Class predictions of new samples using a ROC based classifier obtained by tr.rocc()
a ROC based classifier (containing the classifier specifications). This object is generated in training data using tr.rocc()
a matrix containing the new samples, with genes as rows and samples as columns. rownames(g) and colnames (g) must be specified. All features of the classifier (trocc$genes) have to be present in the rownames of the matrix.
The classifier specifications of the trocc object from classifier training are used to classify new samples. The metagene value of the new sample is calculated using the information from trocc$positiv and trocc$negativ. If the metagene value is higher than the threshold value (obtained from trocc$cutoffvalue) the new sample is predicted to be of class 1, else to be of class 0.
a named factor vector with levels 0 and 1 containing the predictions.
p.rocc() requires a trocc object generated by the tr.rocc() function
Lauss M, Frigyesi A, Ryden T, Hoglund M. Robust assignment of cancer subtypes from expression data using a uni-variate gene expression average as classifier. BMC Cancer 2010 (in print)
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#### tr.rocc ### Random Dataset and phenotype set.seed(100) ## Dataset should be a matrix g <- matrix(rnorm(1000*25),ncol=25) rownames(g) <- paste("Gene",1:1000,sep="_") colnames(g) <- paste("Sample",1:25,sep="_") ## Phenotype should be a factor with levels 0 and 1: out <- as.factor(sample(c(0:1),size=25,replace=TRUE)) predictor <- tr.rocc (g,out,xgenes=50) ## find classifier specification: predictor$positiv predictor$negativ predictor$cutoffvalue #### p.rocc ### just an example: classification of the training samples p.rocc(trocc=predictor,newsample=g) predictions<-p.rocc(trocc=predictor,newsample=g) table(predictions,out) ## all correctly classified because newsample is the training set ## (try UNSEEN validation data instead)
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