2025 -生物信息学- GEO - 神经网络分析-ANN

GEO - 神经网络分析-ANN

01. 准备文件

在这里插入图片描述

id	GSM3587381_con	GSM3587382_con	GSM3587383_con	GSM3587384_con	GSM3587385_con	GSM3587386_con	GSM3587387_con	GSM3587388_con	GSM3587389_con	GSM3587390_con	GSM3587391_con	GSM3587392_con	GSM3587393_con	GSM3587394_con	GSM3587395_con	GSM3587396_con	GSM3587397_treat	GSM3587398_treat	GSM3587399_treat	GSM3587400_treat	GSM3587401_treat	GSM3587402_treat	GSM3587403_treat	GSM3587404_treat	GSM3587405_treat	GSM3587406_treat	GSM3587407_treat	GSM3587408_treat	GSM3587409_treat	GSM3587410_treat	GSM3587411_treat	GSM3587412_treat
AQP4	0	0	0	0	0	1	0	0	0	1	0	0	0	0	0	0	1	1	0	1	1	1	1	1	1	1	1	0	1	1	1	1
AEBP1	0	0	0	0	0	1	0	0	1	0	0	0	0	0	0	0	1	1	0	1	1	1	1	0	1	1	1	1	1	1	1	1
PRRX1	0	0	0	0	0	1	0	0	0	0	0	0	1	1	0	0	1	1	0	1	1	0	1	1	1	1	1	1	1	1	0	1
IGF1	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	1	1	1	1	1	1	1	1	1	1	1	1	1	1	0	1
GAS1	0	1	0	0	0	0	0	0	0	0	1	0	0	0	0	0	1	1	1	1	1	1	1	1	0	1	1	1	1	1	0	1
COL1A1	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	1	1	1	1	1	1	1	1	1	1	0	1	1	1	1	1
COL3A1	0	0	0	0	0	1	0	0	0	0	0	0	0	1	0	0	1	1	0	1	1	1	1	1	1	1	0	1	1	1	1	1
HTRA1	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	1	1	1	1	1	1	1	1	1	1	0	1	1	1	1	1
LRRC15	0	0	0	0	0	0	0	0	0	1	1	0	0	1	0	0	1	1	1	1	1	1	1	0	0	1	1	0	1	1	1	1
HSPG2	0	0	0	0	1	1	0	0	0	0	0	0	0	0	1	0	1	0	1	1	1	1	1	0	0	1	1	1	1	1	1	1

#install.packages("neuralnet")
#install.packages("NeuralNetTools")#引用包
library(neuralnet)
library(NeuralNetTools)
set.seed(12345678)inputFile="geneScore.txt"       #输入文件
setwd("/Users/wangyang/Desktop/BCBM/03geneScore")     #设置工作目录#读取输入文件
data=read.table(inputFile, header=T, sep="\t", check.names=F, row.names=1)
data=as.data.frame(t(data))#获取样品分组信息
group=gsub("(.*)\\_(.*)", "\\2", row.names(data))
data$con=ifelse(group=="con", 1, 0)
data$treat=ifelse(group=="treat", 1, 0)#神经网络模型
fit=neuralnet(con+treat~., data, hidden=15)
fit$result.matrix
fit$weight
#plot(fit)pdf(file="neuralnet.pdf", width=15, height=20)
plotnet(fit)
dev.off()#利用模型预测结果
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))#输出预测结果
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)