netClass: An R Package for Network-Based Biomarker Discovery

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Documentation for package ‘netClass’ version 1.2.1

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netClass-package An R package for network-Based microarray Classification
ad.matrix An adjacency matrix of a sample graph...
calc.diffusionKernelp Computing the Random Walk Kernel matrix of network
classify.aep Training and predicting using aepSVM (aepSVM) classification methods
classify.frsvm Training and predicting using FrSVM
classify.hubc Training and predicting using hub nodes classification methods
classify.pac Training and predicting using PAC classification methods
classify.stsvm Training and predicting using stSVM classification methods
cv.aep Cross validation for aepSVM (aepSVM)
cv.frsvm Cross validation for FrSVM
cv.hubc Cross validation for hub nodes classification
cv.pac Cross validation for Pathway Activities Classification(PAC)
cv.stsvm Cross validation for smoothed t-statistic to select significant top ranked differential expressed genes
EN2SY An list for mapping gene entre ids to symbol ids
expr Two expression profile matrixs and their labels
getGeneRanking Get gene ranking based on geneRank algorithm.
getGraphRank Random walk kernel matrix smoothing t-statistic
Gs2 An subgraph of hub nodes
netClass An R package for network-Based microarray Classification
pGeneRANK GeneRANK
pOfHubs Computing p value of hubs using the permutation test
predictAep Predicting the test tdata using aep trained model
predictFrsvm Predicting the test data using frsvm trained model
predictHubc Predicting the test data using hubc trained model
predictPac Predicting the test data using pac trained model
predictStsvm Predicting the test data using stsvm trained model
probeset2pathway Generae a mean gene expression of genes of each pathway matrix
probeset2pathwayTrain Search CROG in training data
probeset2pathwayTst Applied CROG to testing data
train.aep Training the data using aep methods
train.frsvm Training the data using frsvm method
train.hubc Predicting the data using hub nodes classification model
train.pac Training the data using pac methods
train.stsvm Training the data using stsvm methods