#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("<", "&lt;", 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")

}