https://github.com/DanniGadd/EpiScores-for-protein-levels
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Tip revision: a5130fab3895a0d95f0dcc8826aa9fb5e8c0fa86 authored by DanniGadd on 16 February 2022, 08:46:38 UTC
Merge branch 'main' of https://github.com/DanniGadd/EpiScores-for-protein-levels
Tip revision: a5130fa
10_project_to_generation_scotland_wave_3.R
Copyright (c) <2022>, <DanniGadd>
All rights reserved.

This source code is licensed under the MIT license found in the
LICENSE file in the root directory.

############################################################################################
############################################################################################
################# Projection into GS Wave 3 ################################################
############################################################################################
############################################################################################

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

data = readRDS("/Cluster_Filespace/Marioni_Group/GS/GS_methylation/wave3_mvals.rds")

sexinfo <- read.csv("/Cluster_Filespace/Marioni_Group/GS/GS_methylation/wave3-final/samplesheet.final.csv")
sexinfo$Sex <- ifelse(sexinfo$sex %in% "M", "Male", "Female")
sexinfo$ID <- sexinfo$Sample_Sentrix_ID
sexinfo <- sexinfo[,c("ID", "Sex")]

## Start to Process Files 

message("1. Loading data") 

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

library(tidyverse)

# Weight file 
# cpgs <- read.csv("/Cluster_Filespace/Marioni_Group/Danni/Proxies_stradl/KORA_train_recieved_020221/weights_KORA.csv")
cpgs <- read.delim("/Cluster_Filespace/Marioni_Group/Danni/LBC_proteins_Jan2021/00_Running_pQTLs_regressed/Weights_230221/weights_KORA.csv")

un <- unique(cpgs$Predictor) %>% as.data.frame() # 793 unique predictors 
names(un) <- "Protein"

# Create matchable ID 
cpgs$ID<- gsub("\\:.*","", cpgs$Predictor)
cpgs$ID <- gsub("\\_.*","", cpgs$ID)
cpgs <- cpgs[c(1,2,4)]
names(cpgs)[3] <- "Predictor"

# Remove intercept
intercepts <- which(cpgs$CpG_site %in% "(Intercept)")
cpgs <- cpgs[-intercepts,]



names(cpgs)[1] <- "CpG_Site"


#cpgs <- read.csv("/Cluster_Filespace/Marioni_Group/Danni/LBC_proteins/outputs/Glmnet_re_runs_for_all_analyses/00_Figure_1_R2_results_comparison/Predictors_by_groups_sep292020.csv", check.names = F) 
# cpgs <- read.csv("/Cluster_Filespace/Marioni_Group/Danni/LBC_proteins_Jan2021/Model_extract_weights/predictors_joint_to_phenotypes_220121.csv", check.names = F)
# age = cpgs[which(cpgs$Predictor %in% "Biological Age"),]
# cpgs = cpgs[-which(cpgs$Predictor %in% "Biological 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) == 21526) { 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]
#   }
# } 

# names(out_age)[1] <- "Age"
# 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\"") 


## Save file 

write.csv(out, "/Cluster_Filespace/Marioni_Group/Danni/LBC_proteins_Jan2021/00_Running_pQTLs_regressed/Model_projections/KORA_GS_W3_projections.csv")


#################################################################################################################

# Combine the data from waves 1 and 3 first and save out (commented out after inital use)

#################################################################################################################

# Read in GS data
W1 <- read.csv("/Cluster_Filespace/Marioni_Group/Danni/LBC_proteins_Jan2021/00_Running_pQTLs_regressed/Model_projections/KORA_GS_W1_projections.csv", check.names = F)

W3 <- read.csv("/Cluster_Filespace/Marioni_Group/Danni/LBC_proteins_Jan2021/00_Running_pQTLs_regressed/Model_projections/KORA_GS_W3_projections.csv", check.names = F)


# Combine so we have measures across all of GS (they have matching colnames)
combine <- rbind(W1, W3)

# Save this file out 
write.csv(combine, "/Cluster_Filespace/Marioni_Group/Danni/LBC_proteins_Jan2021/00_Running_pQTLs_regressed/Model_projections/KORA_GS_combined_9537_projections.csv", row.names = F)


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