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Figure_3.R
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Figure_3.R
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setwd("/Users/datn/github/SNP_array_comparison")
setwd("data/PRS_scores")
# get /dragennfs/area8/datnguyen/PRS_arrays_project/VN_1008_all/phased_1008/WGS/PRS_WGS/*all_score /media/datn/data2gb/GitHub/SNP_array_comparsion/data/PRS_scores
# get /dragennfs/area8/datnguyen/PRS_arrays_project/VN_1008_all/imputed_1008/PRS_result/*all_score /media/datn/data2gb/GitHub/SNP_array_comparsion/data/PRS_scores
# get /dragennfs/area8/datnguyen/PRS_arrays_project/WGS_PRS_IGSR/PRS_WGS/*all_score /media/datn/data2gb/GitHub/SNP_array_comparsion/data/PRS_scores
# get /dragennfs/area8/datnguyen/PRS_arrays_project/IGSR_imputed/PRS_result_all_pop/*all_score /media/datn/data2gb/GitHub/SNP_array_comparsion/data/PRS_scores
require(data.table)
# wgs_height = wgs = fread("WGS_VNP_HEIGHT.all_score")
# wgs_bmi = fread("WGS_VNP_BMI.all_score")
# arr = fread("Axiom_JAPONICA_HEIGHT.all_score")
# arr = fread("global-screening-array-v.3_HEIGHT.all_score")
# col = names(wgs)[6]
# cor(wgs[,..col], arr[,..col])
get_cor_array <- function(wgs, array, pop, trait){
#wgs_path = paste0("WGS_", pop, "_", trait, ".all_score" )
#wgs = fread()
cols = names(wgs)[-c(1,2)]
array_path = paste0(array, "_", pop, "_", trait, ".all_score")
arr = fread(array_path)
res = c()
for(col in cols){
tem = cor(wgs[,..col], arr[,..col])
res = c(res, tem)
}
res2 = c(array, trait, res, mean(res), sd(res))
names(res2) = c("array", "trait", cols, "mean", "sd")
return(res2)
}
#get_cor_array(wgs, "Axiom_JAPONICA", "VNP", "HEIGHT")
array_list = c("Axiom_GW_ASI", "Axiom_GW_CHB", "Axiom_GW_EUR", "Axiom_GW_PanAFR", "Axiom_JAPONICA", "infinium-omnizhonghua-v1.4", "infinium-psycharray-v1.3", "japanese-screening-array-v1.0", "multi-ethnic-eur-eas-sas-v1.0", "multi-ethnic-global-v1.0", "oncoarray-500k", "Axiom_PMDA", "Axiom_PMRA", "Axiom_UKB_WCSG", "chinese-genotyping-array-v1.0", "cytosnp-850k-v1.2", "global-screening-array-v.3", "human-cytosnp-12-v2.1", "infinium-core-v1.2", "infinium-global-diversity-array-v1.0", "infinium-omni2.5.v1.5", "infinium-omni5-v1.2", "GenomeWideSNP_6.0")
get_cor_pop <- function(pop, array_list, trait_list = c("HEIGHT", "BMI", "Type_2_diabetes")){
res = list()
for( trait in trait_list){
wgs_path = paste0("WGS_", pop, "_", trait, ".all_score" )
wgs = fread(wgs_path)
all_cor = list()
for( array in array_list){
tem = get_cor_array(wgs, array, pop, trait)
all_cor[[array]] = tem
}
res[[trait]] = as.data.frame(do.call(rbind, all_cor))
}
df = as.data.frame(do.call(rbind, res))
df$pop = pop
return(df)
}
#df = get_cor_pop("VNP", array_list)
pop_list = c("EAS", "EUR", "SAS", "AMR", "AFR", "VNP")
res_all = list()
for(pop in pop_list){
res_all[[pop]] = get_cor_pop(pop, array_list)
}
df = as.data.frame(do.call(rbind, res_all))
array_size = fread("../array_size.csv")
#
array_size$size = round(array_size$No.Assays / 1000, 0)
array_size$array = array_size$'Short name'
array_size$array_rename = paste0(array_size$array, "_", array_size$size, "k")
df$short_name = array_size$array[match(df$array, array_size$old_name)]
df$array_rename = array_size$array_rename[match(df$array, array_size$old_name)]
#df$array <- factor(df$array, levels = array_size$array)
df$array_rename <- factor(df$array_rename, levels = array_size$array_rename)
df$mean = as.numeric(df$mean)
df$sd = as.numeric(df$sd)
df2 = df
df2$trait[df2$trait == "HEIGHT"] = "Height"
require(ggplot2)
p = ggplot(data = df2, aes(x = array_rename, y = mean, fill = trait, color = trait)) + geom_point(stat="identity", position = position_dodge(.7)) + geom_errorbar(aes(x = array_rename, ymin = mean - sd, ymax = mean + sd), width = 0, position = position_dodge(.7)) + guides(x = guide_axis(angle = 90)) + scale_y_continuous(breaks=seq(0,1,0.02), limits = c(0.85,1) ) + theme_light() + ylab("PGS correlation") + xlab("SNP array") + facet_wrap(~ pop, nrow = 2) + theme(legend.position="bottom", axis.title.x = element_blank(), axis.text=element_text(size = 6)) + theme(legend.title=element_blank())
pdf(file= "../../output_paper/Figure_3.pdf", width=8, height=6)
p
dev.off()
### get table statistics
get_stat_pop_trait <- function(df, trait, pop_list = c("AFR", "AMR", "EAS", "EUR", "SAS", "VNP")){
#pop = "EAS"
#trait = "HEIGHT"
res = list()
for(pop in pop_list){
pick = df$trait == trait & df$pop == pop
tem = df[pick,]
tem$mean = round(tem$mean, 4)
tem$sd = round(tem$sd, 4)
tem$mean_sd = paste(tem$mean, tem$sd, sep = "±")
pick_col = c("short_name", "mean_sd")
tem = tem[,pick_col]
res[[pop]] = tem
}
res_df = as.data.frame(do.call(cbind, res))
pick = seq(1,ncol(res_df), by = 2)[-1]
res_df = res_df[,-pick]
names(res_df)[1] = "short_name"
return(res_df)
}
res_all = list()
trait_list = c("HEIGHT", "BMI", "Type_2_diabetes")
for(trait in trait_list){
res_all[[trait]] = get_stat_pop_trait(df, trait)
}
pop_list = c("EAS", "EUR", "SAS", "AMR", "AFR", "VNP")
for(pop in pop_list){
res_all[[pop]] = get_stat_pop_trait(df, pop)
}
# for sorting
require(data.table)
array_size = fread("../array_size.csv")
export_trait <- function(trait, res_all, array_size){
array_size$size = round(array_size$No.Assays / 1000, 0)
array_size$array = array_size$'Short name'
res = res_all[[trait]]
res2 = res_all[[trait]]
res2$size = array_size$No.Assays[match(res2$short_name, array_size$array)]
od = order(res2$size)
res = res[od,]
out_name = paste0("../../output_paper/tables/", trait ,"_PRS_correlation.csv")
fwrite(res, out_name, sep = ",")
}
for(trait in trait_list){
export_trait(trait, res_all, array_size)
}
# # pdf(file= "PRS_acc_height_bmi_6_pop.pdf", width=12, height=8)
# # p
# # dev.off()
# ### get table statistics
# get_stat_pop_trait <- function(df, pop, trait_list = c("HEIGHT", "BMI", "Type_2_diabetes")){
# #pop = "EAS"
# #trait = "HEIGHT"
# res = list()
# for(trait in trait_list){
# pick = df$trait == trait & df$pop == pop
# tem = df[pick,]
# tem$mean = round(tem$mean, 4)
# tem$sd = round(tem$sd, 4)
# tem$mean_sd = paste(tem$mean, tem$sd, sep = "±")
# pick_col = c("short_name", "mean_sd")
# tem = tem[,pick_col]
# res[[trait]] = tem
# }
# res_df = as.data.frame(do.call(cbind, res))
# pick = seq(1,ncol(res_df), by = 2)[-1]
# res_df = res_df[,-pick]
# names(res_df)[1] = "short_name"
# return(res_df)
# }
# res_all = list()
# pop_list = c("EAS", "EUR", "SAS", "AMR", "AFR", "VNP")
# for(pop in pop_list){
# res_all[[pop]] = get_stat_pop_trait(df, pop)
# }
# # for sorting
# array_size = fread("../array_size.csv")
# export_pop <- function(pop_list, res_all, array_size){
# array_size$size = round(array_size$No.Assays / 1000, 0)
# array_size$array = array_size$'Short name'
# require(data.table)
# #pop_list = c("AFR", "AMR")
# tem = res_all[pop_list]
# res = merge(res_all[[pop_list[1]]], res_all[[pop_list[2]]], by = "short_name")
# res2 = res
# res2$size = array_size$No.Assays[match(res2$short_name, array_size$array)]
# od = order(res2$size)
# res = res[od,]
# out_name = paste(pop_list, collapse = "_")
# out_name = paste0(out_name, "_PRS_cor_12_cutoff.csv")
# fwrite(res, out_name, sep = ",")
# }
# export_pop(c("AFR", "AMR"), res_all, array_size)
# export_pop(c("EAS", "EUR"), res_all, array_size)
# export_pop(c("SAS", "VNP"), res_all, array_size)