dksClassify: Predict classes for gene expression sets.
In dualKS: Dual KS Discriminant Analysis and Classification

Description Usage Arguments Value Author(s) See Also Examples

Kolmogorov-Smirnov rank sum scoring will be used to assign one or more samples to one of two or more classes based on previously defined gene signatures (see dksTrain).

1	dksClassify(eset, classifier, rescale=FALSE, method="kort")

`eset`	An `ExpressionSet` or `matrix` containing the gene expression data for the samples to be classified.
`classifier`	An `DKSClassifier` produced by `dksSelectGenes` describing the gene expression signature for each class.
`rescale`	If TRUE, scores for each class will be mean centered and normalized to remove arbitrary differences in scale and baseline value between signatures for different classes.
`method`	Two methods are supported. The 'kort' method returns the maximum of the running sum. The 'yang' method returns the sum of the maximum and the minimum of the running sum, thereby penalizing classes that are highly enriched in a subset of genes of a given signature, but highly down regulated in another subset of that same signature.

An object of class DKSPredicted containing the class to which each sample in the eset was assigned as well as other information. This object has its own summary and show functions useful for displaying this information in a user friendly format.

Eric J. Kort, Yarong Yang

dksTrain, dksSelectGenes, dksClassify, DKSGeneScores, DKSPredicted, DKSClassifier

	data("dks")
	tr <- dksTrain(eset, 1, "up")
	cl <- dksSelectGenes(tr, 100)
	pr <- dksClassify(eset, cl,rescale=FALSE)
	summary(pr, pData(eset)[,1])
	show(pr)
	plot(pr, actual=pData(eset)[,1])