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.
1 2 3 4 5 |
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 |
yG.class2 |
a integer vector comprising the indexes of class 2 samples in |
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 |
yM.class2 |
a integer vector comprising the indexes of class 2 samples in |
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 |
testStatistic |
The test method used to identify differential genes. For |
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 |
nFolds |
The number of folds to split |
numTops |
The number of pathway features used for feature selection. Default is 50. |
iter |
The number of runs to split |
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 |
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 |
tScore |
The t statistic and p-value of each gene in |
vertexWeight |
The topological weights of vertexes in |
pathSet |
The pathways used to construct the global directed gene-metabolite graph. |
globalGraph |
An |
testStatistic |
The test method used to identify differential genes. |
classifier |
The method to train classifiers. |
nFolds |
The number of folds to split |
numTops |
The number of pathway features used for feature selection. |
iter |
The number of runs to split |
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.
1 2 3 4 5 6 7 8 | 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)
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