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BLUPHAT_For_Hypotheses_Test.R
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####################################################################
### BLUPHAT For Hypothesis ###
####################################################################
setwd('~/bigdata/LABDATA/BLUPHAT/')
###############################################################
source('script/BLUP_Functions.R')
source('script/Commercial_Panels.R')
### phenotype
phenoData <- readRDS('data/TCGA-PRAD/Clinical_TCGA_PRAD_With_PreopPSA_and_BCR.RDS')
filter <- which(is.na(phenoData$pfr_5yr))
phenoData <- phenoData[-filter,]
#### rfs
#phenoData$rfs <- phenoData$days_to_first_biochemical_recurrence
#phenoData$days_to_first_biochemical_recurrence <- ifelse(phenoData$recurrence_status==1, phenoData$days_to_first_biochemical_recurrence,
# phenoData$days_to_last_followup)
#phenoData$days_to_first_biochemical_recurrence
#yr <- 5
#keep <- which(phenoData$recurrence_status==1 | (phenoData$recurrence_status==0 & phenoData$days_to_first_biochemical_recurrence>=yr*365))
#length(keep)
#phenoData <- phenoData[keep,]
#phenoData$days_to_first_biochemical_recurrence<- ifelse(phenoData$days_to_first_biochemical_recurrence>=yr*365,
# 1, phenoData$days_to_first_biochemical_recurrence/yr/365)
rnaData <- readRDS('data/TCGA-PRAD/mRNA_Expression_LogCPM_Filter_Low_TCGA_PRAD.RDS')
mirData <- readRDS('data/TCGA-PRAD/miRNA_Expression_LogCPM_Filter_Low_TCGA_PRAD.RDS')
methyData <- readRDS('data/TCGA-PRAD/Methylation_Filter_NA_TCGA_PRAD.RDS')
samples <- Reduce(intersect, list(rownames(phenoData), colnames(rnaData), colnames(mirData), colnames(methyData)))
samples
phenoData <-phenoData[samples,]
#gene <- rnaExpr490F[,samples]
gene <- rnaData[,samples]
gene <- as.matrix(t(gene))
gene[1:5,1:5]
dim(gene)
dim(phenoData)
gene <- scale(gene)
gene[1:5,1:5]
### pfr_5yr
pheno <- as.matrix(phenoData$pfr_5yr, drop=FALSE)
#pheno <- as.matrix(phenoData$days_to_first_biochemical_recurrence, drop=FALSE)
y <- as.numeric(pheno)
#y <- sqrt(y)
y
sum(y != 1)
corr <- abs(apply(gene, 2, function(v) cor.test(v,y)$estimate))
corrVal <- apply(gene, 2, function(v) cor.test(v,y)$estimate)
o <- order(corr, decreasing=T)
corrDa <- data.frame(corrVAL=corrVal[o],corrABS=corr[o], rank=1:length(o))
View(corrDa)
write.table(corrDa, file='report/gene_phe_correlation.rfs10yr.txt', sep='\t', quote=F)
write.table(corrDa[oncotype,], file='report/gene_phe_correlation.rfs10yr.oncotype.txt', sep='\t', quote=F)
write.table(corrDa[decipher,], file='report/gene_phe_correlation.rfs10yr.decipher.txt', sep='\t', quote=F)
write.table(corrDa[prolaris,], file='report/gene_phe_correlation.rfs10yr.prolaris.txt', sep='\t', quote=F)
panels <- list(oncotype, decipher, prolaris)
panels
i <- 0
h12 <- c()
while(i<10) {
print (i)
i = i + 1
selected <- sample(rownames(rnaData), 12)
geno<-gene[,selected]
#geno<-gene[te,selected]
kk<-kinship(gen=geno)
#write.csv(x=kk[[1]],file="yan\\input\\kk1.csv",row.names=FALSE)
#write.csv(x=kk[[2]],file="yan\\input\\cc1.csv",row.names=FALSE)
kk <- kk[[1]]
kk<-kk[,-c(1,2)]
kk<-as.matrix(kk)
result1 <- blup.hat(mydata=y, mykin=kk)
hat <- result1$predic.HAT
h12 <- c(h12, hat)
}
i <- 0
h18 <- c()
while(i<10) {
print (i)
i = i + 1
selected <- sample(rownames(rnaData), 18)
geno<-gene[,selected]
#geno<-gene[te,selected]
kk<-kinship(gen=geno)
#write.csv(x=kk[[1]],file="yan\\input\\kk1.csv",row.names=FALSE)
#write.csv(x=kk[[2]],file="yan\\input\\cc1.csv",row.names=FALSE)
kk <- kk[[1]]
kk<-kk[,-c(1,2)]
kk<-as.matrix(kk)
result1 <- blup.hat(mydata=y, mykin=kk)
hat <- result1$predic.HAT
h18 <- c(h18, hat)
}
h18
i <- 0
h31 <- c()
while(i<10) {
print (i)
i = i + 1
selected <- sample(rownames(rnaData), 31)
geno<-gene[,selected]
#geno<-gene[te,selected]
kk<-kinship(gen=geno)
#write.csv(x=kk[[1]],file="yan\\input\\kk1.csv",row.names=FALSE)
#write.csv(x=kk[[2]],file="yan\\input\\cc1.csv",row.names=FALSE)
kk <- kk[[1]]
kk<-kk[,-c(1,2)]
kk<-as.matrix(kk)
result1 <- blup.hat(mydata=y, mykin=kk)
hat <- result1$predic.HAT
h31 <- c(h31, hat)
}
h <- c(mean(h12),mean(h18),mean(h31))
h
for (i in 1:3) {
selected <- panels[[i]]
#####################################################################################
geno<-gene[,selected]
#print (selected)
#geno<-gene[te,selected]
kk<-kinship(gen=geno)
#write.csv(x=kk[[1]],file="yan\\input\\kk1.csv",row.names=FALSE)
#write.csv(x=kk[[2]],file="yan\\input\\cc1.csv",row.names=FALSE)
kk <- kk[[1]]
kk<-kk[,-c(1,2)]
kk<-as.matrix(kk)
result1 <- blup.hat(mydata=y, mykin=kk)
hat <- result1$predic.HAT
h <- c(h,hat)
#blup<-cv.mixed(x=x,y=pheno,kk=kk,nfold=nfold,foldid=foldid)
#blup[[2]]
##sum(blup[[2]]$yobs<0.6 & blup[[2]]$yhat>=0.6)
#br2<- as.numeric(blup[[1]])
#h2 <- c(br2, h2)
}
h
nGene <- length(o)
length(o)
for (i in seq(2,nGene,1)) {
print ('====================================')
print (i)
selected <- o[1:i]
#####################################################################################
geno<-gene[,selected]
#geno<-gene[te,selected]
kk<-kinship(gen=geno)
#write.csv(x=kk[[1]],file="yan\\input\\kk1.csv",row.names=FALSE)
#write.csv(x=kk[[2]],file="yan\\input\\cc1.csv",row.names=FALSE)
kk <- kk[[1]]
kk<-kk[,-c(1,2)]
kk<-as.matrix(kk)
result1 <- blup.hat(mydata=y, mykin=kk)
hat <- result1$predic.HAT
h <- c(h,hat)
}
#nGene <- i -1
#nGene
p <- data.frame(x=c('rand12','rand18','rand31','oncotype','decipher','prolaris', seq(2,nGene,1)), y=h)
write.table(p, file='report/HAT_Nomogram/BLUPHAT_PFR5YR_Step1.txt', sep='\t', quote=F, row.names = FALSE)
#write.table(p, file='Results/hat.rfs10yr.step1.mir.txt', sep='\t', quote=F, row.names = FALSE)
tr <- p[-c(1:6),]
tr$x <- as.numeric(tr$x)
tr$y <- as.numeric(tr$y)
points <- tr[tr$x %in% c(12,18,31),]
points
points$Panel=factor(c('Oncotype','Decipher','Prolaris'),
levels=c('Oncotype','Decipher','Prolaris'))
#points$Panel=factor(rep(c('Top12','Top18','Top31'), 6),
# levels=c('Top12','Top18','Top31'))
rand <- p[1:3,]
rand$y <- as.numeric(rand$y)
rand$Panel=factor(c('Oncotype','Decipher','Prolaris'),
levels=c('Oncotype','Decipher','Prolaris'))
#rand$Panel=factor(rep(c('Rand12','Rand18','Rand31'), 6),
# levels=c('Rand12','Rand18','Rand31'))
com <- p[4:6,]
com$y <- as.numeric(com$y)
com$Panel=factor(c('Oncotype','Decipher','Prolaris'),
levels=c('Oncotype','Decipher','Prolaris'))
ggplot() + geom_line(data=tr, aes(x=x, y=y), size=0.8) +
#facet_wrap(~trait, ncol=3) +
geom_hline(data=rand, aes(yintercept = y, color=Panel),linetype=1, size=1) +
geom_hline(data=com, aes(yintercept = y, color=Panel),linetype=2, size=1) +
xlab('Number of genes') + ylab('Predictability') + ylim(0,0.4) +
geom_point(data=points, aes(x=x, y=y, color=Panel), size=5, shape=20) +
theme_bw() + theme(axis.line = element_line(colour = "black"),
axis.title.y = element_text(size=16),
axis.title.x = element_text(size=16),
axis.text.x = element_text(size=12, color='black'), #, angle=45, hjust = 1.2, vjust=0.8
axis.text.y = element_text(size=12, color='black'),
#axis.ticks.x = element_blank(),
legend.title = element_text(size=0),
legend.text = element_text(size=14),
strip.text = element_text(size=16, color='black'),
#strip.background = element_rect(fill="white", color='black'),
strip.background = element_blank(),
panel.background = element_blank(),
panel.border = element_rect(color='black')) #strip.background = element_blank()
topn <- as.numeric(p[p$y==max(p$y),]$x)
topn
################ miRNAs
############### Top mRNA + Top miRNA