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RF_roc_fortest19_20200425.Rmd
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RF_roc_fortest19_20200425.Rmd
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```{r}
rm(list = ls())
library(readr)
library(plyr)
library(readxl)
library(stringr)
library(magrittr)
source("D:/datamining_library_ge20200306.R")
df <- read_xlsx("../../COVID19_123456.xlsx",sheet = 2)
df1 <- df[,grepl("Ratio:",names(df))] %>% as.data.frame()
row.names(df1) <- df$Accession
nm1 <- ge.split(names(df1),"\\)",1)
nm2 <- ge.split(nm1,"\\(",2)
names(df1) <- gsub(", ","_",nm2)
ge.na.ratio(df1)
ge.plot.density(df1)
max(df1,na.rm = T)
min(df1,na.rm = T)
df2 <- df1[apply(df1,1, function(x){sum(!is.na(x))})>0,]
ge.na.ratio(df2)
ge.plot.density(df2)
max(df2,na.rm = T)
min(df2,na.rm = T)
dele <- which(names(df2) %in% c("F2_128N","F1_129C","F2_133N","F5_131C","F4_133N"))
df2 <- df2[,-dele]
info <- read_xlsx("../../sampleinfo2.xlsx")
info$TMT <- gsub("^b","F",info$TMT)
df2 <- df2[,-na.omit(match(info$TMT[which(info$Type=="NA")],names(df2)))]
info2 <- read_xlsx("../sTable1_20200329-2100.xlsx",sheet = 2)
names(df2) <- info2$`Metabonomics ID`[match(names(df2),info2$`MS ID`)]
df2 <- df2[,!is.na(names(df2))]
df2 <- df2[,!grepl("\\/",names(df2))]
df.meta <- read_xls("../../metabonomics_ttest/Taizhou20200328.xls",col_names = T)
df1.meta <- df.meta[-c(1:2),-1] %>% as.data.frame()
row.names(df1.meta) <- df.meta$...1[-c(1:2)]
names(df1.meta) <- df.meta[1,-1]
df2.meta <- apply(df1.meta,1, as.numeric) %>% t() %>% as.data.frame() %>% log2()
names(df2.meta) <- names(df1.meta)
label2 <- gsub("\\d+","",names(df2.meta))
zx_type <- which(label2=="ZX")
pt_type <- which(label2=="PT")
jbdz_type <- which(label2=="jbdz")
jkbz_type <- which(label2=="jkdz")
df3.meta <- df2.meta[!apply(df2.meta, 1, function(x){sum(is.na(x[zx_type]))/length(zx_type) > 0.8 | sum(is.na(x[pt_type]))/length(pt_type) > 0.8| sum(is.na(x[jbdz_type]))/length(jbdz_type) > 0.8 | sum(is.na(x[jkbz_type]))/length(jkbz_type) > 0.8}),]
#df3.meta <- df2.meta
for (i in 1:nrow(df3.meta)) {
df3.meta[i,][is.na(df3.meta[i,])] <- min(df3.meta[i,],na.rm = T)
}
detect <- read_xlsx("../../metabonomics_ttest/Detected Drugs information from Calibra.xlsx",col_names = F)
df3.meta <- df3.meta[-which(row.names(df3.meta) %in% detect$...1),]
# write.csv(df3.meta,"meta_matrix_log2delectNA08anddrug36_NAmin20200329.csv")
ov <- intersect(names(df2),names(df3.meta))
proteo <- df2[,ov]
meta <- df3.meta[,ov]
df3 <- rbind(proteo,meta)
label <- gsub("\\d+","",names(df3))
# tmp <- df3
# names(tmp) <- info2$`Patient ID`[match(names(tmp),info2$`Metabonomics ID`)]
# write.xlsx(tmp,"mix_proteomic_metabonomics_RF_trainingset.xlsx")
```
validation
```{r}
va.prot <- read.csv("51peptide19_files_matrix.csv")
va.prot2 <- va.prot[-c(1:10),-1]
va.prot2[,2:ncol(va.prot2)] <- as.numeric(as.matrix(va.prot2[,2:ncol(va.prot2)]))
va.prot3 <- aggregate(va.prot2,by=list(Protein=va.prot2$Protein),FUN=mean,na.rm=TRUE)
va.prot4 <- va.prot3[,-c(1:2)]
row.names(va.prot4) <- as.character(ge.split(as.character(va.prot3$Protein),"\\|",2))
names(va.prot4) <- gsub("\\.","_",names(va.prot4))
va.meta <- read_xlsx("Metabolomics data for 7 biomarkers To Dr.Guo-2(1).xlsx")
va.meta2 <- va.meta[,-1]
row.names(va.meta2) <- va.meta$...1
names(va.meta2)[3] <- "bilirubin degradation product, C17H20N2O5 (1)"
names(va.meta2)[1] <- "taurochenodeoxycholic acid 3-sulfate"
names(va.meta2)[2] <- "thyroxine"
names(va.meta2)[5] <- "N,N-dimethyl-pro-pro"
names(va.meta2)[7] <- "glucuronate"
va.meta2[c(5,10),1] <- min(va.meta2[,1],na.rm = T)
row.names(va.meta2) <- gsub("-0","_",row.names(va.meta2))
row.names(va.meta2) <- gsub("-","_",row.names(va.meta2))
va.meta3 <- as.data.frame(log2(t(va.meta2)))
da.va <- rbind(va.prot4,va.meta3)
gene <- read_xlsx("uniprot_gene.xlsx")
da.va$gene <- gene$Gene[match(row.names(da.va),gene$yourlist)]
write.xlsx(da.va,"matrix_featrue29_sample19.xlsx")
group <- read_xlsx("validationcohort.xlsx",sheet=2)
group$`Patient ID1` <- gsub("-","_",group$`Patient ID1`)
label.va <- group$Group[match(names(da.va),group$`Patient ID1`)]
label.va[label.va==1] <- "ZX"
label.va[label.va==0] <- "PT"
```
ROC ZX/PT RF importance select
```{r}
df3 <- t(apply(df3, 1, scale)) %>% as.data.frame()
colnames(df3) <- ov
nmva <- names(da.va)
da.va <- t(apply(da.va, 1, scale)) %>% as.data.frame()
colnames(da.va) <- nmva
changename <- data.frame(real=row.names(df3),change=paste0("cg",1:nrow(df3)))
df4 <- df3
row.names(df4) <- changename$change
pt <- df4[,which(label=="PT")]
zx <- df4[,which(label=="ZX")]
prot.up <- read.csv("../../proteome_delect5/ZX_PT_up_volcano.csv")$X
prot.dw <- read.csv("../../proteome_delect5/ZX_PT_dw_volcano.csv")$X
meta.up <- read.csv("../../metabonomics_ttest/ZX_PT_up_volcano.csv")$X
meta.dw <- read.csv("../../metabonomics_ttest/ZX_PT_dw_volcano.csv")$X
prot <- c(as.character(prot.up),as.character(prot.dw),as.character(meta.up),as.character(meta.dw))
prot <- prot[!grepl("ose",prot)]
protcg <- changename$change[which(changename$real %in% prot )] %>% as.character()
p3 <- data.frame(pt,zx)
p3[is.na(p3)] <- 0
p4 <- data.frame(t(p3))
names(p4) <- row.names(df4)
tmp <- p4[,protcg]
tmp$label <- rep(c("PT","ZX"),c(length(pt),length(zx)))
set.seed(202122.3)
rf_train<-randomForest(as.factor(label)~.,data=tmp,importance=T,ntree=1000,nodesize=5)
result <- data.frame(importance(rf_train,type=1))
result1 <- row.names(result)[result$MeanDecreaseAccuracy>3]
#ge.plot.pca(t(tmp[,result1]),rep(c("PT","ZX"),c(length(pt),length(zx))),"PT_ZX_test")
# ge.plot.umap(t(tmp[,result1]),rep(c("PT","ZX"),c(length(pt),length(zx))),"PT_ZX_test")
# ge.plot.tsne(t(tmp[,result1]),rep(c("PT","ZX"),c(length(pt),length(zx))),"PT_ZX_test")
# prot.up <- read.csv("../proteome_delect5/ZX_PT_up_volcano.csv")
# prot.dw <- read.csv("../proteome_delect5/ZX_PT_dw_volcano.csv")
# meta.up <- read.csv("../metabonomics_ttest/ZX_PT_up_volcano.csv")
# meta.dw <- read.csv("../metabonomics_ttest/ZX_PT_dw_volcano.csv")
# prot.dif <- rbind(prot.up,prot.dw)
# prot.dif1 <- prot.dif[which(prot.dif$X %in% changename$real[match(result1,changename$change)]),c(1,87:89)]
#
# meta.dif <- rbind(meta.up,meta.dw)
# meta.dif1 <- meta.dif[which(meta.dif$X %in% changename$real[match(result1,changename$change)]),c(1,98:100)]
#
# resu.dif <- rbind(prot.dif1,meta.dif1)
# write.csv(resu.dif,"covid_29_fcpv.csv")
data.select <- data.frame(change=result1,real=changename$real[match(result1,changename$change)])
#write.csv(data.select,"PT_ZX_select.csv")
#
# pdf("RF_important_PT_ZX_select.pdf")
# varImpPlot(rf_train,n.var=min(length(result1), nrow(rf_train$importance)))
# dev.off()
#############################
tmp <- p4[,result1]
tmp$label <- rep(c("PT","ZX"),c(length(pt),length(zx)))
set.seed(203420.03)
tmpRF <- randomForest(factor(label) ~ . ,data=tmp,importance=T,ntree=1000,nodesize=5)
df.va2 <- da.va[match(as.character(data.select$real), row.names(da.va)), ]
row.names(df.va2) <-as.character( data.select$change)
df.va3 <- t(df.va2) %>% as.data.frame()
df.va4 <- df.va3
predicted <- predict(tmpRF,df.va4,type='prob')
predicts <- t(apply(predicted,1,function(v){v/sum(v)}))
colnames(predicts) <- colnames(predicted)
predicts <- data.frame(predicts,check.names=F)
predicts$predicted <- apply(predicts,1,function(v){names(v)[max(v)==v]})
predicts$observed <- label.va
ROC <- roc(predicts$observed, as.numeric(predicts$ZX))
sum(predicts$predicted==predicts$observed)
#############################
df6 <- da.va[,row.names(predicts)]
mean <- apply(df6,2,mean,na.rm=T)
data <- data.frame(mean,predicts=predicts$ZX,sample2=row.names(predicts),type2=predicts$observed )
ge.plot.point <- function(data,sample,value,type,group,title="",xlab="sample",ylab="value"){
a <- ggplot(data,aes(x=sample,y=value,group=group,color=group))+
geom_point()+geom_vline(xintercept = 0.5 ,linetype="dotted")+
ggtitle(paste0(title,"_pointplot"))+
xlab(xlab)+
ylab(ylab)+
xlim(0,1)+
theme(legend.text = element_text(size = 15,color = "black"),legend.position = 'top',
legend.title = element_text(size=15,color="black") ,
panel.grid.major =element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank(),
axis.line = element_line(colour = "black"))+
theme(panel.grid =element_blank())+
theme(axis.text = element_text(size = 10,color = "black"))+
theme(axis.text.x = element_text( hjust = 1,angle = 45))+
theme(plot.subtitle=element_text(size=30, hjust=0, color="black"))+
theme(axis.title.x=element_text(size=17, hjust=0.5, color="black"))+
theme(axis.title.y=element_text(size=17, hjust=0.5, color="black"))+ geom_text(aes(label=type,vjust = -0.8, hjust = 0.5),show.legend = FALSE)+
scale_color_manual(limits=c("PT","ZX"), values=c("#E29827","#922927"))
ggsave(paste0(title,"_pointplot.pdf"),plot=a,width=4,height=8)
}
ge.plot.point(data,data$predicts,data$mean,data$sample2,data$type2, "RF predict19","predict value","Average")
#########################
tmp <- p4[,result1]
tmp$label <- rep(c("PT","ZX"),c(length(pt),length(zx)))
sm <- c()
for (i in runif(1000,1,1000)) {
accu <- c()
set.seed(i)
folds <- createFolds(tmp$label,3)
for(fold in folds){
#fold=folds[[8]]
valids <- tmp[fold,]
trains <- tmp[setdiff(1:dim(tmp)[1],fold),]
trains$label <- as.factor(trains$label)
set.seed(2020.3)
tmpRF <- randomForest(label ~ . ,data=trains,importance=T,ntree=1000,nodesize=5)
predicted <- predict(tmpRF,valids,type='prob')
predicts <- t(apply(predicted,1,function(v){v/sum(v)}))
colnames(predicts) <- colnames(predicted)
predicts <- data.frame(predicts,check.names=F)
predicts$predicted <- apply(predicts,1,function(v){names(v)[max(v)==v]})
predicts$observed <- valids$label
acc <- sum(predicts$predicted==predicts$observed)
accu <- c(accu,acc/length(fold))
}
sm <- rbind(sm,c(i,accu))
}
set.seed(42.349741)
folds <- createFolds(tmp$label,3)
fold=folds[[3]]
valids <- tmp[fold,]
trains <- tmp[setdiff(1:dim(tmp)[1],fold),]
trains$label <- as.factor(trains$label)
set.seed(2020.3)
tmpRF <- randomForest(label ~ . ,data=trains,importance=T,ntree=1000,nodesize=5)
df.va2 <- da.va[match(as.character(data.select$real), row.names(da.va)), ]
row.names(df.va2) <-as.character( data.select$change)
df.va3 <- t(df.va2) %>% as.data.frame()
df.va4 <- df.va3
predicted <- predict(tmpRF,df.va4,type='prob')
predicts <- t(apply(predicted,1,function(v){v/sum(v)}))
colnames(predicts) <- colnames(predicted)
predicts <- data.frame(predicts,check.names=F)
predicts$predicted <- apply(predicts,1,function(v){names(v)[max(v)==v]})
predicts$observed <- label.va
ROC <- roc(predicts$observed, as.numeric(predicts$ZX))
sum(predicts$predicted==predicts$observed)
```
heatmap
```{r}
zx_ty <- which(label.va=="ZX")
pt_ty <- which(label.va=="PT")
gene <- read_xlsx("uniprot_gene.xlsx")
df11 <- da.va[,c(pt_ty,zx_ty)]
df11[is.na(df11)] <- 0
row.names(df11)[1:22] <- paste0(gene$Gene,"_",gene$yourlist)[match(row.names(df11)[1:22],gene$yourlist)]
ann_col <- data.frame(Type=rep(c("non-severe","severe"),c((length(pt_ty)),(length(zx_ty)))),row.names = names(df11))
ann_col$Type <- factor(ann_col$Type,levels =c("non-severe","severe"))
Type_color <- c("#E29827","#922927")
names(Type_color) <- c("non-severe","severe")
ann_colors <- list(Type=Type_color)
a <- pheatmap(df11, color = c(brewer.pal(11,"RdYlBu")[9:7],"azure1",brewer.pal(11,"RdYlBu")[4:1]), fontsize_col = 8,
annotation_col = ann_col,
annotation_colors = ann_colors,
#scale = "row",
cluster_rows = T, cluster_cols = F,show_rownames = T, show_colnames = T, na_col = "white",
filename = "heatmap_featrue29_sample19.pdf",main = "Heatmap",width=8,height=8)
```