the code related to the construction of the neural network model
install.packages("neuralnet")
install.packages("NeuralNetTools")
library(neuralnet)
library(NeuralNetTools)
set.seed(12345678)
inputFile="geneScore.txt"   
setwd("C:\\biowolf\\neuralDiagnostic\\15.neuralNet")     
data=read.table(inputFile, header=T, sep="\t", check.names=F, row.names=1)
data=as.data.frame(t(data))
group=gsub("(.*)\\_(.*)", "\\2", row.names(data))
data$con=ifelse(group=="con", 1, 0)
data$treat=ifelse(group=="treat", 1, 0)
fit=neuralnet(con+treat~., data, hidden=5)
fit$result.matrix
fit$weight
plot(fit)
pdf(file="neuralnet.pdf", width=9, height=7)
plotnet(fit)
dev.off()
net.predict=compute(fit, data)$net.result
net.prediction=c("con", "treat")[apply(net.predict, 1, which.max)]
predict.table=table(group, net.prediction)
predict.table
conAccuracy=predict.table[1,1]/(predict.table[1,1]+predict.table[1,2])
treatAccuracy=predict.table[2,2]/(predict.table[2,1]+predict.table[2,2])
paste0("Con accuracy: ", sprintf("%.3f", conAccuracy))
paste0("Treat accuracy: ", sprintf("%.3f", treatAccuracy))
colnames(net.predict)=c("con", "treat")
outTab=rbind(id=colnames(net.predict), net.predict)
write.table(outTab, file="neural.predict.txt", sep="\t", quote=F, col.names=F)
#install.packages("pROC")
library(pROC)                   
inputFile="neural.predict.txt" 
setwd("C:\\biowolf\\neuralDiagnostic\\16.ROC") 
rt=read.table(inputFile, header=T, sep="\t", check.names=F, row.names=1)
y=gsub("(.*)\\_(.*)", "\\2", row.names(rt))
y=ifelse(y=="con", 0, 1)
roc1=roc(y, as.numeric(rt[,2]))
ci1=ci.auc(roc1, method="bootstrap")
ciVec=as.numeric(ci1)
pdf(file="ROC.pdf", width=5, height=5)
plot(roc1, print.auc=TRUE, col="red", legacy.axes=T, main="Train group")
text(0.39, 0.43, paste0("95% CI: ",sprintf("%.03f",ciVec[1]),"-",sprintf("%.03f",ciVec[3])), col="red")
dev.off()
install.packages("neuralnet")
install.packages("NeuralNetTools")
library(neuralnet)
library(NeuralNetTools)
set.seed(12345678)
trainFile="geneScore.txt"         
testFile="testGeneScore.txt"    
setwd("C:\\biowolf\\neuralDiagnostic\\18.testNeuralPre")  
data=read.table(trainFile, header=T, sep="\t", check.names=F, row.names=1)
data=as.data.frame(t(data))
group=gsub("(.*)\\_(.*)", "\\2", row.names(data))
data$con=ifelse(group=="con", 1, 0)
data$treat=ifelse(group=="treat", 1, 0)
fit=neuralnet(con+treat~., data, hidden = 5)
data2=read.table(testFile, header=T, sep="\t", check.names=F, row.names=1)
data2=t(data2)
group2=gsub("(.*)\\_(.*)", "\\2", row.names(data2))
sameGene=intersect(colnames(data), colnames(data2))
data2=data2[,sameGene]
net.predict=compute(fit, data2)$net.result
net.prediction = c("con", "treat")[apply(net.predict, 1, which.max)]
predict.table = table(group2, net.prediction)
predict.table
conAccuracy=predict.table[1,1]/(predict.table[1,1]+predict.table[1,2])
treatAccuracy=predict.table[2,2]/(predict.table[2,1]+predict.table[2,2])
paste0("Con accuracy: ", sprintf("%.3f", conAccuracy))
paste0("Treat accuracy: ", sprintf("%.3f", treatAccuracy))
colnames(net.predict)=c("con", "treat")
outTab=rbind(id=colnames(net.predict), net.predict)
write.table(outTab, file="test.neuralPredict.txt", sep="\t", quote=F, col.names=F)
install.packages("pROC")
library(pROC)      
inputFile="test.neuralPredict.txt"     
setwd("C:\\biowolf\\neuralDiagnostic\\19.testROC")   
rt=read.table(inputFile, header=T, sep="\t", check.names=F, row.names=1)
y=gsub("(.*)\\_(.*)", "\\2", row.names(rt))
y=ifelse(y=="con", 0, 1)
roc1=roc(y, as.numeric(rt[,2]))
ci1=ci.auc(roc1, method="bootstrap")
ciVec=as.numeric(ci1)
pdf(file="ROC.pdf", width=5, height=5)
plot(roc1, print.auc=TRUE, col="red", legacy.axes=T, main="Test group")
text(0.39, 0.43, paste0("95% CI: ",sprintf("%.03f",ciVec[1]),"-",sprintf("%.03f",ciVec[3])), col="red")
dev.off()