## FOR THE USER - PLACE INPUT FILES HERE - CAN CHANGE readRDS or read.csv if you so wish 

data = readRDS("Methylation_object_here.rds")

sexinfo <- read.csv("Sexinfo_file_here.csv", header = T)

## Start to Process Files 

message("1. Loading data") 

message("1.1 Loading Methylation data - rows to be CpGs and columns to be individuals") 

cpgs <- read.csv("./Predictors_Shiny_by_Groups.csv", header = T) 
age = cpgs[which(cpgs$Predictor %in% "Age"),]
cpgs = cpgs[-which(cpgs$Predictor %in% "Age"),]

## Check if Data needs to be Transposed

message("2. Quality Control and data Preparation") 

message("2.1 Checking if Row Names are CpG Sites") 

if(ncol(data) > nrow(data)){
  message("It seems that individuals are rows - data will be transposed!")
  data<-t(data) 
}

message("2.2 Subsetting CpG sites to those required for Predictor Calculation") 

## Subset CpG sites to those present on list for predictors 

coef=data[intersect(rownames(data), cpgs$CpG_Site),]

## Set aside data for age as this will not use scaled DNAm data for calculation 
coef_age=data[intersect(rownames(data), age$CpG_Site),]


## Check if Beta or M Values 

m_to_beta <- function (val) 
{
  beta <- 2^val/(2^val + 1)
  return(beta)
}

coef<-if((range(coef,na.rm=T)> 1)[[2]] == "TRUE") { message("Suspect that M Values are present. Converting to Beta Values");m_to_beta(coef) } else { message("Suspect that Beta Values are present");coef}
coef_age<-if((range(coef_age,na.rm=T)> 1)[[2]] == "TRUE") { message("Suspect that M Values are present. Converting to Beta Values");m_to_beta(coef_age) } else { message("Suspect that Beta Values are present");coef_age}



## Scale Data if Needed 

ids = colnames(coef)
scaled <- apply(coef, 1, function(x) sd(x,na.rm = T)) 

coef <-  if(range(scaled)[1] == 1 & range(scaled)[2] == 1) { 
    coef
  } else { 
    coef_scale <- apply(coef, 1, scale)
    coef_scale <- t(coef_scale)
    coef_scale <- as.data.frame(coef_scale)
    colnames(coef_scale) <- ids
   coef_scale
  } 


message("2.3 Find CpGs not present in uploaded file, add these with mean Beta Value for CpG site from Training Sample") 

## Identify CpGs missing from input dataframe, include them and provide values as mean methylation value at that site

coef <- if(nrow(coef) == 7662) { message("All sites present"); coef } else if(nrow(coef)==0){ 
  message("There Are No Necessary CpGs in The dataset - All Individuals Would Have Same Values For Predictors. Analysis Is Not Informative!")
} else { 
  missing_cpgs = cpgs[-which(cpgs$CpG_Site %in% rownames(coef)),c("CpG_Site","Mean_Beta_Value")]
  message(paste(length(unique(missing_cpgs$CpG_Site)), "unique sites are missing - add to dataset with mean Beta Value from Training Sample", sep = " "))
  mat = matrix(nrow=length(unique(missing_cpgs$CpG_Site)),ncol = ncol(coef))
  row.names(mat) <- unique(missing_cpgs$CpG_Site)
  colnames(mat) <- colnames(coef) 
  mat[is.na(mat)] <- 1
  missing_cpgs1 <- if(length(which(duplicated(missing_cpgs$CpG_Site))) > 1) { 
    missing_cpgs[-which(duplicated(missing_cpgs$CpG_Site)),]
  } else {missing_cpgs
  }  
  ids = unique(row.names(mat))
  missing_cpgs1 = missing_cpgs1[match(ids,missing_cpgs1$CpG_Site),]
  mat=mat*missing_cpgs1$Mean_Beta_Value
  coef=rbind(coef,mat) } 

    #### Age done separately 


coef_age <- if(nrow(coef_age) == 514) { message("All sites present"); coef_age } else if(nrow(coef_age)==0){ 
  message("There Are No Necessary CpGs in The dataset - All Individuals Would Have Same Values For Predictors. Analysis Is Not Informative!")
} else { 
  missing_cpgs = age[-which(age$CpG_Site %in% rownames(coef_age)),c("CpG_Site","Mean_Beta_Value")]
  message(paste(length(unique(missing_cpgs$CpG_Site)), "unique sites are missing - add to dataset with mean Beta Value from Training Sample", sep = " "))
  mat = matrix(nrow=length(unique(missing_cpgs$CpG_Site)),ncol = ncol(coef_age))
  row.names(mat) <- unique(missing_cpgs$CpG_Site)
  colnames(mat) <- colnames(coef_age) 
  mat[is.na(mat)] <- 1
  missing_cpgs1 <- if(length(which(duplicated(missing_cpgs$CpG_Site))) > 1) { 
    missing_cpgs[-which(duplicated(missing_cpgs$CpG_Site)),]
  } else {missing_cpgs
  }  
  ids = unique(row.names(mat))
  missing_cpgs1 = missing_cpgs1[match(ids,missing_cpgs1$CpG_Site),]
  mat=mat*missing_cpgs1$Mean_Beta_Value
  coef_age=rbind(coef_age,mat) } 




message("2.4 Convert NA Values to Mean for each Probe") 

## Convert NAs to Mean Value for all individuals across each probe 

na_to_mean <-function(methyl){
  methyl[is.na(methyl)]<-mean(methyl,na.rm=T)
  return(methyl)
}

coef <- t(apply(coef,1,function(x) na_to_mean(x)))
coef_age <- t(apply(coef_age,1,function(x) na_to_mean(x)))


message("3. Calculating the Predictors") 

loop = unique(cpgs$Predictor)
out <- data.frame()
for(i in loop){ 
  tmp=coef[intersect(row.names(coef),cpgs[cpgs$Predictor %in% i,"CpG_Site"]),]
  tmp_coef = cpgs[cpgs$Predictor %in% i, ]
  if(nrow(tmp_coef) > 1) { 
    tmp_coef = tmp_coef[match(row.names(tmp),tmp_coef$CpG_Site),]
    out[colnames(coef),i]=colSums(tmp_coef$Coefficient*tmp)
  } else {
    tmp2 = as.matrix(tmp)*tmp_coef$Coefficient 
    out[colnames(coef),i] = tmp2[,1]
  }
} 


loop1 = unique(age$Predictor)
out_age <- data.frame()
for(i in loop1){ 
  tmp=coef_age[intersect(row.names(coef_age),age[age$Predictor %in% i,"CpG_Site"]),]
  tmp_coef = age[age$Predictor %in% i, ]
  if(nrow(tmp_coef) > 1) { 
    tmp_coef = tmp_coef[match(row.names(tmp),tmp_coef$CpG_Site),]
    out_age[colnames(coef_age),i]=colSums(tmp_coef$Coefficient*tmp)
  } else {
    tmp2 = as.matrix(tmp)*tmp_coef$Coefficient 
    out_age[colnames(coef_age),i] = tmp2[,1]
  }
} 

out_age$Age <- out_age$Age + 65.79295
out$ID <- row.names(out) 
out_age$ID <- row.names(out_age) 
out <- merge(out, out_age, by = "ID")
out <- out[,c(1,ncol(out),2:(ncol(out)-1))] 

## combine Sex information
if(!exists("sexinfo")){
  out$Sex <- NA 
} else { 
  ids = out$ID
  sexinfo = sexinfo[match(ids, sexinfo$ID),] 
  out$Sex <- sexinfo$Sex
}

out <- out[,c(1,ncol(out),2:c(ncol(out)-1))]

## Save File and Finish Up 
message("Analysis Finished! Thank you for using our application. Output File is called \"out\"")