KS: Calculate Kolmogorov Smirnov rank sum scores.
In dualKS: Dual KS Discriminant Analysis and Classification

Description Usage Arguments Value Author(s) See Also Examples

This function calculates the degree to which a subset of genes (i.e. a "signature") is biased in the ordered list of all genes. The function is typically used internally by dksClassify, but the user may want to call it directly to inspect the running sums.

1	KS(data, geneset, decreasing=TRUE, method="kort")

`data`	A vector of gene expression data. The data need not be sorted, as the function will sort it itself.
`geneset`	A `DKSGeneSet` object, such as one of the slots of the `DKSClassifier` returned by `link{dksClassify}`.
`decreasing`	Indicates which way `data` should be sorted. If TRUE, the degree of upregulation will be scored. If FALSE, the degree of down regulation will be scored.
`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.

`runningSums`	A matrix with 1 row per gene and 1 column per signature. The value is the running sum of the KS metric at each point along the sorted list of genes. The maximum of this column vector corresponds to the KS score for the corresponding signature.
`ksScores`	A named vector giving the KS score for each signature.

Eric J. Kort, Yarong Yang

dksTrain, dksSelectGenes, dksClassify, DKSGeneScores, DKSPredicted, DKSClassifier

	data("dks")
	tr <- dksTrain(eset, 1, "both")
	cl <- dksSelectGenes(tr, 100)
	sc <- KS(exprs(eset)[,1], cl@genes.up)
	plot(sc$runningSums[,1], type='l')