#merge EHR and CpG
{
library(impute)
library(tibble)
library(dplyr)
library(impute)
#JHU
{
setwd("E:\\workplace\\mywork\\methy\\dbgap\\chf\\data_chf_contr\\early_chf\\c1_UMN_JHU\\train_UMN_tset_JHU/1123_dataSummary")
#cell
load(file="test/CellFraction.Rdata")
load(file="HFpEF.Rdata")
data_cpg_test = filter(HFpEF,PACKS_SET == "JHU")
data_cpg_chf <- filter(data_cpg_test , chf == 1)
data_cpg_nochf <- filter(data_cpg_test , chf == 0)
data_cpg <- rbind(data_cpg_chf,data_cpg_nochf)
data_cpg$Sample_Name <- paste(rep(c("chf","control"),c(nrow(data_cpg_chf),nrow(data_cpg_nochf))),
c(c(1:nrow(data_cpg_chf)),c(1:nrow(data_cpg_nochf))),sep = "")
test_meta <- data_cpg[,-c(4:7)]
test_meta <- test_meta[,c(128,1:127)]
test_meta_raw <- cbind(test_meta,CellFraction)
{
colnames(test_meta_raw)= c("Sample_Name","shareid",
"Ejectionfraction","PACKS_SET",
"Omega3","Omega3amount",
"Statin","Statinamount",
"Thiazides","Thiazidesamount",
"Diuretic","Diureticamount",
"Potassium","Potassiumamount",
"Aldosterone","Aldosteroneamount",
"Amiodarone","Amiodaroneamount",
"Vasodilators","Vasodilatorsamount",
"CoQ10","CoQ10amount",
"Betablocking","Betablockingamount",
"AngiotensinIIantagonists","AngiotensinIIantagonistsamount",
"ACEI","ACEIamount",
"Warfarin","Warfarinamount",
"Clopidogrel","Clopidogrelamount",
"Aspirin","Aspirinamount",
"Folicacid","Folicacidamount",
"cvd","Coronaryheartdisease","Heartfailure",
"chddate","chfdate","cvddate","midate",
"Myocardialinfarction","Diabetes",
"Atrialfibrillation","afxdate",
"Stroke","strokedate",
"DATE8","DATE9",
"Gender","Age","Bloodglucose","BMI","LDLcholesterol",
"Numberofcigarettessmoked","Creatinineserum",
"Smoking","Averagediastolicbloodpressure",
"Leftventricularhypertrophy",
"Fastingbloodglucose","HDLcholesterol","Hight",
"Averagesystolicbloodpressure",
"Totalcholesterol","Triglycerides" ,"Ventricularrate",
"Waist","Weight","Treatedforhypertension",
"Treatedforlipids","aspirin.1",
"Drinkbeer","Drinkwine","Drinkliquor","Sleep",
"Albuminurine", "Creatinineurine" ,
"HemoglobinA1cwholeblood","Atrialenlargement",
"Rightventricularhypertrophy","lvh","Rheumatic",
"Aorticvalve","Mitralvalve","other_heart",
"Arrhythmia","other_peripheral_vascular_disease",
"other_vascular_diagnosis",
"Dementia","Parkinson","Adultseizuredisorder" ,
"Neurological","Thyroid","Endocrine","Renal","Gynecologic",
"Emphysema","Pneumonia","Asthma","Pulmonary","Gout",
"Degenerative","Rheumatoidarthritis","Musculoskeletal","Gallbladder",
"Gerd","Liver","Gidisease","Hematologicdisorder","Bleedingdisorder",
"Eye","Ent","Skin","other","Depression","Anxiety","Psychosis",
"other2","Prostate","Infectious","Fever","pneumonia.1",
"Chronicbronchitis","emphysema.1","COPD","Creactiveprotein",
"CD8T","CD4T","NK","Bcell","Mono","Gran")
}
setwd("E:\\workplace\\mywork\\methy\\dbgap\\chf\\data_chf_contr\\early_chf\\c1_UMN_JHU\\train_UMN_tset_JHU/1123_dataSummary/test/")
save(test_meta_raw,file="test_meta_raw.Rdata")
test_meta = test_meta_raw[,!colnames(test_meta_raw) %in% c("PACKS_SET","Omega3amount","Statinamount","Thiazidesamount","Diureticamount",
"Potassiumamount" , "Aldosteroneamount" , "Amiodaroneamount",
"Vasodilatorsamount","CoQ10amount","Betablockingamount",
"AngiotensinIIantagonistsamount", "ACEIamount" , "Warfarinamount" ,
"Clopidogrelamount" , "Aspirinamount" , "Folicacidamount" ,"chddate" ,
"chfdate" ,"cvddate" ,"midate" ,"afxdate" ,"strokedate" ,"DATE8","lvh","cvd",
"DATE9","aspirin.1","other_heart","other_peripheral_vascular_disease",
"other_vascular_diagnosis","other","other2","pneumonia.1","emphysema.1")]
}
}
#EHR
{
#sex
test_meta$Gender <- ifelse(test_meta$Gender == 1,"F","M")
test_meta = test_meta[,-c(1,2)]
{
library(table1)
library(tableone)
{
#0,1 feature
c = c("Omega3" ,"Statin" , "Thiazides" ,"Diuretic" ,"Potassium" ,"Aldosterone",
"Amiodarone","Vasodilators" , "CoQ10", "Betablocking",
"AngiotensinIIantagonists" , "ACEI", "Warfarin","Clopidogrel" ,"Aspirin" ,
"Folicacid",
"Coronaryheartdisease", "Myocardialinfarction", "Diabetes","Atrialfibrillation" , "Stroke" ,
"Smoking" ,"Leftventricularhypertrophy" ,"Treatedforhypertension", "Treatedforlipids" ,
"Drinkbeer", "Drinkwine" ,"Drinkliquor",
"Atrialenlargement","Rightventricularhypertrophy" ,
"Rheumatic", "Aorticvalve","Mitralvalve" ,"Arrhythmia",
"Dementia", "Parkinson",
"Adultseizuredisorder","Neurological" , "Thyroid","Endocrine" ,
"Renal" , "Gynecologic", "Emphysema" , "Pneumonia" ,"Asthma","Pulmonary",
"Gout" ,"Degenerative", "Rheumatoidarthritis","Musculoskeletal" ,
"Gallbladder" , "Gerd" , "Liver" , "Gidisease" ,"Hematologicdisorder" ,
"Bleedingdisorder" , "Eye" , "Ent" , "Skin", "Depression" ,"Anxiety" ,
"Psychosis" ,"Prostate" ,"Infectious", "Fever" ,
"Chronicbronchitis" , "COPD" )
for(i in c){
test_meta[,i]<- as.factor(test_meta[,i])
test_meta[,i]<- as.logical(test_meta[,i] == 1)}
{
label(test_meta$Ejectionfraction) <- "Ejection fraction"
label(test_meta$CoQ10) <- "CoQ 10"
label(test_meta$Omega3) <- "Omega 3"
label(test_meta$AngiotensinIIantagonists) <- "Angiotensin II antagonists"
label(test_meta$Betablocking) <- "Beta blocking"
label(test_meta$Coronaryheartdisease) <- "Coronary heart disease"
label(test_meta$Myocardialinfarction) <- "Myocardial infarction"
label(test_meta$Atrialfibrillation) <- "Atrial fibrillation"
label(test_meta$Bloodglucose) <- "Blood glucose"
label(test_meta$LDLcholesterol) <- "LDL cholesterol"
label(test_meta$Numberofcigarettessmoked) <- "Number of cigarettes smoked"
label(test_meta$Creatinineserum) <- "Creatinine serum"
label(test_meta$Averagediastolicbloodpressure) <- "Average diastolic blood pressure"
label(test_meta$Leftventricularhypertrophy) <- "Left ventricular hypertrophy"
label(test_meta$Fastingbloodglucose) <- "Fasting blood glucose"
label(test_meta$HDLcholesterol) <- "HDL cholesterol"
label(test_meta$Averagesystolicbloodpressure) <- "Average systolic blood pressure"
label(test_meta$Totalcholesterol) <- "Total cholesterol"
label(test_meta$Ventricularrate) <- "Ventricular rate"
label(test_meta$Treatedforhypertension) <- "Treated for hypertension"
label(test_meta$Treatedforlipids) <- "Treated for lipids"
label(test_meta$Drinkbeer) <- "Drink beer"
label(test_meta$Drinkwine) <- "Drink wine"
label(test_meta$Drinkliquor) <- "Drink liquor"
label(test_meta$Albuminurine) <- "Albumin urine"
label(test_meta$Creatinineurine) <- "Creatinine urine"
label(test_meta$HemoglobinA1cwholeblood) <- "Hemoglobin A1c whole blood"
label(test_meta$Atrialenlargement) <- "Atrial enlargement"
label(test_meta$Rightventricularhypertrophy) <- "Right ventricular hypertrophy"
label(test_meta$Aorticvalve) <- "Aortic valve"
label(test_meta$Mitralvalve) <- "Mitral valve"
label(test_meta$Adultseizuredisorder) <- "Adultseizure disorder"
label(test_meta$Hematologicdisorder) <- "Hematologic disorder"
label(test_meta$Bleedingdisorder) <- "Bleeding disorder"
label(test_meta$Chronicbronchitis) <- "Chronic bronchitis"
label(test_meta$Creactiveprotein) <- "C reactive protein"
label(test_meta$CD8T) <- "CD8+ T cell"
label(test_meta$CD4T) <- "CD4+ T cell"
label(test_meta$NK) <- "Natural killer cell"
label(test_meta$Bcell) <- "B cell"
label(test_meta$Mono) <- "Monocyte cell"
label(test_meta$Gran) <- "Granulocytes cell"
}
{
units(test_meta$Age) <- "years"
units(test_meta$Sleep) <- "hours"
units(test_meta$Numberofcigarettessmoked) <- "day"
units(test_meta$CD8T) <- "proportions"
units(test_meta$CD4T) <- "proportions"
units(test_meta$NK) <- "proportions"
units(test_meta$Bcell) <- "proportions"
units(test_meta$Mono) <- "proportions"
units(test_meta$Gran) <- "proportions"
test_meta$Heartfailure <- factor(test_meta$Heartfailure, levels=c(0, 1, 2), labels=c("NoChf", "Chf", "P-value"))
}
rndr <- function(x, name, ...) {
if (length(x) == 0) {
y <- test_meta[[name]]
s <- rep("", length(render.default(x=y, name=name, ...)))
if (is.numeric(y)) {
p <- wilcox.test(y ~ test_meta$Heartfailure)$p.value #wilcoxon = Mann-Whitney U
} else {#t.test
p <- chisq.test(table(y, droplevels(test_meta$Heartfailure)))$p.value
}
s[2] <- sub("<", "<", format.pval(p, digits=3, eps=0.001))
s
} else {
render.default(x=x, name=name, ...)
}
}
rndr.strat <- function(label, n, ...) {
ifelse(n==0, label, render.strat.default(label, n, ...))
}
table1(~ Gender+Age+
CD8T+CD4T+NK+Bcell+Mono+Gran+
Smoking+Numberofcigarettessmoked+BMI+Hight+Waist+Weight+
Fastingbloodglucose+Bloodglucose+LDLcholesterol+HDLcholesterol+Totalcholesterol+
Triglycerides+Ventricularrate+Averagediastolicbloodpressure+Averagesystolicbloodpressure+
Treatedforhypertension+Treatedforlipids+
Ejectionfraction+Creactiveprotein+Creatinineserum+Creatinineurine+
Albuminurine+HemoglobinA1cwholeblood+
Drinkbeer+Drinkwine+Drinkliquor+Sleep+
Omega3+Statin+Thiazides+Diuretic+Potassium+Aldosterone+Amiodarone+Vasodilators+CoQ10+
Betablocking+AngiotensinIIantagonists+ACEI+Warfarin+Clopidogrel+Aspirin+Folicacid+
Coronaryheartdisease+Myocardialinfarction+Diabetes+Atrialfibrillation+
Stroke+Leftventricularhypertrophy+Atrialenlargement+Rightventricularhypertrophy+
Rheumatic+Aorticvalve+Mitralvalve+Arrhythmia+Dementia+Parkinson+Adultseizuredisorder+
Neurological+Thyroid+Endocrine+Renal+Gynecologic+Emphysema+Pneumonia+Asthma+Pulmonary+Gout+
Degenerative+Rheumatoidarthritis+Musculoskeletal+Gallbladder+Gerd+Liver+
Gidisease+Hematologicdisorder+Bleedingdisorder+Eye+Ent+Skin+Depression+
Anxiety+Psychosis+Prostate+Infectious+Fever+Chronicbronchitis+COPD | Heartfailure,data=test_meta,
droplevels=F, render=rndr, render.strat=rndr.strat, overall=F)
}
}
#rm- same of UMN
{
#p value
test_meta <- test_meta[,!colnames(test_meta) %in% c("Ejectionfraction", "Omega3", "Statin",
"Thiazides", "Potassium",
"Aldosterone", "Amiodarone", "Vasodilators", "CoQ10",
"Warfarin", "Clopidogrel", "Folicacid", "Myocardialinfarction", "Stroke",
"Numberofcigarettessmoked", "Smoking", "Leftventricularhypertrophy", "Hight", "Triglycerides", "Ventricularrate",
"Drinkbeer", "Drinkwine", "Drinkliquor", "Sleep", "Creatinineurine",
"Mitralvalve", "Arrhythmia", "Dementia",
"Parkinson", "Adultseizuredisorder", "Neurological",
"Thyroid", "Endocrine", "Renal", "Gynecologic", "Emphysema",
"Pneumonia", "Asthma", "Pulmonary", "Gout", "Degenerative",
"Musculoskeletal", "Gallbladder", "Gerd", "Liver",
"Gidisease", "Hematologicdisorder", "Bleedingdisorder",
"Eye", "Ent", "Skin", "Depression", "Anxiety", "Psychosis",
"Prostate", "Infectious", "Fever", "Chronicbronchitis",
"COPD","CD8T","CD4T","NK","Bcell","Mono","Gran")]
#missing
test_meta <- test_meta[,!colnames(test_meta) %in% c("Diabetes")]
# all 0
test_meta <- test_meta[,!colnames(test_meta) %in% c("Rightventricularhypertrophy")]
save(test_meta,file="test_meta.Rdata")
}
#impute
{
library(tibble)
library(impute)
load(file="test_meta_raw.Rdata")
load(file="test_meta.Rdata")
test_meta <- test_meta_raw[,colnames(test_meta_raw) %in% c(colnames(test_meta))]
#same feature is many NA (NA>1%)
Patient_impute_test <- impute.knn(as.matrix(data.frame(t(test_meta))))
Patient_impute_test <- data.frame(t(Patient_impute_test$data))
#change 0,1,
for(i in c("LDLcholesterol","Fastingbloodglucose","Atrialenlargement")){
Patient_impute_test[,i] <- round(Patient_impute_test[,i],0)
}
for(i in c("BMI","Waist","Albuminurine","Creactiveprotein")){
Patient_impute_test[,i] <- round(Patient_impute_test[,i],2)
}
write.csv(Patient_impute_test,"Patient_impute_test.csv")
}
Patient_impute_test <- read.table("Patient_impute_test.csv",sep=",",header = T,row.names = 1)
colnames(Patient_impute_test)= c("Diuretic","Beta blocking",
"Angiotensin II antagonists","ACEI",
"Aspirin","Coronary heart disease","Heart failure",
"Atrial fibrillation","Gender","Age","Blood glucose","BMI",
"LDL cholesterol","Creatinine serum",
"Average diastolic blood pressure","Fasting blood glucose",
"HDL cholesterol","Average systolic blood pressure",
"Total cholesterol","Waist","Weight",
"Treated for hypertension","Treated for lipids",
"Albumin urine","Hemoglobin a1c whole blood",
"Atrial enlargement","Rheumatic","Aortic valve",
"Rheumatoid arthritis","C reactive protein")
##rm cor<0.8 feature same UMN
Patient_impute_test <- Patient_impute_test[,!colnames(Patient_impute_test) %in% c("Blood glucose","LDL cholesterol","Waist","Weight")]#删除彼此相关性高，和chf相关性低的特征,还剩下41-5=36
write.csv(Patient_impute_test,"Patient_impute_test_25.csv")
#norm
{
#normalization-not 0,1 feature
normalization<-function(x){
return((x-min(x))/(max(x)-min(x)))}
#c= c("AGE8","BMI8","CREAT8","DBP8","FASTING_BG8","HDL8","SBP8","TC8,Albumin_urine","Hemoglobin_A1c_wholeblood","crp")
for(i in c(10:17,20,21,26)){
Patient_impute_test[,i] <- normalization(Patient_impute_test[,i])
}
write.csv(Patient_impute_test,"Patient_impute_test_25_nor.csv")
}
}
#merge cpg and valid EHR
{
#beta
load(file= "CorrectedBeta.Rdata")
new_beta_test = data.frame(t(CorrectedBeta))
new_beta_test[1:4,1:10]
#valid EHR
Patient_impute <- read.csv("Patient_impute_test_25_nor.csv",head=T,row.names = 1)
#beta+ehr
test_data <- cbind(Patient_impute,new_beta_test)
save(test_data,file="test_data.Rdata")
}
Datasets
Models
