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Figure_2.R
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setwd("/Users/datn/github/SNP_array_comparison")
setwd("data/Imputation_correlation")
require(data.table)
require(ggplot2)
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")
#c("(0-0.01]", "(0.01-0.05]", "(0.01-0.5]", "(0.05-0.5]")
get_acc_pop <- function(pop, array_list, cutoffs = list("(0-0.01]" = c(0, 0.01), "(0.01-0.05]" = c(0.01, 0.05), "(0.01-0.5]" = c(0.01, 0.5), "(0.05-0.5]" = c(0.05,0.5))){
require(data.table)
# pop = "EAS"
# 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")
# array = array_list[1]
# cutoffs = list("(AC>1, MAF<=0.01]" = c(0, 0.01), "(MAF>0.01, MAF<=0.05]" = c(0.01, 0.05))
#CREATE list to save values
chr_list = as.character(c(1:22))
tem_vec = rep(0.0, 22)
names(tem_vec) = chr_list
tem_list = list()
sizes = rep(0, length(cutoffs))
names(sizes) = names(cutoffs)
for(cut in names(cutoffs)){
tem_list[[cut]] = tem_vec
}
res_list = list()
array_statistis = list(imp_acc = tem_list, imp_cov = tem_list)
for(array in array_list){
res_list[[array]] = array_statistis
}
sizes = rep(0, length(cutoffs))
names(sizes) = names(cutoffs)
for( chr in chr_list){
path = paste0("chr", chr, "_", pop, "_merged_23_array_correlation.txt.gz")
tem = fread(path)
tem = tem[tem$AC >1,]
for(cut in names(cutoffs)){
x = cutoffs[[cut]]
pick = tem$MAF > x[1] & tem$MAF <= x[2]
sizes[cut] = sizes[cut] + sum(pick)
}
for(array in array_list){
pick_col = c(colnames(tem)[1:4], array)
tem2 = tem[,..pick_col]
tem2$r_2 = tem2[[array]]^2
for(cut in names(cutoffs)){
x = cutoffs[[cut]]
pick = tem2$MAF > x[1] & tem2$MAF <= x[2]
acc = mean(tem2$r_2[pick], na.rm = T)
cov = sum(tem2$r_2[pick] >= 0.8, na.rm = T) / sum(pick)
res_list[[array]][["imp_acc"]][[cut]][chr] = acc
res_list[[array]][["imp_cov"]][[cut]][chr] = cov
}
}
}
res = list()
for(array in array_list){
for(cut in names(cutoffs)){
mean_r2 = mean(res_list[[array]][["imp_acc"]][[cut]])
sd_r2 = sd(res_list[[array]][["imp_acc"]][[cut]])
mean_cov = mean(res_list[[array]][["imp_cov"]][[cut]])
sd_cov = sd(res_list[[array]][["imp_cov"]][[cut]])
res_df = data.frame(array = array, number_variant = sizes[cut], mean_r2 = mean_r2, sd_r2 = sd_r2, mean_cov = mean_cov, sd_cov = sd_cov, MAF_range = cut, pop = pop)
res = c(res, list(res_df))
}
}
df = do.call("rbind", res)
return(df)
}
#t = get_acc_pop("EAS", array_list)
pop_list = c("EAS", "EUR", "SAS", "AMR", "AFR", "VNP")
res_pop = list()
# RUN IT WILL TAKE LONG TIME
# for( pop in pop_list){
# tem = get_acc_pop(array_list, pop = pop)
# #tem$pop = pop
# # rename KHV to VNP
# #if(pop == "KHV") tem$pop = "VNP"
# res_pop[[pop]] = tem
# #res_pop[[pop]] = get_stat_pop(array_list2, pop = pop)
# }
df = as.data.frame(do.call(rbind, res_pop))
#fwrite(df, file = "cor_data_all_pop_DF.csv", sep = "\t")
df = fread("cor_data_all_pop_DF.csv")
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$size, "k")
df$array_rename = array_size$array_rename[match(df$array, array_size$old_name)]
df$short_name = array_size$array[match(df$array, array_size$old_name)]
#df$array <- factor(df$array, levels = array_size$array)
df$array_rename <- factor(df$array_rename, levels = array_size$array_rename)
df_all = df
pick = df$MAF_range == "(0.01-0.5]"
###################### main figure
df = df_all[pick,]
require(ggplot2)
library(patchwork)
p1 = ggplot(data=df, aes(x = array_rename, y = mean_r2, fill = MAF_range, color = MAF_range)) +
geom_point(stat="identity", position = position_dodge(.5)) + geom_errorbar(aes(x = array_rename, ymin = mean_r2 - sd_r2, ymax = mean_r2 + sd_r2), width = 0, position = position_dodge(.5)) + guides(x = guide_axis(angle = 90)) + scale_y_continuous(breaks=seq(0,1,0.1)) + theme_light() + ylab("Imputation r2") + xlab("SNP arrays") + facet_wrap(~ pop, nrow = 2) + ggtitle("A")
p1x = p1 + theme(legend.position="none") + theme(
axis.text.x = element_blank(),
axis.ticks.x = element_blank(),
axis.title.x = element_blank()
)
p2 = ggplot(data=df, aes(x = array_rename, y = mean_cov, fill = MAF_range, color = MAF_range)) +
geom_point(stat="identity", position = position_dodge(.5)) + geom_errorbar(aes(x = array_rename, ymin = mean_cov - sd_r2, ymax = mean_cov + sd_r2), width = 0, position = position_dodge(.5)) + guides(x = guide_axis(angle = 90)) + scale_y_continuous(breaks=seq(0,1,0.1)) + theme_light() + ylab("Impuation coverage") + xlab("SNP arrays") + facet_wrap(~ pop, nrow = 2) + ggtitle("B") + theme(legend.position="bottom", axis.title.x = element_blank(), axis.text=element_text(size = 6)) + theme(legend.position="none")
l = list(p1x,p2)
m = wrap_plots(l, nrow = 2, plot_layout(guides = "auto"))
#ggplot(data=df, aes(x = array, y = mean_cov, fill = MAF_range)) + geom_bar(stat="identity", color="black", position=position_dodge()) + geom_errorbar(aes(x = array, ymin = mean_cov - sd_r2, ymax = mean_cov + sd_r2), width = 0.5, position=position_dodge(.9)) + guides(x = guide_axis(angle = 45)) + scale_y_continuous(breaks=seq(0,1,0.05)) + theme_light() + ylab("Mean coverage (r2s >= 0.8)") + xlab("SNP array") + facet_wrap(~ pop, nrow = 2)
pdf( file= "../../output_paper/Figure_2.pdf", width= 7, height=8)
m
dev.off()
#################### supplement figure
df = df_all[!pick,]
require(ggplot2)
library(patchwork)
p1 = ggplot(data=df, aes(x = array_rename, y = mean_r2, fill = MAF_range, color = MAF_range)) +
geom_point(stat="identity", position = position_dodge(.5)) + geom_errorbar(aes(x = array_rename, ymin = mean_r2 - sd_r2, ymax = mean_r2 + sd_r2), width = 0, position = position_dodge(.5)) + guides(x = guide_axis(angle = 90)) + scale_y_continuous(breaks=seq(0,1,0.2)) + theme_light() + ylab("Imputation r2") + xlab("SNP arrays") + facet_wrap(~ pop, nrow = 2) + ggtitle("A")
p1x = p1 + theme(legend.position="none") + theme(
axis.text.x = element_blank(),
axis.ticks.x = element_blank(),
axis.title.x = element_blank()
)
p2 = ggplot(data=df, aes(x = array_rename, y = mean_cov, fill = MAF_range, color = MAF_range)) +
geom_point(stat="identity", position = position_dodge(.5)) + geom_errorbar(aes(x = array_rename, ymin = mean_cov - sd_r2, ymax = mean_cov + sd_r2), width = 0, position = position_dodge(.5)) + guides(x = guide_axis(angle = 90)) + scale_y_continuous(breaks=seq(0,1,0.2)) + theme_light() + ylab("Impuation coverage") + xlab("SNP arrays") + facet_wrap(~ pop, nrow = 2) + ggtitle("B") + theme(legend.position="bottom", axis.title.x = element_blank(), axis.text=element_text(size = 6)) + theme(legend.title=element_blank())
l = list(p1x,p2)
m = wrap_plots(l, nrow = 2, plot_layout(guides = "auto"))
#ggplot(data=df, aes(x = array, y = mean_cov, fill = MAF_range)) + geom_bar(stat="identity", color="black", position=position_dodge()) + geom_errorbar(aes(x = array, ymin = mean_cov - sd_r2, ymax = mean_cov + sd_r2), width = 0.5, position=position_dodge(.9)) + guides(x = guide_axis(angle = 45)) + scale_y_continuous(breaks=seq(0,1,0.05)) + theme_light() + ylab("Mean coverage (r2s >= 0.8)") + xlab("SNP array") + facet_wrap(~ pop, nrow = 2)
pdf(file= "../../output_paper/Figure_2_sup.pdf", width=7, height=8)
m
dev.off()
# get statistic tables
# imputation accuracy
array_size = fread("../array_size.csv")
get_acc_bin <- function(df, array_size, bin, out_fn, pop_list = c("AFR", "AMR", "EAS", "EUR", "SAS", "VNP")){
df = as.data.frame(df)
df$mean_r2 = as.numeric(df$mean_r2)
df$sd_r2 = as.numeric(df$sd_r2)
df$mean_cov = as.numeric(df$mean_cov)
df$sd_cov = as.numeric(df$sd_cov)
df$mean_r2 = round(df$mean_r2, 4)
df$sd_r2 = round(df$sd_r2, 4)
df$mean_sd_r2 = paste(df$mean_r2, df$sd_r2, sep = "±")
df = df[df$MAF_range == bin,]
res_list = list()
for(pop in pop_list){
tem = df[df$pop == pop, c("array", "short_name", "mean_sd_r2")]
res_list[[pop]] = tem
}
res = do.call("cbind", res_list)
res = as.data.frame(res)
res = res[, c(1:3,6,9,12,15,18)]
n = names(res)
n[1] = "array"
n[2] = "short_name"
names(res) = n
array_size$size = round(array_size$No.Assays / 1000, 0)
array_size$array = array_size$'Short name'
res2 = res
res2$size = array_size$No.Assays[match(res$short_name, array_size$array)]
od = order(res2$size)
res = res[od,]
fwrite(res, out_fn, sep = ",")
}
bin_list = c("(0-0.01]", "(0.01-0.05]", "(0.01-0.5]", "(0.05-0.5]")
for(bin in bin_list){
out_fn = paste0("../../output_paper/tables/", "imputation_r2_", bin, ".csv")
get_acc_bin(df_all, array_size, bin, out_fn)
}
### coverage
# imputation accuracy
array_size = fread("../array_size.csv")
get_cov_bin <- function(df, array_size, bin, out_fn, pop_list = c("AFR", "AMR", "EAS", "EUR", "SAS", "VNP")){
df = as.data.frame(df)
df$mean_r2 = as.numeric(df$mean_r2)
df$sd_r2 = as.numeric(df$sd_r2)
df$mean_cov = as.numeric(df$mean_cov)
df$sd_cov = as.numeric(df$sd_cov)
# df$mean_r2 = round(df$mean_r2, 4)
# df$sd_r2 = round(df$sd_r2, 4)
# df$mean_sd_r2 = paste(df$mean_r2, df$sd_r2, sep = "±")
df$mean_cov = round(df$mean_cov, 4)
df$sd_cov = round(df$sd_cov, 4)
df$mean_sd_cov = paste(df$mean_cov, df$sd_cov, sep = "±")
df = df[df$MAF_range == bin,]
res_list = list()
for(pop in pop_list){
tem = df[df$pop == pop, c("array", "short_name", "mean_sd_cov")]
res_list[[pop]] = tem
}
res = do.call("cbind", res_list)
res = as.data.frame(res)
res = res[, c(1:3,6,9,12,15,18)]
n = names(res)
n[1] = "array"
n[2] = "short_name"
names(res) = n
array_size$size = round(array_size$No.Assays / 1000, 0)
array_size$array = array_size$'Short name'
res2 = res
res2$size = array_size$No.Assays[match(res$short_name, array_size$array)]
od = order(res2$size)
res = res[od,]
fwrite(res, out_fn, sep = ",")
}
bin_list = c("(0-0.01]", "(0.01-0.05]", "(0.01-0.5]", "(0.05-0.5]")
for(bin in bin_list){
out_fn = paste0("../../output_paper/tables/", "imputation_cov_", bin, ".csv")
get_cov_bin(df_all, array_size, bin, out_fn)
}