SCATTome: FluidgmR

PredictEnsemble <-
function(train,test,ImportantGenes,GeneNumbers=7){

dtrain<-train[,-1];
dtest<-test[,-1];

testcells<-test[,1];

library(randomForest)
library(e1071)

trcol<-colnames(dtrain);
tecol<-colnames(dtest);

SelectedSet<-ImportantGenes[1:GeneNumbers];

trind<-c();
tsind<-c();

for(i in 1:length(SelectedSet)){

trind<-c(trind, which(trcol==SelectedSet[i]));
tsind<-c(tsind, which(tecol==SelectedSet[i]));

}

if(!(length(trind)==length(tsind))){

print("Test and Train set do not have same important genes");
return(0)

}

dtr<-dtrain[,c(trind,dim(dtrain)[2])];
dts<-dtest[,tsind];

rf<-randomForest(as.factor(Status)~., ntree=1000, data=dtr);
prf<-predict(rf, newdata=dts, type="prob");
prf1<-prf[,2];

sv<-svm(as.factor(Status)~., data=dtr, kernel="radial", type="C-classification", cross=10, probability=TRUE);
psv<-predict(sv, newdata=dts, probability=TRUE);

prob<-attr(psv,"probabilities");
prob1<-prob[,2];

sv1<-svm(as.factor(Status)~., data=dtr, kernel="sigmoid", type="C-classification", cross=10, probability=TRUE);
psv1<-predict(sv, newdata=dts, probability=TRUE);

prob<-attr(psv1,"probabilities");
prob2<-prob[,2];

ens.p=(prf1+prob1+prob2)/3;

Prob<-as.data.frame(cbind(ID=test[,1], Prob=ens.p));

write.csv(Prob, "Test_Probabilities.csv", quote=FALSE, row.names=FALSE);

pdf("Predicted_Probabilities_of_Single_Cells.pdf")
plot(seq(1,length(ens.p)),ens.p, type="n", ylim=c(0,1), main="Probability for Test Data");
#text(x=seq(1,length(ens.p),1),y=ens.p,col=ceiling(ens.p*3));
text(x=seq(1,length(ens.p),1),y=ens.p,labels=test$ID,col=ceiling(ens.p*3),cex=0.5);
plot(seq(1,length(ens.p)),prf1, type="n", ylim=c(0,1), main="Probability for Test Data (From Random Forest)");
#text(x=seq(1,length(ens.p),1),y=prf1,col=ceiling(ens.p*3));
text(x=seq(1,length(ens.p),1),y=prf1,labels=test$ID,col=ceiling(ens.p*3),cex=0.5);
plot(seq(1,length(ens.p)),prob1, type="n", ylim=c(0,1), main="Probability for Test Data (From SVM 1)");
#text(x=seq(1,length(ens.p),1),y=prob1,col=ceiling(ens.p*3));
text(x=seq(1,length(ens.p),1),y=prob1,labels=test$ID,col=ceiling(ens.p*3),cex=0.5);
plot(seq(1,length(ens.p)),prob2, type="n", ylim=c(0,1), main="Probability for Test Data (From SVM 2)");
#text(x=seq(1,length(ens.p),1),y=prob2,col=ceiling(ens.p*3));
text(x=seq(1,length(ens.p),1),y=prob2,labels=test$ID,col=ceiling(ens.p*3),cex=0.5);
dev.off();
return(0);
}