netClass: netClass: An R Package for Network-Based Biomarker Discovery

netClass is an R package for network-based feature (gene) selection for biomarkers discovery via integrating biological information. This package adapts the following 5 algorithms for classifying and predicting gene expression data using prior knowledge: 1) average gene expression of pathway (aep); 2) pathway activities classification (PAC); 3) Hub network Classification (hubc); 4) filter via top ranked genes (FrSVM); 5) network smoothed t-statistic (stSVM).

AuthorYupeng Cun
Date of publication2013-12-03 22:44:46
MaintainerYupeng Cun <yupeng.cun@gmail.com>
LicenseGPL (>= 2)
Version1.2.1

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Man pages

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...

classify.frsvm: Training and predicting using FrSVM

classify.hubc: Training and predicting using hub nodes classification...

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...

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-package: 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...

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

Questions? Problems? Suggestions? or email at ian@mutexlabs.com.

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