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MCD_fun.r
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MCD_fun.r
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library("keras")
library("GenomicRanges")
library("rtracklayer")
library("data.table")
library("openxlsx")
library("pheatmap")
library("RColorBrewer")
library("grid")
library('ggplotify')
library("cowplot")
library("pROC")
library("ggplot2")
library("ggrepel")
library("plyr")
library("dplyr")
format_sample_table=function(file, main.class="Disease", sub.class="IDH1", read.col="Filter_reads", sort=F){
if(is.data.frame(file)){
sample.df=file
}else{
sample.df=read.xlsx(file)
}
sample.df$Label=paste(sample.df[, main.class],sep="_")
sample.df$Label=factor(sample.df$Label, levels=sort(unique(sample.df$Label)))
sample.df$Detail_Label=paste(sample.df[, main.class],
ifelse(is.na(sample.df[, sub.class]),"",paste("_",sub.class, sample.df[,sub.class],sep="")),
sep="")
sample.df$Detail_Label=factor(sample.df$Detail_Label, levels=sort(unique(sample.df$Detail_Label)))
sample.df$ReadsNum=get_read_number(sample.df[, read.col])
if(sort){
sample.df=sample.df[order(sample.df$Detail_Label, -sample.df$ReadsNum),]
}
return(sample.df)
}
get_read_number=function(mystr){
mystr=gsub(",","",mystr)
myvalue=sub("^(\\d+).*","\\1",mystr)
return(as.numeric(myvalue))
}
get_read_percent=function(mystr){
mystr=gsub(",","",mystr)
mypercent=sub(".*\\((.+)\\%\\).*","\\1",mystr)
return(as.numeric(mypercent)/100)
}
get_file_list=function(info.list, bw.norm.dir, bw.bias.dir, bias.suffix="_partition.bw"){
data.list=list()
for(n in 1:length(info.list)){
dens.bw=paste(bw.norm.dir,"/",info.list[[n]]$Sample,"/",info.list[[n]]$Sample,".bw",sep="")
names(dens.bw)=info.list[[n]]$Sample
bias.bw=paste(bw.bias.dir,"/",info.list[[n]]$Sample, bias.suffix,sep="")
names(bias.bw)=info.list[[n]]$Sample
input.label=info.list[[n]]$Label
names(input.label)=info.list[[n]]$Sample
input.name=sub("^([^_]+)_(.+)$","\\1",info.list[[n]]$Sample)
input.name=paste(input.name,"_",info.list[[n]]$PatientID,"_",round(info.list[[n]]$ReadsNum/1e6),"M",sep="")
names(input.name)=info.list[[n]]$Sample
if("Detail_Label" %in% colnames(info.list[[n]])){
detail.label=info.list[[n]]$Detail_Label
names(detail.label)=info.list[[n]]$Sample
data.list[[n]]=list(Dens=dens.bw, Bias=bias.bw, Label=input.label, Detail_Label=detail.label, Name=input.name)
}else{
data.list[[n]]=list(Dens=dens.bw, Bias=bias.bw, Label=input.label, Name=input.name)
}
}
names(data.list)=names(info.list)
return(data.list)
}
sample_pred=function(my.sample, input.label=NULL, pred.level,
one.model.dir, out.dir=NULL,mid.dir=NULL,
value.type="abs", transform=F, N_model=10, model.name="GLMNET_model", N_thread=12, force=F, rm.ref=T, hp.color=NA){
result.list=list()
for(j in 1:length(model.name)){
cat("Predict by", model.name[j],"\n")
if(!is.null(out.dir)){
pred.result.dir=paste(out.dir,"/",model.name[j],"_test",sep="")
if(!dir.exists(pred.result.dir)){dir.create(pred.result.dir, recursive = T)}
}else{
pred.result.dir=NULL
}
model.list=read_my_model(model.dir=one.model.dir, model.name=model.name[j], N_model=N_model)
pred.list=list()
##simplify
for (i in 1:length(model.list)){
cat("Predict by",model.name[j],":",i,"model\n")
if(rm.ref==T){
model.input.file=paste(one.model.dir,"/Model_Input/","model",i,"_input.txt",sep="")
model.input=read.table(model.input.file, header = T, sep="\t", quote="\"",stringsAsFactors = F)
model.sample=colnames(model.input)
keep.ind=!names(my.sample) %in% model.sample
}else{
keep.ind=1:length(my.sample)
}
keep.label=input.label[keep.ind]
rand.bw=my.sample[keep.ind]
rand.bed=paste(one.model.dir,"/feature/","rand",i,".bed",sep="")
if(!is.null(out.dir)){
pred.mat.file=paste(out.dir,"/Prediction/","prediction",i,"_input.txt",sep="")
if(!dir.exists(dirname(pred.mat.file))){dir.create(dirname(pred.mat.file), recursive = T)}
}else{
pred.mat.file=NULL
}
pred.mat=get_bw_mat(bw.file=rand.bw, region.file=rand.bed,
mat.file=pred.mat.file, mid.dir=mid.dir,
N_threads=N_thread, force=force)
if(value.type=="abs"){
pred.mat=abs(pred.mat)
}
train.input.file=paste(one.model.dir,"/Model_Input/","model",i,"_input.txt",sep="")
train.mat=read.table(train.input.file, header = T, sep="\t", quote="\"",stringsAsFactors = F)
if(value.type=="abs"){
train.mat=abs(train.mat)
}
train.avg=apply(train.mat, 1, mean, na.rm=T)
if(transform==T){
pred.final.mat=(pred.mat - train.avg) / apply(train.mat, 1, sd, na.rm=T)
pred.final.mat[is.na(pred.final.mat)]=0
}else{
pred.final.mat=pred.mat
pred.final.mat[is.na(pred.final.mat)]=0
}
if(!is.null(out.dir)){
cat("plot signal heatmap...\n")
if(is.null(input.label[1])|is.na(input.label[1])){
col.anno=NA
}else{
col.anno=data.frame(Label=input.label)
rownames(col.anno)=names(my.sample)
}
pred.zmat=(pred.mat - train.avg) / apply(train.mat, 1, sd, na.rm=T)
pred.hp.file=sub("\\.txt$",".pdf", pred.mat.file)
pred.zhp.file=sub("\\.txt$","_zmat.pdf", pred.mat.file)
hplot1=draw_heatmap(list(pred_mat=pred.final.mat), graph.file=pred.hp.file,
smooth=NA, my.color=hp.color, ncolor=100,
fontsize=10,
sort=F,show_rownum=30,show_colnum=1,width=7,height=7,
annotation_col=col.anno,
cluster_rows=F,cluster_cols=F)
hplot2=draw_heatmap(list(zmat=pred.zmat), graph.file=pred.zhp.file,
smooth=NA, my.color=hp.color, ncolor=100,
zMin = -2,zMax=2,
fontsize=10,
sort=F,show_rownum=30,show_colnum=1,width=7,height=7,
annotation_col=col.anno,
cluster_rows=F,cluster_cols=F)
}
cat("Predict by",i, model.name[j],"\n")
one.pred=my_model_pred(list(model.list[[i]]), mod.name=paste(model.name[j],i,sep="_"), input.mat=t(pred.final.mat), pred.level=pred.level, input.label=keep.label)
pred.list[[i]]=one.pred[[1]]
names(pred.list)[i]=names(model.list)[i]
if(!is.null(pred.result.dir)){
model.result.file=paste(pred.result.dir,"/", model.name[j],"_",i,"_result.txt",sep="")
write.table(pred.list[[i]], model.result.file,quote=F,sep="\t",row.names = F,col.names = T)
}
}
out.avg=pred_average(pred.list = pred.list)
if(!is.null(pred.result.dir)){
avg.result.file=paste(pred.result.dir,"/", model.name[j],"_avg","_result.txt",sep="")
write.table(out.avg, avg.result.file,quote=F,sep="\t",row.names = F,col.names = T)
}
result.list[[j]]=pred.list
names(result.list)[j]=model.name[j]
}
return(result.list)
}
read_my_model=function(model.dir, model.name="GLMNET_model",N_model=1){
model.type.dir=paste(model.dir,"/",model.name,sep="")
if(!is.null(N_model)){
model.file=paste(model.type.dir,"/",model.name,"_",1:N_model,".rds",sep="")
model.list=lapply(model.file, readRDS)
names(model.list)=paste(model.name,"_",1:N_model,sep="")
}else{
model.file=list.files(path=model.type.dir, pattern=paste("^",model.name,".*\\.rds$",sep=""),full.names = T)
rand.id=sub(paste("^",model.name,"_(\\d+).*",sep=""), "\\1",basename(model.file))
cancer.id=sapply(1:length(model.file), function(x){
sub.match=regexpr(paste("(?<=",model.name,"_",rand.id[x],"_)","\\w+","(?=\\.rds$)",sep=""),
basename(model.file)[x],
perl=T)
sub.model=ifelse(sub.match==-1,NA,regmatches(basename(model.file)[x], sub.match))
})
if(length(unique(cancer.id))<2){
model.list=lapply(model.file, readRDS)
}else{
rand.type=unique(rand.id)
cancer.type=unique(cancer.id)
cat("Read",length(rand.type),"of",length(cancer.type), "submodel:",cancer.type,"\n")
model.list=list()
for(i in 1:length(rand.type)){
sub.model.file=model.file[grepl(paste("^",model.name,"_",rand.type[i],sep=""), basename(model.file))]
sub.model.list=lapply(sub.model.file, readRDS)
names(sub.model.list)=sub("\\..*","",basename(sub.model.file))
model.list[[i]]=sub.model.list
names(model.list)[i]=paste(model.name,"_",rand.type[i],sep="")
}
}
}
return(model.list)
}
get_bw_mat=function(bw.file, region.file, mat.file=NULL, mid.dir, N_threads=12, force=F, verbose=F){
if(!is.null(mat.file) & !force){
if(file.exists(mat.file)){
cal.flag=T
}else{
cal.flag=T
}
}else if(is.null(mat.file) & !force){
cal.flag=T
}else{
cal.flag=T
}
if(cal.flag==F){
}else{
cat("Cal input...\n")
if(!dir.exists(mid.dir)){dir.create(mid.dir, recursive = T)}
score.files=paste(mid.dir,"/",names(bw.file),"_score.txt",sep="")
names(score.files)=names(bw.file)
stepi=round(length(bw.file)/10)
pb=txtProgressBar(min=0, max=10)
for(k in 1:length(bw.file)){
if(verbose){
cat(k, bw.file[k],"...\n")
}else{
setTxtProgressBar(pb, round(k/stepi))
}
}
close(pb)
score.mat=build_bias_mat(score.files)
score.mat=score.mat[,-c(1,2)]
my.gr=import.bed(region.file)
rownames(score.mat)=my.gr$name
if(!is.null(mat.file)){
if(!dir.exists(dirname(mat.file))){dir.create(dirname(mat.file), recursive = T)}
write.table(score.mat, file=mat.file, quote=F, sep="\t", row.names = T, col.names = T)
}
}
return(score.mat)
}
build_bias_mat=function(peak.bias.file){
bias.mat=c()
for(i in 1:length(peak.bias.file)){
window.df=as.data.frame(fread(peak.bias.file[i], header = T, sep="\t", quote = "\"", stringsAsFactors = F))
if(i==1){
bias.mat=data.frame(region=paste(window.df[,1],":",window.df[,2],"-",window.df[,3],sep=""),
name=window.df$name,
score=window.df$score)
}else{
bias.mat=cbind(bias.mat, window.df$score)
}
colnames(bias.mat)[i+2]=names(peak.bias.file)[i]
}
return(bias.mat)
}
draw_heatmap=function(mat.file, graph.file,
zMin=NA, zMax=NA,zRange=c(0,1),smooth=NA,
my.color=NA, ncolor=100,
sort=FALSE,show_rownum=1,show_colnum=1,width=NA,height=NA,append=F,
hrow.min=100,hrow.max=1000,combine=F,...){
obj.list=c()
sub.label=c()
sub.height=c()
for(i in 1:length(mat.file)){
cat(i,"loading data of ")
if(is.matrix(mat.file[[i]])){
plot.mat=mat.file[[i]]
plot.name=names(mat.file)[[i]]
}else if(is.data.frame(mat.file[[i]])){
plot.mat=mat.file[[i]]
plot.name=names(mat.file)[[i]]
}else if(is.vector(mat.file[[i]]) & length(mat.file[[i]])==1){
if(file.exists(mat.file[[i]])){
if(grepl("\\.xlsx$", mat.file[[i]])){
plot.mat=read.xlsx(mat.file[[i]],colNames = T, rowNames = T)
plot.name=sub("(\\.mat)*\\.xlsx$", "", basename(mat.file[[i]]))
}else{
plot.mat=read.table(mat.file[[i]], header = T, sep="\t", quote = "\"", stringsAsFactors = F,check.names = F)
plot.name=sub("\\..*$", "", basename(mat.file[[i]]))
}
}else{
stop(paste(i,mat.file[[i]],"not exists!\n"))
}
}else{
stop(paste(i,names(mat.file)[i],"is not a matrix!\n"))
}
cat(plot.name,"\n")
remove.ind=which(apply(plot.mat, 1, function(x){all(is.nan(x))}))
if(length(remove.ind)>0){
cat(paste(rownames(plot.mat)[remove.ind], collapse = ","), "removed because of NaN\n")
plot.mat=plot.mat[-c(remove.ind), ]
}
if(is.na(zMin) | is.na(zMax)){
vmin=max(min(unlist(plot.mat),na.rm=T), mean(unlist(plot.mat),na.rm=T)-2*sd(unlist(plot.mat),na.rm=T))
vmax=min(max(unlist(plot.mat),na.rm=T), mean(unlist(plot.mat),na.rm=T)+2*sd(unlist(plot.mat),na.rm=T))
my.range=c(vmin,vmax)
}
if(is.na(zMin)){zMin=my.range[1]}
if(is.na(zMax)){zMax=my.range[2]}
if(!is.na(smooth)){ plot.mat=apply(plot.mat, 1, function(x){rollapply(x, width=2*smooth, function(y){mean(y,na.rm=T)})})}
if(sort==T){
cat("sorting the matrix...\n")
w1=apply(abs(plot.mat), 1, function(x){mean(x,na.rm=T)})
w2=apply(abs(plot.mat), 1, function(x){sd(x,na.rm = T)})
w2[w2==0]=NA
w2[is.na(w2)]=min(w2,na.rm = T)
w=w1
plot.mat=plot.mat[order(w, decreasing = T),]
}
plot.mat[plot.mat > zMax]=zMax
plot.mat[plot.mat < zMin]=zMin
if(is.na(my.color[1])){
my.color=colorRampPalette(rev(brewer.pal(n = 7, name ="RdYlBu")))(ncolor)
}else{
if(all(grepl("^#",my.color))){
}else{
my.color=colorRampPalette(my.color)(ncolor)
}
}
if((show_rownum >=0)&(show_rownum <= 1)){
row.num=nrow(plot.mat)*show_rownum
}else{
row.num=show_rownum
}
if(row.num >1){
cut=ceiling(nrow(plot.mat) / row.num)
show.ind=seq(cut,nrow(plot.mat),by=cut)
tmp.name=rownames(plot.mat)[show.ind]
plot.mat=as.matrix(plot.mat)
rownames(plot.mat)=rep("",nrow(plot.mat))
rownames(plot.mat)[show.ind]=tmp.name
}else if(row.num==0){
plot.mat=as.matrix(plot.mat)
rownames(plot.mat)=rep("",nrow(plot.mat))
}
if((show_colnum >=0)&(show_colnum <= 1)){
col.num=ncol(plot.mat)*show_colnum
}else{
col.num=show_colnum
}
if(col.num >1){
cut=ceiling(ncol(plot.mat)/ col.num)
show.ind=seq(cut,ncol(plot.mat),by=cut)
tmp.name=colnames(plot.mat)[show.ind]
plot.mat=as.matrix(plot.mat)
colnames(plot.mat)=rep("",ncol(plot.mat))
colnames(plot.mat)[show.ind]=tmp.name
}else if(col.num==0){
colnames(plot.mat)=rep("",ncol(plot.mat))
}
my.break=seq(zMin, zMax, length.out = ncolor+1)
if(nrow(plot.mat) <= hrow.min){
p.height=2
}else if(nrow(plot.mat) >= hrow.max){
p.height=height
}else{
p.height=2+round((height-2) * (nrow(plot.mat)-hrow.min)/(hrow.max-hrow.min), 1)
}
sub.height[i]=p.height
sub.label[i]=paste(nrow(plot.mat),"rows",sep=" ")
p.obj=pheatmap(plot.mat,
color=my.color,
breaks=my.break,
border_color = "NA",
main =paste(plot.name,"\n",nrow(plot.mat),"rows", sep=" "),
width=width,height=p.height
,...
)
obj.list[[i]]=p.obj
names(obj.list)[i]=plot.name
}
gg.list=lapply(obj.list, as.ggplot)
names(gg.list)=names(obj.list)
if(combine){
merge.plot=plot_grid(plotlist=gg.list, align = "v",ncol=1,
rel_heights = sub.height
)
}
if(grepl("\\.pdf",basename(graph.file))){
if(!dir.exists(dirname(graph.file))){dir.create(dirname(graph.file),recursive = T)}
if(!combine){
pdf(graph.file,width=width,height=height,onefile = T,bg="white")
lapply(gg.list,function(x){
grid.draw(x)
})
dev.off()
}else{
ggsave(graph.file,plot=merge.plot,width=width,height = height)
}
}else{
if(!dir.exists(graph.file)){dir.create(graph.file,recursive = T)}
if(!combine){
for(i in 1:length(gg.list)){
jpeg(filename =file.path(graph.file,paste(names(gg.list)[i],".jpg",sep="")),width=width,height=height,units="in",res=300)
grid.draw(gg.list[[i]])
dev.off()
}
}else{
jpeg(filename=paste(graph.file,".jpg",sep=""),width=width,height=height,units="in",res=300)
grid.newpage()
grid.draw(merge.plot)
dev.off()
}
}
if(!combine){
result=list(gg.list)
}else{
result=merge.plot
}
return(result)
}
my_model_pred=function(mod.list, mod.name, input.mat,
pred.level=NULL,
input.label=NULL){
if(length(mod.name) != length(mod.list)){stop("model list should be same length as model names!\n")}
pred.list=list()
for(i in 1:length(mod.list)){
my.pred=predict(mod.list[[i]], input.mat,type="prob")
my.pred$prediction=predict(mod.list[[i]],input.mat)
if(!is.null(input.label)){
my.pred$Label=input.label[rownames(my.pred)]
}
my.pred=cbind(data.frame(Sample=rownames(input.mat)),my.pred)
pred.list[[i]]=my.pred
}
names(pred.list)=mod.name
return(pred.list)
}
pred_average=function(pred.list){
all.pred.df=Reduce(rbind, pred.list)
label.level=colnames(all.pred.df)[!colnames(all.pred.df) %in% c("Sample","prediction","Label")]
pred.unique.list=list()
for(x in label.level){
avg.value=tapply(all.pred.df[,x], all.pred.df[,1], mean)
avg.df=data.frame(names(avg.value), avg.value)
colnames(avg.df)=c("Sample", x)
pred.unique.list[[x]]=avg.df
}
pred.unique.df=Reduce(merge, pred.unique.list)
pred.ind=apply(pred.unique.df[,-1,drop=F], 1, which.max)
pred.unique.df[,"prediction"]=label.level[pred.ind]
if(any(grepl("^Label$",colnames(all.pred.df)))){
sample.label=unique(all.pred.df[,c("Sample","Label")])
pred.unique.df=merge(pred.unique.df, sample.label)
}
return(pred.unique.df)
}
evaluate_pred=function(result.list, input.label=NA, input.name=NA, normal.cut=NA, out.dir, my.color=c("red","pink","orange","blue"), verbose=F){
if(is.na(input.name)[1]){
input.name=rownames(result.list[[1]][[1]])
}
if(!is.na(input.label[1])){
info.df=data.frame(Sample=names(input.label), Label=input.label, Name=input.name)
}else{
info.df=data.frame(Sample=result.list[[1]][[1]]$Sample, Label=input.label, Name=input.name)
}
prob.mat=c()
pred.col=colnames(result.list[[1]][[1]])
pred.level=pred.col[!pred.col %in% c("Sample","prediction","Label") ]
for(n in 1:length(result.list)){
one.prob.mat=problist_to_mat(df.list=result.list[[n]], pred.level=pred.level,prefix=names(result.list)[n])
prob.mat=rbind(prob.mat, one.prob.mat)
}
mm.name=paste(sub("_.*","", names(result.list)),collapse = "_")
prob.hp.file=paste(out.dir,"/",mm.name,"_","result_prob_mat.pdf",sep="")
if(!is.na(input.label[1])){
col.anno=data.frame(Label=input.label)
rownames(col.anno)=names(input.label)
}else{
col.anno=NA
}
hp.plot=draw_heatmap(list(prob_mat=prob.mat), graph.file=prob.hp.file,
smooth=NA, my.color=NA, ncolor=100,
fontsize=10,
sort=F,show_rownum=30,show_colnum=1,width=7,height=7,
annotation_col=col.anno,
cluster_rows=F,cluster_cols=F)
for(j in 1:length(result.list)){
pred.result.dir=paste(out.dir,"/", names(result.list)[j],"_test",sep="")
if(!dir.exists(pred.result.dir)){dir.create(pred.result.dir, recursive = T)}
pred.list=result.list[[j]]
for(i in 1:length(pred.list)){
pred.list[[i]]=merge(pred.list[[i]], info.df[,c("Sample","Label")], sort=F)
my.pred=pred.list[[i]]
model.graph.file=paste(pred.result.dir,"/", names(result.list)[j],"_",i,"_prediction.pptx",sep="")
model.result.file=paste(pred.result.dir,"/", names(result.list)[j],"_",i,"_result.txt",sep="")
if(verbose){
save_prediction_results(my.pred=my.pred,
normal.cut=normal.cut,
info.df=info.df,
title=paste(names(result.list)[j],nrow(my.pred),"Sample"), font.size = 10,
model.graph.file=model.graph.file, model.result.file=model.result.file, model.confusion.file=model.confusion.file
,my.color=my.color
)
}
}
cat("AUROC of", names(result.list)[j],":",length(pred.list),"models\n")
avg.graph.file=paste(pred.result.dir,"/", names(result.list)[j],"_avg","_prediction.pdf",sep="")
avg.result.file=paste(pred.result.dir,"/", names(result.list)[j],"_avg","_result.txt",sep="")
if(!is.na(input.label[1])){
auc.list=lapply(pred.list, pred_AUC)
all.auc=Reduce(c, auc.list)
all.auc.df=data.frame(Class=names(all.auc), AUROC=all.auc, color="blue")
box.plot=draw_boxplot_style(all.auc.df,yMin=0,yMax=1,y.breaks=0.2,
title=paste(model.name[j],"test sample"),
xlab="Class", ylab="AUROC",
font.size=12,legend.ncol=3,
mycolor="blue",dot=T,text.x.angle=90)
box.graph=paste(pred.result.dir,"/","pred_boxplot.pdf",sep="")
pdf(box.graph, width=3,height=5,onefile = T,bg="white")
lapply(list(box.plot),function(x){
grid.draw(x)
})
dev.off()
}
cat("Save average prediction of",names(result.list)[j],"...\n")
pred.avg=pred_average(pred.list = pred.list)
save_prediction_results(my.pred=pred.avg,
normal.cut=normal.cut,
info.df=info.df,
title=paste(names(result.list)[j],nrow(pred.avg),"Sample"), font.size = 10,
model.graph.file=avg.graph.file,
model.result.file=avg.result.file,
append=T,my.color=my.color
)
}
}
problist_to_mat=function(df.list, pred.level, prefix="Dens"){
all.pred=c()
all.long=c()
for(m in 1:length(df.list)){
one.df=df.list[[m]]
keep.id=grep("Sample|Label",colnames(one.df), value=T)
one.long=melt(one.df, id.vars=keep.id, measure.vars=pred.level, variable.name="Cancer_type", value.name = "Prob")
one.long$source=paste(prefix,"_",one.long$Cancer_type,"_",m,sep="")
all.long=rbind(all.long, one.long)
if("Label" %in% colnames(all.long)){
}else{
}
}
all.pred=dcast(all.long, source~Sample, value.var="Prob")
rownames(all.pred)=all.pred$source
all.pred=all.pred[match(unique(all.long$source), rownames(all.pred)), unique(all.long$Sample)]
return(all.pred)
}
combine_problist=function(result1.list, result2.list, prefix1="Dens", prefix2="Bias",train.level){
prob.mat1=c()
for(n in 1:length(result1.list)){
one.prob.mat=problist_to_mat(result1.list[[n]], train.level, prefix=paste(prefix1,"_",names(result1.list)[n],sep=""))
prob.mat1=rbind(prob.mat1, one.prob.mat)
}
prob.mat2=c()
for(n in 1:length(result2.list)){
one.prob.mat=problist_to_mat(result2.list[[n]], train.level, prefix=paste(prefix2,"_",names(result2.list)[n],sep=""))
prob.mat2=rbind(prob.mat2, one.prob.mat)
}
all.prob.mat=rbind(prob.mat1, prob.mat2)
return(all.prob.mat)
}
save_prediction_results=function(my.pred, test.pred=my.pred, normal.cut=NA,
info.df, gr="group", sort.by="ReadsNum",
title
,model.graph.file, model.result.file
, model.confusion.file=NA
,font.size=12,legend.ncol=3, my.color=c("red","pink","orange","blue")
,xlab="plasma samples", ylab="Probability"
,append=F){
if(!dir.exists(dirname(model.graph.file))){dir.create(dirname(model.graph.file), recursive = T)}
if(!dir.exists(dirname(model.result.file))){dir.create(dirname(model.result.file), recursive = T)}
my.pred=merge(info.df[,c("Sample","Label")],my.pred, sort=F)
test.pred=my.pred
write.table(my.pred, model.result.file,quote=F,sep="\t",row.names = F,col.names = T)
if(!is.na(normal.cut)){
cut.pred=my.pred
cut.pred$prediction=NA
normal.label=grep("normal",colnames(cut.pred),value=T)
cut.pred[cut.pred[,normal.label]>normal.cut,"prediction"]=normal.label
cancer.pred=cut.pred[cut.pred[,normal.label] <= normal.cut, ]
cancer.mat=cancer.pred[,!colnames(cancer.pred) %in% c("Sample","prediction","Label", normal.label)]
cancer.pred$prediction=colnames(cancer.mat)[apply(cancer.mat,1,which.max)]
cut.pred=df_update(old.df=cut.pred, map.df=cancer.pred, key.col="Sample", info.col="prediction")
write.table(cut.pred,
sub("\\.txt",paste("_normal.cut",normal.cut,".txt",sep=""),model.result.file),quote=F,sep="\t",row.names = F,col.names = T)
}
bar.list=draw_prediction_bar(my.pred=my.pred,
info.df=info.df,
gr=gr, sort.by=sort.by
,font.size=font.size,legend.ncol=legend.ncol,barcolor=my.color,
xlab=xlab, ylab=ylab
)
pdf(model.graph.file,onefile = T,bg="white")
lapply(bar.list,function(x){
grid.draw(x)
})
dev.off()
if(nrow(test.pred)>0 & length(unique(test.pred$Label))>1){
if(!is.na(normal.cut)){
normal.pred=cut.pred
normal.pred$Label=as.character(normal.pred$Label)
normal.pred$Label[normal.pred$Label!=normal.label]="Cancer"
normal.pred$prediction[normal.pred$prediction!=normal.label]="Cancer"
normal.tb=as.data.frame.matrix(table(real=normal.pred$Label, pred=normal.pred$prediction))
colnames(normal.tb)=paste("pred_",colnames(normal.tb),sep="")
rownames(normal.tb)=paste("real_",rownames(normal.tb),sep="")
cancer.pred=cut.pred
cancer.pred=cancer.pred[cancer.pred$Label!=normal.label,]
cancer.tb=as.data.frame.matrix(table(cancer.pred$Label, cancer.pred$prediction))
colnames(cancer.tb)=paste("pred_",colnames(cancer.tb),sep="")
rownames(cancer.tb)=paste("real_",rownames(cancer.tb),sep="")
}
pROC.plot=draw_pROC(my.pred=test.pred, model.title=title, my.color = my.color)
auc.graph.file=sub("\\.pdf$", "_AUC.pdf",model.graph.file)
pdf(auc.graph.file, onefile = T,bg="white")
lapply(list(pROC.plot),function(x){
grid.draw(x)
})
dev.off()
my.pred.long=melt(test.pred, id.vars=c("Sample","Label","prediction"),
variable.name = "Prediction_type",
value.name = "Probability")
my.pred.long$Label=paste("real_",my.pred.long$Label, sep="")
my.pred.long$Prediction_type=paste(my.pred.long$Prediction_type, "_prob",sep="")
my.pred.long$color=my.pred.long$Prediction_type
bar.group.plot=draw_color_bar_style(my.pred.long[,c("Label","Probability","color","Prediction_type")],
group="Prediction_type",
interval=T, yMin=0,yMax=1,y.dashed=NULL,
xlab="", ylab="Average Probability",
title=paste(nrow(test.pred), "Sample Average"),
font.size=font.size, legend.ncol=legend.ncol,
barcolor=my.color, flip=F,hide.x=F,
text.x.angle=90, x.fac=T)+
facet_grid(Prediction_type~Label, scales = "free",space="free")+
theme(strip.text = element_text(size = 10, face="bold"))
avg.bar.file=paste(dirname(model.graph.file),"/avg_bar.pdf",sep="")
#sub("\\.pdf$", "_avg_bar.pdf",model.graph.file)
pdf(avg.bar.file, width=5, height=7, onefile = T,bg="white")
lapply(list(bar.group.plot),function(x){
grid.draw(x)
})
dev.off()
test.group.file=paste(dirname(model.result.file),"/avg_group.txt",sep="")
#sub("\\.txt$","_vali_group.txt", model.result.file)
test.pred.list=split(test.pred[, !colnames(test.pred) %in% c("Sample","Label","prediction")], test.pred$Label)
test.pred.group=c()
for(i in 1:length(test.pred.list)){
avg.group=apply(test.pred.list[[i]], 2, mean)
test.pred.group=rbind(test.pred.group, avg.group)
}
colnames(test.pred.group)=paste(colnames(test.pred.list[[1]]),"_prob",sep="")
rownames(test.pred.group)=paste("real_",names(test.pred.list), sep="")
write.table(as.data.frame(t(test.pred.group)), test.group.file, quote=F,sep="\t",row.names = T,col.names = T)
}
}
pred_AUC=function(my.pred){
if(nrow(my.pred)<1){return(c())}
if(!"Label" %in% colnames(my.pred)){stop("Label is not found!")}
my.pred$Label=make.names(my.pred$Label)
my.label=unique(make.names(my.pred$Label))
pred.auc=c()
if(length(unique(my.pred$Label))>1){
for(n in 1:length(my.label)){
my.respone=ifelse(my.pred$Label==my.label[n],1,0)
one.roc=roc(my.respone,my.pred[,my.label[n]], direction = "<", quiet=T)
pred.auc[n]=round(as.numeric(one.roc$auc),2)
names(pred.auc)[n]=my.label[n]
}
}
return(pred.auc)
}
draw_boxplot_style=function(data.df,yMin=NA,yMax=NA,y.breaks=NA,
y.dashed=NULL,xlab="x", ylab="y",title="boxplot",subtitle="",
font.size=12,legend.ncol=NA,mycolor=NA,dot=T,text.x.angle=0, flip=F){
if(is.na(mycolor[1])){mycolor=c("black", "#0073C2FF","#EFC000FF","red","#009E73","orange","purple", "grey")}
if(is.na(yMin)){
ymin=range(as.numeric(data.df[,2]),na.rm=T)[1]
}else{
ymin=yMin
}
if(is.na(yMax)){
ymax=range(as.numeric(data.df[,2]),na.rm=T)[2]
}else{
ymax=yMax
}
data.df[,3]=factor(data.df[,3], levels =sort(unique(data.df[,3])), ordered = T)
box.plot=ggplot(data.df,aes(x = data.df[,1],
y=as.numeric(data.df[,2])
))+
geom_boxplot(aes(color=data.df[,3]),
position = position_dodge(0.8),outlier.shape=NA)
if(dot){
box.plot=box.plot+geom_dotplot(aes(fill=data.df[,3]),position = position_dodge(0.8)
,binaxis = "y"
,dotsize=0.4
,stackdir = "center"
)
}
box.plot=box.plot+scale_color_manual(name="group",breaks=levels(data.df[,3]),values=mycolor)+
scale_fill_manual(name="group", breaks=levels(data.df[,3]), values=mycolor)+
theme_bw()+
theme(
axis.text=element_text(size=font.size, face="bold", colour = "black"),
axis.title = element_text(size=font.size+4,face="bold",colour="black"),
axis.title.x=element_text(size=font.size,margin=ggplot2::margin(t=0,r=0,b=0,l=0)),
axis.title.y=element_text(size=font.size),
axis.ticks = element_line(size = 1, colour = "black"),
axis.text.y=element_text(hjust=1,margin=ggplot2::margin(t=0,r=5,b=0,l=0)),
axis.text.x = element_text(angle=text.x.angle,vjust = 0.5,hjust=0.8),
panel.grid=element_blank(), panel.background = element_blank(),
legend.position = "top",
legend.text = element_text(size = font.size,face="bold"),
plot.title = element_text(hjust = 0.5),
plot.subtitle = element_text(hjust = 0.5)
)+
geom_hline(yintercept = y.dashed,linetype="dashed",color="black", size=1)+
labs(title=title,x=xlab, y=ylab,subtitle = subtitle)
if(!is.na(yMin)|!is.na(yMax)){
box.plot=box.plot+scale_y_continuous(limits = c(ymin,ymax))
}
if(!is.na(legend.ncol)){
box.plot=box.plot+guides(color=guide_legend(ncol=legend.ncol))
}
if(!is.na(y.breaks)){
box.plot=box.plot+scale_y_continuous(limits = c(ymin,ymax),breaks = seq(ymin*10e4,ymax*10e4,y.breaks*10e4)/10e4)
}
if(flip){
box.plot=box.plot+coord_flip()
}
return(box.plot)
}
df_update=function(old.df, map.df, key.col, info.col){
if(any(!key.col %in% colnames(old.df))){
stop("key not found in old dataframe:",key.col,"!\n")
}
if(any(!key.col %in% colnames(map.df))){
stop("key not found in map dataframe:",key.col,"!\n")
}
new.df=old.df
for(i in 1:length(info.col)){
if(info.col[i] %in% colnames(old.df)){
cat("Column",info.col[i],"will be updated\n")
}else{
new.df[, info.col[i]]=NA
cat("Add column:",info.col[i],"\n")
}
}
up.num=0
up.df=c()
for(i in 1:nrow(old.df)){
##each row
match.id=match(old.df[i, key.col], map.df[,key.col])
if(!is.na(match.id)){
##each info column
for(j in 1:length(info.col)){
new.df[i, info.col[j]]=paste(map.df[match.id, info.col[j]], collapse = ";")
}
up.df=rbind(up.df, new.df[i,])
up.num=up.num+1
}
}
cat("Update",up.num,"rows\n")
return(new.df)
}
draw_prediction_bar=function(my.pred, info.df, gr="group",sort.by="ReadsNum",
font.size=12,legend.ncol=3,barcolor=c("red","pink","orange","blue"),
xlab="plasma samples", ylab="Probability"){
if("PatientID" %in% colnames(info.df) ){
info.df$Name=sub("^([^_]+)_(.+)$","\\1",info.df$Sample)
info.df$Name=paste(info.df$Name, info.df[,"PatientID"], sep="_")
}
if("Condition" %in% colnames(info.df) ){
info.df$Name=paste(info.df$Name, sub("(raw|clean).*","\\1", info.df[,"Condition"]), sep="_")
}
if("Input_ssDNA(ng)" %in% colnames(info.df) ){
info.df$Name=paste(info.df$Name, paste(info.df[,"Input_ssDNA(ng)"],"ng",sep=""), sep="_")
}
if("SS(ng/ul)" %in% colnames(info.df) ){
info.df$Name=paste(info.df$Name, paste(info.df[,"SS(ng/ul)"],"/ul",sep=""), sep="_")
}
if("Rate" %in% colnames(info.df) ){
info.df$Name=paste(info.df$Name, paste(round(info.df[,"Rate"]*100),"%",sep=""), sep="_")
}
if("ReadsNum" %in% colnames(info.df) ){
info.df$Name=paste(info.df$Name, paste(round(info.df[,"ReadsNum"]/1e6),"M",sep=""), sep="_")
}
if("library_type" %in% colnames(info.df)){
info.df$Name=paste(info.df$Name, info.df[,"library_type"], sep="_")
}
my.pred.info=merge(info.df,my.pred, sort=F)
if(all(sort.by %in% colnames(my.pred.info)) ){
sort.ind=do.call(order, my.pred.info[,c("Label",sort.by)])
my.pred.info=my.pred.info[sort.ind, ]
}else{
}
my.pred.long=melt(my.pred.info, id.vars=unique(c(colnames(info.df), "Label","prediction","Name")),
variable.name = "Prediction_type",
value.name = "Probability")
label.type=sort(unique(my.pred.long$Label))
if(length(label.type)==0){
label.type="unknown"
my.pred.long$Label="unknown"
}
bar.list=lapply(1:length(label.type), function(n){
sub.pred=my.pred.long[my.pred.long$Label==label.type[n], ]
bar.plot=draw_bar_percent(sub.pred[,c("Name","Probability","Prediction_type")],y.dashed=NULL,
xlab=xlab, ylab=ylab,
title=paste(as.character(label.type[n]), length(unique(sub.pred$Sample)), "Sample"),
font.size=font.size,legend.ncol=legend.ncol,
barcolor=barcolor,
flip=F,text.x.angle=90)+
theme(strip.text = element_text(size = 12, face="bold"))
})
return(bar.list)
}
draw_bar_percent=function(data.df,y.dashed=NULL,yMin=NA,yMax=NA,
xlab="x",ylab="y",title="percentage",subtitle="",y.breaks=NA,
font.size=12,legend.ncol=NA,barcolor=NA,flip=T,text.x.angle=90){
if(is.na(barcolor[1])){barcolor=c("black", "#0073C2FF","#EFC000FF","red","#009E73","yellow","purple", "grey")}
ymin=yMin
ymax=yMax
data.df[,3]=factor(data.df[,3], levels =sort(unique(data.df[,3])), ordered = T)
barplot=ggplot(data.df,aes(x = factor(data.df[,1], levels =unique(data.df[,1]), ordered = T),
y=as.numeric(data.df[,2]),fill=data.df[,3]))+
geom_bar(stat="identity",position = "fill")+
scale_fill_manual(name="group", breaks=levels(data.df[,3]), values=barcolor)+
theme_bw()+
theme(
axis.text=element_text(size=font.size, face="bold", colour = "black"),
axis.title = element_text(size=font.size+4,face="bold",colour="black"),
axis.title.x=element_text(size=font.size,margin=ggplot2::margin(t=0,r=0,b=0,l=0)),
axis.title.y=element_text(size=font.size),
axis.ticks = element_line(size = 1, colour = "black"),
axis.ticks.x = element_blank(),
axis.text.x = element_text(angle=text.x.angle, vjust = 1,hjust=1),
axis.text.y=element_text(hjust=1,margin=ggplot2::margin(t=0,r=5,b=0,l=0)),
panel.grid=element_blank(), panel.background = element_blank(), #remove grid
axis.line.x = element_blank(),
axis.line.y=element_line(colour="grey"),
legend.position = "top",
legend.text = element_text(size = font.size,face="bold"),
plot.title = element_text(hjust = 0.5),
plot.subtitle = element_text(hjust = 0.5)
)+
scale_y_continuous(limits = c(ymin,ymax),expand=expansion(mult=c(0,0.1)))+
labs(x=xlab, y=ylab,title=title,subtitle=subtitle)
if(is.numeric(y.dashed)){
barplot=barplot+geom_hline(yintercept = y.dashed,linetype="dashed",color="black", size=1)
}
if(!is.na(legend.ncol)){
barplot=barplot+guides(fill=guide_legend(ncol=legend.ncol))
}