fNet

an R program, short of Filter by highly ranked gene for Support Vector Machine, for microarray classification. R program and Supplment meterial for "Yupeng Cun, Holger Fröhlich (2012) Integrating Prior Knowledge Into Prognostic Biomarker Discovery Based on Network Structure. arXiv:1212.3214 "

FrSVM is the short of Filter by highly ranked gene for Support Vector Mchine. Download source codes here. FrSVM is a feature selection algorithm which integrates protein-protein interaction network information into gene selection for prognostic biomarker discovery.

How to run FrSVM:

1. Geting gene expression profiles (GEP), PPi Network.

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Geing GEP

#---------------------------------------------------------------------------------- library(GEOquery) a = getGEO("GSExxxxx", destdir="/home/YOURPATH/")

Normalized the GEP by limma

x= t(normalizeBetweenArrays(exprs(a), method="quantile") )

defien your classes labes, y, as a factor

y= facotr("Two Class")

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mapping probest IDs to Entrez IDs

take hgu133a paltform as example

#--------------------------------------------------------------------------------- library('hgu133a.db') mapped.probes<-mappedkeys(hgu133aENTREZID) refseq<-as.list(hgu133aENTREZID[mapped.probes]) times<-sapply(refseq, length) mapping <- data.frame(probesetID=rep(names(refseq),times=times), graphID=unlist(refseq),row.names=NULL, stringsAsFactors=FALSE) mapping<- unique(mapping)

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Summarize probests to genes of x by limma

ad.ppi: Adjacencen matrix of PPI network

#--------------------------------------------------------------------------------- Gsub=ad.ppi mapping <- mapping[mapping[,'probesetID'] %in% colnames(x),] int <- intersect(rownames(Gsub), mapping[,"graphID"]) xn.m=xn.m[,mapping$probesetID]

index = intersect(mapping[,'probesetID'],colnames(xn.m))
x <- x[,index] colnames(xn.m) <- map2entrez[index] ex.sum = t(avereps(t(xn.m), ID=map2entrez[index]))

int= intersect(int, colnames(ex.sum)) ex.sum=ex.sum[,int] ## GEP which matched to PPI network Gsub=Gsub[int,int] ## PPI network which matched to GEP

2. Run FrSVM program ##################################################

You need install for flowing packages for run FrSVM.R programs:

library(ROCR)

library(Matrix)

library(kernlab)

If you want to running parallelly, you also need to load:

library(multicore)

Here is an expale for 5 times 10-folds Cross-Validtaion

source("../FrSVM.R") res <- frSVM.cv(x=ex.sum, y=y, folds=10,Gsub=Gsub, repeats=5, parallel = FALSE, cores = 2, DEBUG=TRUE,d=0.5,top.uper=0.95,top.lower=0.9)

the AUC values for 5*10-folds CV

AUC= res$auc