We use a simple filter feature selection algorithm, called FrSVM, which selected the top ranked genes in PPI network and then training these top raked genes in L2-SVM. FrSVM integrates protein-protein interaction (ppi) network information into feature/gene selection algorithm for prognostic biomarker discovery.
As L2-SVM could not do feature the the ranking of genes were used as feature selection step. Central genes always plays an important role biological process, so make using GeneRank to selected those genes with large differences in their expression.
We applied FrSVM to several cancer datasets and reveals a significantly better prediction performance and higher signature stability. Related manuscript already put to arXiv and R code for FrSVM available at:
Codes: https://sites.google.com/site/yupengcun/software/frsvm
Papers: http://arxiv.org/abs/1212.3214
. Any comments and question on the FrSVM are welcomed. The following is how to run the program:
1. Geting gene expression profiles (GEP), PPi Network.
##############################################
# 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”)
##############################################
# 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)##############################################
# 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
计算基因组学研究组 