KS
(Package: dualKS) :
Calculate Kolmogorov Smirnov rank sum scores.
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.
● Data Source:
BioConductor
● Keywords: classif
● Alias: KS
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The function dksSelectGenes returns an object of class DKSClassifier that contains two DKSGeneSet objects: one for each signature (upregulated and downregulated), needed for subsequent classification. This way, the gene scoring does not need to be repeated for classifiers based on different numbers of genes (see DKSGeneScores and dksTrain).
● Data Source:
BioConductor
● Keywords: classes
● Alias: DKSClassifier, class:DKSClassifier
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The function dksTrain returns on object of class DKSGeneScore to hold the analysis results for subsequent classifier extraction and classification. This way, the gene scoring does not need to be repeated for classifiers based on different numbers of genes.
● Data Source:
BioConductor
● Keywords: classes
● Alias: DKSGeneScores, class:DKSGeneScores
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This class contains a vector of genes, and a corresponding factor of classes that indicates to which signature each gene belongs. The function dksSelectGenes extracts gene signatures of a specified size from an object of class DKSGeneScores.
● Data Source:
BioConductor
● Keywords: classes
● Alias: DKSGeneSet, DKSGeneSet-class, class:DKSGeneSet
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The function dksClassify calculates a score for each possible class (as determined by the DKSGeneSet passed to it) for each sample in the test set passed to the function. It then determines which class each sample most likely belongs to based on which of those scores is the largest. All this information is useful after the analysis, and so it is retained in the return object of class DKSPredicted.
● Data Source:
BioConductor
● Keywords: classes
● Alias: DKSPredicted, DKSPredicted-class, class:DKSPredicted, plot,DKSPredicted,missing-method, plot,DKSPredicted-method, show,DKSPredicted-method, summary,DKSPredicted-method
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dksClassify
(Package: dualKS) :
Predict classes for gene expression sets.
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).
● Data Source:
BioConductor
● Keywords: classif
● Alias: dksClassify
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dksCustomClass
(Package: dualKS) :
Create a classification object from predefined gene signature.
This utility function will build a DKSClassifier object from your own list of gene ids for use by dksClassify. This is useful if you want to use the classification funtionality of this package, but already have gene signatures you want to use (as opposed to generating them with dksTrain.
● Data Source:
BioConductor
● Keywords: classif
● Alias: dksCustomClass
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dksPerm
(Package: dualKS) :
Estimate significance of signature scores.
The distribution of Kolmogorov Rank Sum scores generated by this package depends on a variety of factors including the size of the signature and the total number of genes measured in each sample. For a given classifier, this function bootstraps an approximate distribution for the scores and then identifies optimum parameters for the gamma distribution that best fits the bootstrap distribution. The corresponding gamma probability function is then returned, allowing p-values for one or more scores to be readily computed.
● Data Source:
BioConductor
● Keywords: classif
● Alias: dksPerm
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dksSelectGenes
(Package: dualKS) :
Extract gene signatures from a DKSGeneScores object.
The DKSGeneScores returned by dksTrain holds the rank data for all the genes in the original ExpressioSet. However, generally only the top n genes for each class are desired for classification. Rather than needing to re-run dksTrain every time a signature of different size (n) is desired, you simply extract that top n genes from this object using dksSelectGenes.
● Data Source:
BioConductor
● Keywords: classif
● Alias: dksSelectGenes
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dksTrain
(Package: dualKS) :
Perform Dual KS Discriminant Analysis
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.
● Data Source:
BioConductor
● Keywords: classif
● Alias: dksTrain
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