.
dir <- system.file("extdata", package="KnowSeq") load(paste(dir,"/expressionExample.RData",sep = "")) knn_trn(t(DEGsMatrix)[,1:10],labels,rownames(DEGsMatrix)[1:10],3)
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