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## ---- eval=FALSE---------------------------------------------------------
# library(LEANR)
# library(ROCR)
# set.seed(123456)
# data(g2)
## ---- echo=FALSE,include=FALSE-------------------------------------------
library(LEANR)
library(ROCR)
set.seed(123456)
# load network and subnet results
instances<-LEANR:::instances
LEAN_results_sim<-LEANR:::LEAN_results_sim
## ---- eval=FALSE---------------------------------------------------------
# n_instances=10
#
# instances<-lapply (1:n_instances,function(i){
# subnet.simulation(g2, spec=sprintf('_STRING.fs900_rep%i',i), create.files=F)
# })
## ---- eval=FALSE---------------------------------------------------------
# LEAN_results_sim<-lapply(1:n_instances,function(i){
# pvals<-instances[[i]]$pvals[,'P.Value']
# names(pvals)<-rownames(instances[[i]]$pvals)
# run.lean.fromdata(pvals, g2, n_reps = 1000, ncores = 3)
# })
## ------------------------------------------------------------------------
# extract gene class labels for each of the simulation instances
gene.order<-rownames(instances[[1]]$pvals)
class_matrix<-t(foreach(i=1:length(instances),.combine=rbind) %do% {
tmp<-!grepl('BG',instances[[i]]$pvals$NodeType);
names(tmp)<-rownames(instances[[i]]$pvals);
tmp[gene.order]
})
# extract LEAN scores for each of the simulation instances
LEAN_matrix<-t(foreach(i=1:length(instances),.combine=rbind) %do% {
tmp<-LEAN_results_sim[[i]]$restab[,'pstar']
names(tmp)<-rownames(LEAN_results_sim[[i]]$restab);
tmp[gene.order]
})
# Extract single gene scores
SG_matrix<-t(foreach(i=1:length(instances),.combine=rbind) %do% {
tmp<-instances[[i]]$pvals[,'P.Value']
names(tmp)<-rownames(instances[[i]]$pvals);
tmp[gene.order]
})
# ROC evaluation
predictions<-list(LEAN=prediction(-LEAN_matrix,class_matrix),
SG=prediction(-SG_matrix,class_matrix))
performances<-lapply(predictions,function(pred){performance(pred,"tpr","fpr")})
names(performances)<-names(predictions)
aucs_ind<-lapply(predictions,function(pred){performance(pred,"auc")@y.values})
aucs<-lapply(aucs_ind,function(x)mean(unlist(x)))
names(aucs)<-names(predictions)
## ---- fig.height=6, fig.width=6------------------------------------------
# plot resulting ROC curves
plot(performances$LEAN,xlim=c(0,.2),col='black',avg='vertical',lwd=3,
spread.estimate='stderror',show.spread.at=seq(.02,.2,l=9),main='ROC performance on simulated subnetworks')
plot(performances$SG,col='orange',avg='vertical',lwd=3,add=T,
spread.estimate='stderror',show.spread.at=seq(.02,.2,l=9))
abline(a=0,b=1,lty=2,col='cyan')
legend('topleft',c(sprintf('LEAN (AUC=%0.3g)',aucs$LEAN),sprintf('Single-gene (AUC=%0.3g)',aucs$SG)),fill=c('black','orange'))
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