fit.DRWPClassGM: Train a classifier using DRW-GM
In cuihaibo1/DRWPClass: DRW-GM

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

DRW-GM is a disease classification method which performs pathway-based classifier construction and precise disease status prediction by joint analysis of genomic and metabolomic data and pathway topology.

fit.DRWPClassGM(xG, yG.class1, yG.class2, xM = NULL, yM.class1 = NULL, 
                yM.class2 = NULL, DEBUG = FALSE, pathSet, globalGraph,
                testStatistic = c("t-test", "SAM"), classifier = "Logistic", 
                normalize = TRUE, nFolds = 5, numTops = 50, iter = 1, 
                Gamma = 0.7, Alpha = 0.5, fdr.output = 0.2)

`xG`	a p x n matrix of gene expression measurements with p genes and n samples.
`yG.class1`	a integer vector comprising the indexes of class 1 samples in `xG`.
`yG.class2`	a integer vector comprising the indexes of class 2 samples in `xG`.
`xM`	a m x n matrix of metabolite expression measurements with m metabolites and n samples.
`yM.class1`	a integer vector comprising the indexes of class 1 samples in `xM`.
`yM.class2`	a integer vector comprising the indexes of class 2 samples in `xM`.
`DEBUG`	Logical. Should debugging information be plotted.
`pathSet`	A list of pathways. Each pathway is represented as a vector of pathway member genes and metabolites.
`globalGraph`	An `igraph` R object contains the global directed gene-metabolite pathway graph.
`testStatistic`	The test method used to identify differential genes. For `testStatistic="t-test"`, function `caltScore` is used. For `testStatistic="SAM"`, function `calSAMScore` is used.
`classifier`	The method to train classifiers. The default is "Logistic". To use other methods, such as "libsvm", one should install the corresponding package in Weka.
`normalize`	Logical flag for `xG` and `xM` standardization, prior to fitting the model. Default is `normalize=TRUE`.
`nFolds`	The number of folds to split `xG`. Default is 5.
`numTops`	The number of pathway features used for feature selection. Default is 50.
`iter`	The number of runs to split `xG` for optimal classifier selection. Default is 1.
`Gamma`	A numeric value. The restart probability in directed random walk. Default is 0.7.
`Alpha`	A proportional coefficient to balance the initial weights of genes and metabolites, which are used to construct the initial weights W0 for directed random walk.
`fdr.output`	(Approximate) False Discovery Rate cutoff for output in significant genes table. Default is 0.2.

DRW-GM uses directed random walk to evaluate the topological importance of each gene in reconstructed gene-metabolite graph through integrating information from matched gene expression profiles and metabolomic profiles. The topological importance of genes are used to weight the genes for inferring robust DRW-GM-based pathway activities. Then the pathway activities are selected to train the classifier.

Fitted "DRWPClassGM" model object.

`model`	Fitted `"fitModel"` model object.
`AUC`	The performance (AUC) of the classifier on feature selection set.
`Accuracy`	The performance (Accuracy) of the classifier on feature selection set.
`pathFeatures`	The selected pathway features to build the classifier.
`geneFeatures`	The genes used to infer the pathways in `pathFeatures`
`tScore`	The t statistic and p-value of each gene in `xG`
`vertexWeight`	The topological weights of vertexes in `globalGraph`
`pathSet`	The pathways used to construct the global directed gene-metabolite graph.
`globalGraph`	An `igraph` R object. The global directed gene-metabolite pathway graph.
`testStatistic`	The test method used to identify differential genes.
`classifier`	The method to train classifiers.
`nFolds`	The number of folds to split `xG`.
`numTops`	The number of pathway features used for feature selection.
`iter`	The number of runs to split `xG` for optimal classifier selection.
`Gamma`	The restart probability in directed random walk.
`Alpha`	The proportional coefficient to balance the initial weights of genes and metabolites.

Wei Liu

Liu, W., et al., Topologically inferring risk-active pathways toward precise cancer classification by directed random walk. Bioinformatics, 2013. 29(17): p. 2169-77.

predict.DRWPClassGM

	data(GProf8511)
	data(MProf)
	data(pathSet)
	data(dGMGraph)
	fit <- fit.DRWPClassGM(xG=GProf8511$mRNA_matrix, yG.class1=GProf8511$normal, yG.class2=GProf8511$PCA,
                           xM=MProf$Meta_matrix, yM.class1=MProf$normal, yM.class2=MProf$PCA, DEBUG=TRUE, 
                           pathSet=pathSet, globalGraph=dGMGraph, testStatistic="t-test", classifier = "Logistic", 
                           normalize = TRUE, nFolds = 5, numTops=50, iter = 1, Gamma=0.7, Alpha = 0.5)