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netClass (version 1.1)

netClass: An R Package for Network-Based Biomarker Discovery

Description

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) smoothed t-statistic (stSVM).

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Version

Install

install.packages('netClass')

Monthly Downloads

12

Version

1.1

License

GPL (>= 2)

Maintainer

Yupeng Cun

Last Published

June 4th, 2013

Functions in netClass (1.1)

ad.matrix

An adjacency matrix of a sample graph...
netClass-package

An R package for network-Based microarray Classification
classify.hubc

Training and predicting using hub nodes classification methods
classify.aep

Training and predicting using aepSVM (aepSVM) classification methods
classify.stsvm

Training and predicting using stSVM classification methods
getGeneRanking

Get gene ranking based on geneRank algorithm.
classify.pac

Training and predicting using PAC classification methods
Gs2

An subgraph of hub nodes
expr

Two expression profile matrixs and their labels
probeset2pathway

Generae a mean gene expression of genes of each pathway matrix
aep.cv

Cross validation for aepSVM (aepSVM)
FrSVM.cv

Cross validation for FrSVM
pOfHubs

Computing p value of hubs using the permutation test
getGraphRank

Random walk kernel matrix smoothing t-statistic
stsvm.cv

Cross validation for smoothed t-statistic to select significant top ranked differential expressed genes
probeset2pathwayTrain

Search CROG in training data
pac.cv

Cross validation for Pathway Activities Classification(PAC)
classify.frsvm

Training and predicting using FrSVM
hubc.cv

Cross validation for hub nodes classification
probeset2pathwayTst

Applied CROG to testing data
pGeneRANK

GeneRANK
calc.diffusionKernelp

Computing the Random Walk Kernel matrix of network