dksTrain: Perform Dual KS Discriminant Analysis
In dualKS: Dual KS Discriminant Analysis and Classification

Description Usage Arguments Details Value Author(s) See Also Examples

This function will perform dual KS discriminant analysis on a training set of gene expression data (in the form of an ExpressionSet) and a vector of classes describing which of (two or more) classes each column of data corresponds to. Genes will be be ranked based on the degree to which they are upregulated or downregulated in each class, or both. Discriminant gene signatures are then extracted using dksSelectGenes and applied to new samples with dksClassify.

1	dksTrain(eset, class, type = "up", verbose=FALSE, weights=FALSE, logweights=TRUE, method='kort')

`eset`	Gene expression data in the form of an `ExpressionSet` or `matrix`
`class`	A factor with two or more levels indicating which class each sample in the expression set belongs OR an integer indicating which column of pData(eset) contains this information.
`type`	One of "up", "down", or "both" indicating whether you want to analyze and classify based on up or down regulated genes, or both (note that classification of samples based on down regulated genes from single color experiments should be expected to work well due to the noise at low expression levels. Therefore, 'down', or 'both' should only be used for two color experiments or one color data that has been converted to ratios based on some reference sample(s).)
`verbose`	Set to TRUE if you want more evidence of progress while data is being processed. Set to FALSE if you want your CPU cycles to be used on analysis and not printing messages.
`weights`	Value determines whether and how genes are weighted when building the signatures. See details.
`logweights`	Should the weights be log10 transformed prior to applying?
`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 genes that are highly enriched in a subset of samples of a given class, but highly down regulated in another subset of that same class.

This function calculates the Kolmogorov-Smirnov rank sum statistic for each gene and each level of 'class'. The highest scoring genes can then be extracted for use in classification.

If weights=FALSE, signatures are defined based on the ranks of members of each class when sorted on each gene. Those genes for which a given class has the highest rank when sorting samples by those genes will be included in the classifier, with no regard to the absolute expression level of those genes. This is the classic KS statistic.

Very discriminant genes identified in this way may or may not be the highest expressed genes. The result is that signatures identified in this way have arbitrary "baseline" values. This may lead to misclassification when comparing two signatures (using, for example, dksClassify). Therefore, one may wish to weight genes based on absolute expression level, or some other metric.

Setting weights = TRUE causes the genes to be weighted according to the log (base 10) of the relative rank of the mean expression of each gene in each class. Alternatively, you may provide your own weight matrix as the argument to weights. This matrix must have one column for each possible value of class, and one row for each gene in eset. Note that for type='down' or the down component of type='both', the weight matrix will be inverted as 1-matrix, so the range of weights should be 0 - 1 for each class. NAs are handled "gracefully" by discarding any genes for which any column of the corresponding row of weights is NA. Our experience has been that weights that are a linear function of some feature of the gene expression (like mean) can be too subtle. The effect of the weights can be increased by setting logweights=TRUE (which is the default).

An object of class DKSGeneScores.

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)
	summary(pr, pData(eset)[,1])
	show(pr)
	plot(pr, actual=pData(eset)[,1])