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PRS_DNN_earlystopping_dropout.R
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#try to use DNN to compute PRS
#fisrt hidden layer dropout 0.2-0.9, the rest 0.5
#load installed tensorflow
#ml TensorFlow/2.1.0-foss-2019b-Python-3.7.4
#install---
#install.packages("keras")
#install.packages("tensorflow")
#install_tensorflow(method = "conda", envname = "Tensorflow_for_R")
#conda_install(envname = "Tensorflow_for_R", packages="numpy")
#load tensorflow---
#startup::restart()
library(keras)
library(tensorflow)
reticulate::use_condaenv("Tensorflow_for_R")
reticulate::py_config()
reticulate::py_run_string('import tensorflow')
tf$constant("Hellow Tensorflow") #to see if it works
#reticulate::conda_list()
#use_condaenv("Tensorflow_for_R") #this work
#use_virtualenv("/fh/fast/dai_j/BEACON/BEACON_GRANT/code/tensorflowenv")
#locate installed tensorflow https://tensorflow.rstudio.com/installation/custom/
#Sys.setenv(RETICULATE_PYTHON="/app/software/Python/3.7.4-GCCcore-8.3.0/bin/python") #this worked
#Sys.getenv("RETICULATE_PYTHON")
tf$constant("Hellow Tensorflow")
#NN example https://tensorflow.rstudio.com/tutorials/beginners/
mnist <- dataset_mnist()
mnist$train$x <- mnist$train$x/255
mnist$test$x <- mnist$test$x/255
model <- keras_model_sequential() %>%
layer_flatten(input_shape = c(28, 28)) %>%
layer_dense(units = 128, activation = "relu") %>%
layer_dropout(0.2) %>%
layer_dense(10, activation = "softmax")
summary(model)
model %>%
compile(
loss = "sparse_categorical_crossentropy",
optimizer = "adam",
metrics = "accuracy"
)
model %>%
fit(
x = mnist$train$x, y = mnist$train$y,
epochs = 5,
validation_split = 0.3,
verbose = 2
)
predictions <- predict(model, mnist$test$x)
head(predictions, 2)
model %>%
evaluate(mnist$test$x, mnist$test$y, verbose = 0)
save_model_tf(object = model, filepath = "model")
reloaded_model <- load_model_tf("model")
all.equal(predict(model, mnist$test$x), predict(reloaded_model, mnist$test$x))
#prepare data-----------------------------------
#split samples into training and testing----
sampletable=readxl::read_excel("/fh/fast/dai_j/BEACON/BEACON_GRANT/data/PLINKinputCombo_bca_07Feb2018.xls",1)
#sampletable=xlsx::read.xlsx("/fh/fast/dai_j/BEACON/BEACON_GRANT/data/PLINKinputCombo_bca_07Feb2018.xls",1)
#for EA
casesamples=sampletable$localid[sampletable$phenoEA_bca==2]
contsamples=sampletable$localid[sampletable$phenoEA_bca==1]
#for BEEA
casesamples=sampletable$localid[sampletable$phenoEABE_bca==2]
contsamples=sampletable$localid[sampletable$phenoEABE_bca==1]
set.seed(1000)
idx=sample(length(casesamples),round(length(casesamples)*0.8))
traincasesamples=casesamples[idx]
idx=sample(length(contsamples),round(length(contsamples)*0.8))
traincontsamples=contsamples[idx]
testcasesamples=casesamples[!casesamples %in% traincasesamples]
testcontsamples=contsamples[!contsamples %in% traincontsamples]
trainsamples=c(traincasesamples,traincontsamples)
testsamples=c(testcasesamples,testcontsamples)
idx=match(trainsamples,sampletable$localid)
tmp=sampletable[idx,c(1,2)]
#PRSDNN_train_plinksamples.txt and PRSDNN_test_plinksamples.txt: EA
#write.table(tmp,file="../result/PRSDNN_train_plinksamples.txt",col.names = F,row.names = F,sep="\t",quote = F)
write.table(tmp,file="../result/PRSDNNBEEA_train_plinksamples.txt",col.names = F,row.names = F,sep="\t",quote = F)
idx=match(testsamples,sampletable$localid)
# tmp=sampletable[idx,c(1,2)]
# write.table(tmp,file="../result/PRSDNN_test_plinksamples.txt",col.names = F,row.names = F,sep="\t",quote = F)
# tmp=sampletable[idx,c(1,2)]
write.table(tmp,file="../result/PRSDNNBEEA_test_plinksamples.txt",col.names = F,row.names = F,sep="\t",quote = F)
#generate genotype files in bash
plink=/fh/fast/stanford_j/Xiaoyu_Oct2020/Tools/plink-1.07-x86_64/plink1.9/plink
prefix="/fh/fast/dai_j/BEACON/BEACON_GRANT/result/bca_filtered_30Nov2018_flip_noambiguous"
#EA
$plink --bfile $prefix --keep ../result/PRSDNN_train_plinksamples.txt \
--recode A-transpose --out ../result/PRSDNN_BCAtrain
$plink --bfile $prefix --keep ../result/PRSDNN_train_plinksamples.txt \
--make-bed --out ../result/PRSDNN_BCAtrain
prefix="/fh/fast/dai_j/BEACON/BEACON_GRANT/result/bca_filtered_30Nov2018_flip_noambiguous"
$plink --bfile $prefix --keep ../result/PRSDNN_test_plinksamples.txt \
--recode A-transpose --out ../result/PRSDNN_BCAtest
$plink --bfile $prefix --keep ../result/PRSDNN_test_plinksamples.txt \
--make-bed --out ../result/PRSDNN_BCAtest
#BEEA
prefix="/fh/fast/dai_j/BEACON/BEACON_GRANT/result/bca_filtered_30Nov2018_flip_noambiguous"
$plink --bfile $prefix --keep ../result/PRSDNNBEEA_train_plinksamples.txt \
--recode A-transpose --out ../result/PRSDNNBEEA_BCAtrain
$plink --bfile $prefix --keep ../result/PRSDNNBEEA_train_plinksamples.txt \
--make-bed --out ../result/PRSDNNBEEA_BCAtrain
prefix="/fh/fast/dai_j/BEACON/BEACON_GRANT/result/bca_filtered_30Nov2018_flip_noambiguous"
$plink --bfile $prefix --keep ../result/PRSDNNBEEA_test_plinksamples.txt \
--recode A-transpose --out ../result/PRSDNNBEEA_BCAtest
$plink --bfile $prefix --keep ../result/PRSDNNBEEA_test_plinksamples.txt \
--make-bed --out ../result/PRSDNNBEEA_BCAtest
#use EA phenotype
updatefam=function(famfile="../result/PRSDNN_BCAtrain.fam",opt="EA")
{
fam=read.table(famfile)
idx=match(fam$V2,sampletable$localid)
if (opt=="EA")
{
fam$V6=sampletable$phenoEA_bca[idx]
}
if (opt=="BEEA")
{
fam$V6=sampletable$phenoEABE_bca[idx]
}
if (opt=="BE")
{
fam$V6=sampletable$phenoBE_bca[idx]
}
write.table(fam,file=famfile,row.names = F,col.names = F,sep="\t",quote=F)
}
#EA
updatefam()
updatefam(famfile="../result/PRSDNN_BCAtest.fam")
#BEEA
updatefam(famfile="../result/PRSDNNBEEA_BCAtrain.fam",opt="BEEA")
updatefam(famfile="../result/PRSDNNBEEA_BCAtest.fam",opt="BEEA")
covariateforGWAS=function(famfile="../result/PRSDNN_BCAtrain.fam",prefix="../result/PRSDNN_BCAtrain")
{
fam=read.table(famfile)
idx=match(fam$V2,sampletable$localid)
tmp=data.frame(FID=sampletable$fam[idx],
IID=sampletable$localid[idx],
pc1=sampletable$ev1_bca[idx],
pc2=sampletable$ev2_bca[idx],
pc3=sampletable$ev3_bca[idx],
pc4=sampletable$ev4_bca[idx],
age=sampletable$age[idx],
sex=sampletable$sex[idx])
#for validate_twas, ind ID changed
write.table(tmp,file=paste0(prefix,".covariate"),row.names = F,col.names = T,sep="\t",quote=F)
}
#EA
covariateforGWAS()
covariateforGWAS(famfile="../result/PRSDNN_BCAtest.fam",prefix="../result/PRSDNN_BCAtest")
#BEEA
covariateforGWAS(famfile="../result/PRSDNNBEEA_BCAtrain.fam",prefix="../result/PRSDNNBEEA_BCAtrain")
covariateforGWAS(famfile="../result/PRSDNNBEEA_BCAtest.fam",prefix="../result/PRSDNNBEEA_BCAtest")
#run gwas in bash, consider covariate
#EA
prefix=/fh/fast/dai_j/BEACON/BEACON_GRANT/result/PRSDNN_BCAtrain
$plink --bfile $prefix --covar $prefix.covariate -covar-name pc1,pc2,pc3,pc4,sex,age --logistic --hide-covar --ci 0.95 --out $prefix
#BEEA
prefix=/fh/fast/dai_j/BEACON/BEACON_GRANT/result/PRSDNNBEEA_BCAtrain
$plink --bfile $prefix --covar $prefix.covariate -covar-name pc1,pc2,pc3,pc4,sex,age --logistic --hide-covar --ci 0.95 --out $prefix
#read gwas result
#EA
gwas=read.table("../result/PRSDNN_BCAtrain.assoc.logistic",header = T)
quantile(gwas$P,na.rm=T)
# 0% 25% 50% 75% 100%
# 1.243e-06 2.457e-01 4.962e-01 7.483e-01 1.000e+00
sum(gwas$P<0.05,na.rm=T)
gwas=read.table("../result/PRSDNNBEEA_BCAtrain.assoc.logistic",header = T)
quantile(gwas$P,na.rm=T)
# 0% 25% 50% 75% 100%
# 1.509e-07 2.399e-01 4.906e-01 7.440e-01 1.000e+00
pcutoff=0.001
pcutoff=0.01
pcutoff=0.05
pcutoff=0.1
pcutoff=1 #would require 2xT memory, not implementable
selectsnps=gwas$SNP[which(gwas$P<pcutoff)] #EA:x,9211,45384,831785;BEEA:x,10003,46404
#read genotype
tolocalid=function(samplenames)
{
samplenames=strsplit(samplenames,"_")
samplenames=sapply(1:length(samplenames),function(x){
tmp=samplenames[[x]]
paste0(tmp[2:length(tmp)],collapse = "_")
})
}
readgenotype=function(prefix="../result/PRSDNN_BCAtrain")
{
tmp=data.table::fread(paste0(prefix,".traw"))
rownames(tmp)=tmp$SNP
idx=match(selectsnps,rownames(tmp))
if (sum(is.na(idx))>0) warning("some snps are missing!")
genotype=tmp[idx,7:ncol(tmp)]
rownames(genotype)=tmp$SNP[idx]
k <- which(is.na(genotype), arr.ind=TRUE)
length(k)/nrow(genotype)/ncol(genotype)
genotype[k] <- rowMeans(genotype, na.rm=TRUE)[k[,1]]
return(genotype)
}
form_traintestdata=function(prefix1="../result/PRSDNN_BCAtrain",prefix2="../result/PRSDNN_BCAtest",opt="EA")
{
traingenotype=readgenotype(prefix=prefix1)
colnames(traingenotype)=tolocalid(samplenames = colnames(traingenotype))
testgenotype=readgenotype(prefix=prefix2)
colnames(testgenotype)=tolocalid(samplenames = colnames(testgenotype))
idx1=match(colnames(traingenotype),sampletable$localid)
idx2=match(colnames(testgenotype),sampletable$localid)
if (opt=="EA")
{
trainy=as.numeric(I(sampletable$phenoEA_bca[idx1]==2))
testy=as.numeric(I(sampletable$phenoEA_bca[idx2]==2))
}
if (opt=="BEEA")
{
trainy=as.numeric(I(sampletable$phenoEABE_bca[idx1]==2))
testy=as.numeric(I(sampletable$phenoEABE_bca[idx2]==2))
}
return(list(traingenotype=traingenotype,trainy=trainy,testgenotype=testgenotype,testy=testy))
}
#EA
alldat=form_traintestdata()
#BEEA
alldat=form_traintestdata(prefix1="../result/PRSDNNBEEA_BCAtrain",prefix2="../result/PRSDNNBEEA_BCAtest",opt="BEEA")
traingenotype=alldat$traingenotype
trainy=alldat$trainy
testgenotype=alldat$testgenotype
testy=alldat$testy
#test run
model <- keras_model_sequential() %>%
layer_dense(units = 500,input_shape=c(nrow(traingenotype)),activation = "relu") %>%
layer_dropout(0.2) %>%
layer_dense(units = 250, activation = "relu") %>%
layer_dropout(0.2) %>%
layer_dense(units = 50, activation = "relu") %>%
layer_dropout(0.2) %>%
layer_dense(1, activation = "sigmoid")
summary(model)
auc=tf$keras$metrics$AUC(name='auc')
model %>%
compile(
loss = "binary_crossentropy",
optimizer=optimizer_adam(lr = 0.001),
metrics=auc
)
filepath = "weights_best_dropout.hdf5"
check = callback_model_checkpoint(filepath,
monitor='val_auc', # validation AUC
save_best_only=T,
mode='max')
earlyStopping = callback_early_stopping(monitor="val_auc", min_delta=0,patience=5,mode='auto')
set.seed(1000)
model %>%
fit(
x = t(traingenotype), y = trainy,
callbacks=list(check, earlyStopping),
batch_size=512,
epochs = 10,
validation_split = 0.3,
verbose = 3
)
predictions <- predict(model, t(testgenotype))
names(predictions)=colnames(testgenotype)
#boxplot(predictions~testy)
plot_ROC(predictions,opt="BEEA")
plot_ROC=function(predictions,opt="EA")
{
idx=match(names(predictions),sampletable$localid)
sampletable1=sampletable[idx,]
sampletable1$prs=predictions
#for EA
if (opt=="EA")
{
sampletable1$case=sampletable1$phenoEA_bca
}
if (opt=="BEEA")
{
sampletable1$case=sampletable1$phenoEABE_bca
}
sampletable1$case[sampletable1$case==-9]=NA
fit1 <- glm(I(case==2)~prs,family=binomial,data=sampletable1,y=T)
roc1=pROC::roc(fit1$y,fit1$fitted.values,quiet = T)
#remove bmi_recent_healthy
dat=sampletable1[,c("case","age","sex","recurrent_HB_RF","cig_smk_ever","nsaid_ever","prs","site")]
idx=complete.cases(dat)
if (sum(idx)>0)
{
fit3 <- glm(I(case==2)~age+sex+recurrent_HB_RF+cig_smk_ever+nsaid_ever+prs,family=binomial,data=sampletable1,y=T)
roc3=pROC::roc(fit3$y,fit3$fitted.values,quiet = T)
print(paste0("auc_prs=",round(roc1$auc,3)," auc_env=",round(roc3$auc,3)))
}else
{
print(paste0("auc_prs=",round(roc1$auc,3)))
}
return(round(roc1$auc,3))
}
auc=tf$keras$metrics$AUC(name='auc')
# check = callback_model_checkpoint(filepath,
# monitor='val_auc', # validation AUC
# save_best_only=T,
# mode='max')
earlyStopping = callback_early_stopping(monitor="val_auc", patience=3,mode='auto')
#4 hidden layers NN
testDNN4=function(n1=1000,n2=500,n3=250,n4=125,batch_size=512,lr=0.001,dropout1=0.2,dropout2=0.2,dropout3=0.2,dropout4=0.2,opt="EA",checkfile="EA_earlystop_dropout")
{
filepath = paste0("../result/weights_",checkfile,"_best.hdf5")
check = callback_model_checkpoint(filepath,
monitor='val_auc', # validation AUC
save_best_only=T,
mode='max')
model <- keras_model_sequential() %>%
layer_dense(units = n1, activation = "relu",input_shape=c(nrow(traingenotype))) %>%
layer_dropout(dropout1) %>%
layer_dense(units = n2, activation = "relu") %>%
layer_dropout(dropout2) %>%
layer_dense(units = n3, activation = "relu") %>%
layer_dropout(dropout3) %>%
layer_dense(units = n4, activation = "relu") %>%
layer_dropout(dropout4) %>%
layer_dense(1, activation = "sigmoid")
summary(model)
model %>%
compile(
loss = "binary_crossentropy",
optimizer=optimizer_adam(lr = lr),
#metrics = "accuracy"
metrics=auc
)
set.seed(1000)
model %>%
fit(
x = t(traingenotype), y = trainy,
callbacks=list(check, earlyStopping),
batch_size=batch_size,
epochs = 200,
validation_split = 0.3,
verbose = 3
)
predictions <- predict(model, t(testgenotype))
names(predictions)=colnames(testgenotype)
#boxplot(predictions~testy)
res=plot_ROC(predictions,opt=opt)
if (res>0.62)
{
mymodel=paste0("../result/NNmodel/",checkfile,n1,"_",n2,"_",n3,"batch_size",batch_size,"_",dropout1,"_",dropout2,"_",res)
save_model_tf(object = model, filepath = mymodel)
}
if (file.exists(filepath))
{
file.remove(filepath)
}
return(res)
}
#3 hidden layers NN
testDNN3=function(n1=1000,n2=250,n3=50,batch_size=512,lr=0.001,dropout1=0.2,dropout2=0.2,dropout3=0.3,opt="EA",checkfile="EA_earlystop_dropout")
{
filepath = paste0("../result/weights_",checkfile,"_best.hdf5")
check = callback_model_checkpoint(filepath,
monitor='val_auc', # validation AUC
save_best_only=T,
mode='max')
model <- keras_model_sequential() %>%
layer_dense(units = n1, activation = "relu",input_shape=c(nrow(traingenotype))) %>%
layer_dropout(dropout1) %>%
layer_dense(units = n2, activation = "relu") %>%
layer_dropout(dropout2) %>%
layer_dense(units = n3, activation = "relu") %>%
layer_dropout(dropout3) %>%
layer_dense(1, activation = "sigmoid")
summary(model)
model %>%
compile(
loss = "binary_crossentropy",
optimizer=optimizer_adam(lr = lr),
#metrics = "accuracy"
metrics=auc
)
set.seed(1000)
model %>%
fit(
x = t(traingenotype), y = trainy,
callbacks=list(check, earlyStopping),
batch_size=batch_size,
epochs = 200,
validation_split = 0.3,
verbose = 3
)
predictions <- predict(model, t(testgenotype))
names(predictions)=colnames(testgenotype)
#boxplot(predictions~testy)
res=plot_ROC(predictions,opt=opt)
if (res>0.62)
{
mymodel=paste0("../result/NNmodel/",checkfile,n1,"_",n2,"_",n3,"batch_size",batch_size,"_",dropout1,"_",dropout2,"_",res)
save_model_tf(object = model, filepath = mymodel)
}
if (file.exists(filepath))
{
file.remove(filepath)
}
return(res)
}
#2 hidden layers
testDNN2=function(n1=1000,n2=250,batch_size=512,lr=0.001,dropout1=0.2,dropout2=0.2,opt="EA",checkfile="EA_earlystop_dropout")
{
filepath = paste0("../result/weights_",checkfile,"_best.hdf5")
check = callback_model_checkpoint(filepath,
monitor='val_auc', # validation AUC
save_best_only=T,
mode='max')
model <- keras_model_sequential() %>%
layer_dense(units = n1, activation = "relu",input_shape=c(nrow(traingenotype))) %>%
layer_dropout(dropout1) %>%
layer_dense(units = n2, activation = "relu") %>%
layer_dropout(dropout2) %>%
layer_dense(1, activation = "sigmoid")
summary(model)
model %>%
compile(
loss = "binary_crossentropy",
optimizer=optimizer_adam(lr = lr),
metrics=auc
)
set.seed(1000)
model %>%
fit(
x = t(traingenotype), y = trainy,
callbacks=list(check, earlyStopping),
batch_size=batch_size,
epochs = 200,
validation_split = 0.3,
verbose = 3
)
predictions <- predict(model, t(testgenotype))
names(predictions)=colnames(testgenotype)
#boxplot(predictions~testy)
res=plot_ROC(predictions,opt=opt)
if (res>0.62)
{
mymodel=paste0("../result/NNmodel/",checkfile,n1,"_",n2,"batch_size",batch_size,"_",dropout1,"_",dropout2,"_",res)
save_model_tf(object = model, filepath = mymodel)
}
if (file.exists(filepath))
{
file.remove(filepath)
}
return(res)
}
#1 hidden layer
testDNN1=function(n1=1000,batch_size=512,lr=0.001,dropout=0.2,opt="EA",checkfile="EA_earlystop_dropout")
{
filepath = paste0("../result/weights_",checkfile,"_best.hdf5")
check = callback_model_checkpoint(filepath,
monitor='val_auc', # validation AUC
save_best_only=T,
mode='max')
model <- keras_model_sequential() %>%
layer_dense(units = n1, activation = "relu",input_shape=c(nrow(traingenotype))) %>%
layer_dropout(dropout) %>%
layer_dense(1, activation = "sigmoid")
summary(model)
model %>%
compile(
loss = "binary_crossentropy",
optimizer=optimizer_adam(lr = lr),
metrics=auc
#metrics=tf$keras$metrics$AUC()
)
set.seed(1000)
model %>%
fit(
x = t(traingenotype), y = trainy,
callbacks=list(check, earlyStopping),
batch_size=batch_size,
epochs = 200,
validation_split = 0.3,
verbose = 3
)
predictions <- predict(model, t(testgenotype))
names(predictions)=colnames(testgenotype)
#boxplot(predictions~testy)
res=plot_ROC(predictions,opt=opt)
if (res>0.62)
{
mymodel=paste0("../result/NNmodel/",checkfile,n1,"batch_size",batch_size,"_",dropout,"_",res)
save_model_tf(object = model, filepath = mymodel)
}
if (file.exists(filepath))
{
file.remove(filepath)
}
return(res)
}
traingenotype0=traingenotype
testgenotype0=testgenotype
cleangenotype=function(dat=traingenotype)
{
k <- which(dat>0 & dat<0.5, arr.ind=TRUE)
if (nrow(k)>0) dat[k]=0
k <- which(dat>=0.5 & dat<1.5 & dat!=1, arr.ind=TRUE)
if (nrow(k)>0) dat[k]=1
k <- which(dat>=1.5 & dat<2, arr.ind=TRUE)
if (nrow(k)>0) dat[k]=2
return(dat)
}
traingenotype=cleangenotype()
testgenotype=cleangenotype(dat=testgenotype)
save(traingenotype0,traingenotype,testgenotype0,testgenotype,trainy,testy,file="../result/PRS_DNN_EA_P05traindat.RData")
save(traingenotype,testgenotype,trainy,testy,file="../result/PRS_DNN_EA_P1traindat.RData")
#p=0.01
opt="EA"
checkfile="EA_earlystop_dropout"
opt="BEEA"
checkfile="BEEA_earlystop_dropout"
#p=0.05
opt="EA"
checkfile="EA_earlystop_p05_dropout"
opt="BEEA"
checkfile="BEEA_earlystop_p05_dropout"
allres3=NULL
for (n1 in c(3000,2000,1600,1400,1000))
{
n2=round(n1/2)
n3=round(n2/2)
for (batch_size in c(64,256,512))
{
for (lr in c(0.0001))
{
for (dropout1 in seq(0.2,0.4,0.2))
{
for (dropout2 in seq(0.2,0.4,0.2))
{
res=data.frame(opt=opt,n1=n1,n2=n2,n3=n3,batch_size=batch_size,lr=lr,dropout1=dropout1,dropout2=dropout2,dropout3=dropout2)
res$auc=testDNN3(n1,n2,n3,batch_size,lr,dropout1=dropout1,dropout2=dropout2,dropout3=dropout2,opt=opt,checkfile = checkfile)
allres3=rbind(allres3,res)
}
}
}
}
}
opt="EA"
checkfile="BEEA_4layer_earlystop_p05_dropout"
allres4=NULL
for (n1 in c(3000,2000,1600,1400,1000))
{
n2=round(n1/2)
n3=round(n2/2)
n4=round(n3/2)
for (batch_size in c(256,512))
{
for (lr in c(0.0001))
{
for (dropout1 in seq(0.2,0.4,0.2))
{
for (dropout2 in seq(0.2,0.4,0.2))
{
res=data.frame(opt=opt,n1=n1,n2=n2,n3=n3,n4=n4,batch_size=batch_size,lr=lr,dropout1=dropout1,dropout2=dropout2,dropout3=dropout2,dropout4=dropout2)
res$auc=testDNN4(n1,n2,n3,n4,batch_size,lr,dropout1=dropout1,dropout2=dropout2,dropout3=dropout2,dropout4=dropout2,opt=opt,checkfile = checkfile)
allres4=rbind(allres4,res)
}
}
}
}
}
# allres2=NULL
# for (n1 in c(1600,1400,1000,800))
# {
# n2=round(n1/2)
# for (batch_size in c(64,256,512))
# {
# for (lr in c(0.0001))
# {
# for (dropout1 in seq(0.2,0.4,0.2))
# {
# for (dropout2 in seq(0.2,0.4,0.2))
# {
# res=data.frame(opt=opt,n1=n1,n2=n2,batch_size=batch_size,lr=lr,dropout1=dropout1,dropout2=dropout2)
# res$auc=testDNN2(n1,n2,batch_size,lr,dropout1=dropout1,dropout2=dropout2,opt=opt,checkfile = checkfile)
# allres2=rbind(allres2,res)
# }
# }
# }
# }
# }
#
# allres1=NULL
# for (n1 in c(1600,1400,1000,800))
# {
# for (batch_size in c(64,256,512))
# {
# for (lr in c(0.0001))
# {
# for (dropout in seq(0.2,0.4,0.2))
# {
# res=data.frame(opt=opt,n1=n1,batch_size=batch_size,lr=lr,dropout=dropout,checkfile = checkfile)
# res$auc=testDNN1(n1,batch_size,lr,dropout=dropout,opt=opt)
# allres1=rbind(allres1,res)
# }
#
# }
# }
# }
#PRS_DNN_res.RData for pvalue=0.001
#PRS_DNN_res_01.RData for pvalue=0.01,without early stopping
#EA
save(allres3,allres2,allres1,file="../result/PRS_DNN_earlydropoutres_01.RData")
#BEEA
save(allres3,allres2,allres1,file="../result/PRS_DNNBEEA_earlydropoutres_01.RData")
#p=0.05
#EA
save(allres3,file="../result/PRS_DNN_earlydropoutres_05.RData")
#BEEA
save(allres3,allres2,allres1,file="../result/PRS_DNNBEEA_earlydropoutres_05.RData")
#to implement early stopping https://stackoverflow.com/questions/63150719/early-stopping-based-on-auc
#https://stackoverflow.com/questions/58682098/keras-callback-modelcheckpoint-doesnt-save-weights?rq=1
#https://community.rstudio.com/t/use-auc-as-metric-in-keras-for-r/84573/2
#https://cran.r-project.org/web/packages/keras/vignettes/training_callbacks.html
#check 3nn with many neurons
opt="EA"
checkfile="EA_largeNN_earlystop_p05_dropout"
allres3=NULL
for (n1 in c(10000,8000,6000,4000,3000))
{
n2=round(n1/2)
n3=round(n2/2)
for (batch_size in c(256,512))
{
for (lr in c(0.0001))
{
for (dropout1 in seq(0.2,0.4,0.2))
{
for (dropout2 in seq(0.2,0.4,0.2))
{
res=data.frame(opt=opt,n1=n1,n2=n2,n3=n3,batch_size=batch_size,lr=lr,dropout1=dropout1,dropout2=dropout2,dropout3=dropout2)
res$auc=testDNN3(n1,n2,n3,batch_size,lr,dropout1=dropout1,dropout2=dropout2,dropout3=dropout2,opt=opt,checkfile = checkfile)
allres3=rbind(allres3,res)
}
}
}
}
}
save(allres3,file="../result/PRS_DNN_earlydropoutres_05_large3NN.RData")
#p=0.1
#check 3nn with many neurons
opt="EA"
checkfile="EA_earlystop_p1_dropout"
allres3=NULL
for (n1 in c(10000,7000,6000,5000,4000,3000))
{
n2=round(n1/2)
n3=round(n2/2)
for (batch_size in c(256,512))
{
for (lr in c(0.0001))
{
for (dropout1 in seq(0.2,0.4,0.2))
{
for (dropout2 in seq(0.2,0.4,0.2))
{
res=data.frame(opt=opt,n1=n1,n2=n2,n3=n3,batch_size=batch_size,lr=lr,dropout1=dropout1,dropout2=dropout2,dropout3=dropout2)
res$auc=testDNN3(n1,n2,n3,batch_size,lr,dropout1=dropout1,dropout2=dropout2,dropout3=dropout2,opt=opt,checkfile = checkfile)
allres3=rbind(allres3,res)
gc()
}
}
}
}
}
save(allres3,file="../result/PRS_DNN_earlydropoutres_p1.RData")
#work on linear model
library(glmnet)
set.seed(100)
# require(doMC)
# registerDoMC(cores=12)
cvfit=cv.glmnet(t(traingenotype), trainy, family="binomial", standardize=F,parallel = F,nfolds=10, trace.it=1)
plot(cvfit)
glmcoeff=coef(cvfit,s=cvfit$lambda.min)
idx=glmcoeff@i
snps=glmcoeff@Dimnames[[1]][idx+1]
snps=snps[!snps %in% "(Intercept)"]
snps=as.numeric(gsub("V","",snps))
snps=rownames(traingenotype)[snps]
idx=match(snps,gwas$SNP)
quantile(gwas$P[idx])
# 0% 25% 50% 75% 100%
# 6.70900e-07 1.64000e-03 4.17550e-03 6.88925e-03 9.99700e-03
glmnet.predictions=predict(cvfit, s=cvfit$lambda.min,newx = data.matrix(t(testgenotype)), type = "response")
names(glmnet.predictions)=colnames(testgenotype)
#boxplot(predictions~testy)
plot_ROC(glmnet.predictions,opt="EA") #p=0.01:0.565,p=0.05:0.578
plot_ROC(glmnet.predictions,opt="BEEA") #p=0.01:0.564,p=0.05:0.571
#work on svm
library(e1071)
tune.out=tune(svm ,t(traingenotype),factor(trainy),scale=F, kernel ="radial", ranges =list(cost=c(0.1 ,1 ,10,20),gamma=c(0.01,0.1,0.5,1) ))
#summary(tune.out)
bestmod =tune.out$best.model
#summary(bestmod)
svmfit <- svm(t(traingenotype),factor(trainy),scale=F,kernel ="radial", cost=bestmod$cost,gamma = bestmod$gamma)
#svmfit <- svm(t(traingenotype),factor(trainy),scale=F,kernel ="radial")
load("../result/PRSSVMres.RData")
svm.predictions=predict(svmfit, t(testgenotype))
plot_ROC(svm.predictions,opt="EA") #0.552
#prediction from LDpred
tmp=read.table("../result/DNNvalidition_EA_genotyped.PRS.txt",header = T,stringsAsFactors = F)
ldpred.predictions=tmp$PRS
names(ldpred.predictions)=tmp$IID
load("../result/PRSSVMres_1.RData") #PRSSVM.R
plot_ROC(svm.predictions,opt="EA") #0.552
load("../result/PRSSVMres_2.RData") #PRSSVM.R
plot_ROC(svm.predictions,opt="EA") #0.569
svm.predictions1=predict(svmfit, t(testgenotype),probability = T)
plot_ROC(svm.predictions1,opt="EA")
plot_ROC(attributes(svm.predictions1)[4]$probabilities[,1])
svm.predictions=attributes(svm.predictions1)[4]$probabilities[,1]
#load prediction from NN
mymodel="../result/NNmodel/EA2_earlystop_p05_dropout_6_3000_1500_750batch_size256_0.2_0.2_0.621"
mymodel="../result/NNmodel/EA_binary_earlystop_p05_dropout_36_8000_4000_2000batch_size256_0.4_0.2_0.631"
rm(reloaded_model)
reloaded_model <- load_model_tf(mymodel)
NN.predictions <- predict(reloaded_model, t(testgenotype))
names(NN.predictions)=colnames(testgenotype)
plot_ROC(NN.predictions)
names(testy)=colnames(testgenotype)
library(ROCR) #prediction
library(pROC) #roc,auc,plot.roc
plotroc3=function(predict1=NN.predictions,predict2=svm.predictions,predict3=ldpred.predictions,y=testy,main="")
{
comsamples=intersect(names(predict1),names(predict2))
comsamples=intersect(names(predict3),comsamples)
predict1=predict1[match(comsamples,names(predict1))]
predict2=predict2[match(comsamples,names(predict2))]
predict3=predict3[match(comsamples,names(predict3))]
y=y[match(comsamples,names(y))]
predict1_=predict1[order(predict1)]
TP1=FP1=rep(0,length(y))
for (i in 1:length(y)) {
TP1[i] <- mean(predict1[y==1]>=predict1_[i])
FP1[i] <- mean(predict1[y==0]>=predict1_[i])
}
plot(c(FP1,0),c(TP1,0),type="l",xlim=c(0,1),ylim=c(0,1),lwd=4,xlab="1-specificity",ylab="Sensitivity",main=main,cex.lab=1.7,cex.axis=1.7,col="red",yaxs="i")
#abline(0,1,lty=2)
fit1 <- glm(I(y==1)~predict1,family=binomial,y=T)
roc1=pROC::roc(fit1$y,fit1$fitted.values,quiet = T)
auc1=roc1$auc
auc1=round(as.numeric(auc1),3)
print(paste0("auc1=",auc1))
# pauc1=pauc(marker = predict1,status = y,fpr = 0.05)
# print(paste0("pauc1=",round(pauc1,4)))
#text(0.07,0.94,paste0("AUC=",auc1),cex=1.2)
predict2=as.numeric(as.character(predict2))
predict2_=predict2[order(predict2)]
TP2=FP2=rep(0,length(y))
for (i in 1:length(y)) {
TP2[i] <- mean(predict2[y==1]>=predict2_[i])
FP2[i] <- mean(predict2[y==0]>=predict2_[i])
}
lines(c(FP2,0),c(TP2,0),lwd=4,col="green")
#lines(c(0.05,0.05),c(0,1),lty=2)
fit2 <- glm(I(y==1)~predict2,family=binomial,y=T)
roc2=pROC::roc(fit2$y,fit2$fitted.values,quiet = T)
auc2=roc2$auc
auc2=round(as.numeric(auc2),3)
print(paste0("auc2=",auc2))
# pauc2=pauc(marker = predict2,status = y,fpr = 0.05)
# print(paste0("pauc2=",round(pauc2,4)))
#
predict3_=predict3[order(predict3)]
TP3=FP3=rep(0,length(y))
for (i in 1:length(y)) {
TP3[i] <- mean(predict3[y==1]>=predict3_[i])
FP3[i] <- mean(predict3[y==0]>=predict3_[i])
}
lines(c(FP3,0),c(TP3,0),lwd=4,col="blue")
fit3 <- glm(I(y==1)~predict3,family=binomial,y=T)
roc3=pROC::roc(fit3$y,fit3$fitted.values,quiet = T)
auc3=roc3$auc
auc3=round(as.numeric(auc3),3)
print(paste0("auc3=",auc3))
# pauc3=pauc(marker = predict3,status = y,fpr = 0.05)
# print(paste0("pauc3=",round(pauc3,4)))
# auc.pvalue=roc.test(response=y,predictor1 = predict1,predictor2 = predict3)$p.value
# print(paste0("auc.pvalue=",round(auc.pvalue,4)))
# roc1 <- roc(y, predict1, partial.auc=c(1, 0.95), partial.auc.focus="sp")
# roc3 <- roc(y, predict3, partial.auc=c(1, 0.95), partial.auc.focus="sp")
# pauc.pvalue=roc.test(roc1, roc3)$p.value #0.47
# print(paste0("pauc.pvalue=",round(pauc.pvalue,4)))
legend=c(paste0("DNN : AUC=",round(auc1,3)),paste0("SVM : AUC=",round(auc2,3)),paste0("LDpred : AUC=",round(auc3,3)))
legend("topleft",legend=legend,col=c("red","green","blue"),lty=1,cex=1.5,lwd=4,bty = "n")
# res=data.frame(auc1=auc1,pauc1=pauc1,auc2=auc2,pauc2=pauc2,auc3=auc3,pauc3=pauc3,auc.pvalue=auc.pvalue,pauc.pvalue=pauc.pvalue,stringsAsFactors = F)
#return(res)
}
png(filename = "../result/PRS3ROC.png",type = "cairo")
par(mar=c(6,6,2,1))
plotroc3()
dev.off()
# we used the so-called one-hot encoding (Wan
# et al. 2010) in a subset of analyses, where each genotype is
# coded as a set of three binary variables instead of a number
#tray binary genotype data
genotype_2binary=function(dat=traingenotype)
{
tmp=length(unique(unlist(dat[,1:100])))
if (tmp>3) stop("data include non integer")
newdat=data.frame(matrix(NA,nrow=3*nrow(dat),ncol=ncol(dat)))
colnames(newdat)=colnames(dat)
rownames(newdat)[seq(1,nrow(newdat),3)]=paste0(rownames(dat),"_0")
rownames(newdat)[seq(2,nrow(newdat),3)]=paste0(rownames(dat),"_1")
rownames(newdat)[seq(3,nrow(newdat),3)]=paste0(rownames(dat),"_2")
k <- which(dat==0, arr.ind=TRUE)
tmp1=as.numeric((k[,1]-1)*3+1)
tmp2=as.numeric(k[,2])
tmp=as.matrix(data.frame(row=tmp1,col=tmp2))
newdat[tmp]=0
tmp[,1]=tmp[,1]+1
newdat[tmp]=0
tmp[,1]=tmp[,1]+1
newdat[tmp]=0
k <- which(dat==1, arr.ind=TRUE)
tmp1=as.numeric((k[,1]-1)*3+1)
tmp2=as.numeric(k[,2])
tmp=as.matrix(data.frame(row=tmp1,col=tmp2))
newdat[tmp]=0
tmp[,1]=tmp[,1]+1
newdat[tmp]=1
tmp[,1]=tmp[,1]+1
newdat[tmp]=0
k <- which(dat==2, arr.ind=TRUE)
tmp1=as.numeric((k[,1]-1)*3+1)
tmp2=as.numeric(k[,2])
tmp=as.matrix(data.frame(row=tmp1,col=tmp2))
newdat[tmp]=0
tmp[,1]=tmp[,1]+1
newdat[tmp]=0
tmp[,1]=tmp[,1]+1
newdat[tmp]=1
return(newdat)
}
traingenotype=genotype_2binary()
testgenotype=genotype_2binary(dat=testgenotype)
save(traingenotype,testgenotype,trainy,testy,file="../result/PRS_DNN_EA_binary_P05traindat.RData")
opt="EA"
checkfile="EA_binary_earlystop_p05_dropout"
allres3=NULL
for (n1 in c(8000,6000,4000,3000,1500,1000))
{
n2=round(n1/2)
n3=round(n2/2)
for (batch_size in c(256,512))
{
for (lr in c(0.0001))
{
for (dropout1 in seq(0.2,0.4,0.2))
{
for (dropout2 in seq(0.2,0.4,0.2))
{
res=data.frame(opt=opt,n1=n1,n2=n2,n3=n3,batch_size=batch_size,lr=lr,dropout1=dropout1,dropout2=dropout2,dropout3=dropout2)
res$auc=testDNN3(n1,n2,n3,batch_size,lr,dropout1=dropout1,dropout2=dropout2,dropout3=dropout2,opt=opt,checkfile = checkfile)
allres3=rbind(allres3,res)
gc()
}
}
}
}
}
save(allres3,file="../result/PRS_DNN_binary_earlydropoutres_05.RData")
opt="EA"
checkfile="EA_binary_earlystop_p05_4layer_dropout"
allres4=NULL
for (n1 in c(8000,6000,4000,3000,1500,1000))
{
n2=round(n1/2)
n3=round(n2/2)
n4=round(n3/2)
for (batch_size in c(256,512))
{
for (lr in c(0.0001))
{
for (dropout1 in seq(0.2,0.4,0.2))
{
for (dropout2 in seq(0.2,0.4,0.2))
{
res=data.frame(opt=opt,n1=n1,n2=n2,n3=n3,batch_size=batch_size,lr=lr,dropout1=dropout1,dropout2=dropout2,dropout3=dropout2,dropout4=dropout2)
res$auc=testDNN4(n1,n2,n3,n4,batch_size,lr,dropout1=dropout1,dropout2=dropout2,dropout3=dropout2,dropout4=dropout2,opt=opt,checkfile = checkfile)
allres4=rbind(allres4,res)
gc()
}
}
}
}
}
save(allres4,file="../result/PRS_DNN_binary_4layer_earlydropoutres_05.RData")
load("/fh/fast/dai_j/BEACON/BEACON_GRANT/data/GSE81334.RData")
BEID <- row.names(clinicaltable)[clinicaltable$type=="BE.BE"]
BE2ID <- row.names(clinicaltable)[clinicaltable$type=="BE.EAC"]
EACID <- row.names(clinicaltable)[clinicaltable$type=="EAC"]
BEmethy <- MEall[,names(MEall)%in% BEID]
BEmethy2 <- MEall[,names(MEall)%in% BE2ID]
EAmethy <- MEall[,names(MEall)%in% EACID]
alldat=cbind(BEmethy,BEmethy2,EAmethy)
types=c(rep("BE samples from BE patients",ncol(BEmethy)),rep("BE samples from EAC patients",ncol(BEmethy2)),rep("EAC samples from EAC patients",ncol(EAmethy)))
allcolors=c("blue","black","red","darkorchid1","limegreen","goldenrod1","brown4","darkseagreen","darkseagreen1")
allpch=c(15,16,17)
load("/fh/fast/dai_j/BEACON/BEACON_GRANT/data/Yu_paired_BE_EA_output.RData")
sum(Yuout[,1] < 0.05/nrow(Yuout) | Yuout[,2] < 0.05/nrow(Yuout))
probes=rownames(Yuout)[Yuout[,1] < 0.05/nrow(Yuout) | Yuout[,2] < 0.05/nrow(Yuout)]