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mvmr_estimates.R
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library(TwoSampleMR)
rm(list=ls())
gc()
library("readxl")
library(TwoSampleMR)
library(tidyverse)
library(RadialMR)
library(GWAS.utils)
library("readxl")
library(TwoSampleMR)
library(phenoscanner)
library(LDlinkR)
library(dplyr)
library(cause)
library(RadialMR)
library(MVMR)
library(MRPRESSO)
library(boot.pval)
library(MendelianRandomization)
library(RMVMR)
server<-'home'
corfile<-'.../2_20/result_save/rmvmr_clump_0.01/_corr.txt'
whole_result_folder<-'.../hypertension/systolic'#b1
whole_result_folder<-'.../hypertension/exp_systolic2ich'#b2
cvs_path<-'.../hypertension/alldata.csv'
mvmr_tool<-'.../2_20/code/mvmr_tool.R'
bfile<-"F:/home/featurize/work/gwas_pairs/plink_reference/EUR"
source('.../2_20/code/umr_tool.R')
###setting
source(mvmr_tool)
b<-'b1'
ld_matrix_cal<-FALSE
gencov_cal<-FALSE
develop<-TRUE
train_type<-'mvmr'
ptm <- proc.time()
style<-'Amrican'
d<-read_csv(cvs_path)
sample_rename_fun<-function(d_exp){
d_exp[is.na(d_exp$sample_size),]$sample_size<-d_exp[is.na(d_exp$sample_size),]$ncase+d_exp[is.na(d_exp$sample_size),]$ncontrol
d_exp$abbr<-NA
d_exp$abbr[d_exp$id=='ebi-a-GCST006804']<-'RCDW'
d_exp$abbr[d_exp$id=='ukb-d-30040_irnt']<-'MCV'
d_exp$abbr[d_exp$id=='ieu-b-38']<-'SBP'
d_exp$abbr[d_exp$id=='ieu-b-39']<-'DBP'
d_exp$abbr[d_exp$id=='finn-b-R18_ELEVATED_ERYTHROCYTE_SEDIM_RATE_ABNORMALITY_PLASMA_VISCO']<-'BD'
d_exp$abbr[d_exp$id=='finn-b-I9_ICH']<-'ICH'
d_exp$abbr[d_exp$id=='finn-b-I9_INTRACRA']<-'nITH'
return(d_exp)
}
out_name<-function(x){
if(x=='ebi-a-GCST006804'){
return('RCDW')
} else if (x=='ukb-d-30040_irnt'){
return('MCV')
} else if
(x=='ieu-b-38'){
return('SBP')
}else if
(x=='ieu-b-39'){
return('DBP')
}else if
(x=='finn-b-R18_ELEVATED_ERYTHROCYTE_SEDIM_RATE_ABNORMALITY_PLASMA_VISCO'){
return('BD')
}else if
(x=='finn-b-I9_ICH'){
return('ICH')
}else if(x=='finn-b-I9_INTRACRA'){
return('nITH')
} else{
return(x)
}
}
change_name_mvmr<-function(nam,id_outcome,style='American'){
n<-length(nam)/2
for (i in 1:length(nam)){
if (i<=n){
if (nam[i]==out_name(nam[i])){
outcome_abbr<-out_name_new_list(id_outcome,id2abbr_real=1,reverse=FALSE,style=style)
nam[i]<-paste0(outcome_abbr,'_b')
} else{
nam[i]<-paste0(out_name(nam[i]),'_b')
}
} else {
if (nam[i]==out_name(nam[i])){
outcome_abbr<-out_name_new_list(id_outcome,id2abbr_real=1,reverse=FALSE,style=style)
nam[i]<-paste0(outcome_abbr,'_se')
}else{
nam[i]<-paste0(out_name(nam[i]),'_se')
}
}
}
re<-list('renam'=nam,'out_abbr'=outcome_abbr)
return(re)
}
id_fun<-function(b){
d<-read_csv(cvs_path)
exp_id<-c('ebi-a-GCST006804','ukb-d-30040_irnt')
exp_list<-d%>%filter(id %in%exp_id)
exp_list$abbr<-out_name_new_list(exp_list$id,id2abbr_real=1,reverse=FALSE,style=style)
if (b=='b0'){
out_id<-c("finn-b-I9_SAHANEUR", "finn-b-ST19_TRAUMAT_SUBAR_HAEMORRHAGE",
"finn-b-I9_SAH", "finn-b-ST19_INTRACRATRAUMA", "finn-b-I9_INTRACRA",
"finn-b-I9_ICH")
out_list<-d%>%filter(id %in%out_id)
out_list$abbr<-out_name_new_list(out_list$id,id2abbr_real=1,reverse=FALSE,style=style)
dat<-list('exp_list'=exp_list,'out_list'=out_list,'exp_id'=exp_id,'out_id'=out_id)
return(dat)
}else if(b=='b2'){
med_id<-c("ieu-b-39","ieu-b-38","finn-b-R18_ELEVATED_ERYTHROCYTE_SEDIM_RATE_ABNORMALITY_PLASMA_VISCO")
med_list<-d%>%filter(id %in%med_id)
med_list$abbr<-out_name_new_list(med_list$id,id2abbr_real=1,reverse=FALSE,style=style)
out_id<-c('finn-b-I9_INTRACRA','finn-b-I9_ICH')
out_list<-d%>%filter(id %in%out_id)
out_list$abbr<-out_name_new_list(out_list$id,id2abbr_real=1,reverse=FALSE,style=style)
dat<-list('exp_list'=exp_list,'med_list'=med_list,'out_list'=out_list,'exp_id'=exp_id,'med_id'=med_id,'out_id'=out_id)
}else if(b=='b1'){
out_id<-c("ieu-b-39","ieu-b-38","finn-b-R18_ELEVATED_ERYTHROCYTE_SEDIM_RATE_ABNORMALITY_PLASMA_VISCO")
out_list<-d%>%filter(id %in%out_id)
out_list$abbr<-out_name_new_list(out_list$id,id2abbr_real=1,reverse=FALSE,style=style)
dat<-list('exp_list'=exp_list,'out_list'=out_list,'exp_id'=exp_id,'out_id'=out_id)
return(dat)
}else{stop('check b')}
}
t<-id_fun(b)
clean_exp<-function(data){
for (i in 1:nrow(data)){
if ('exposure' %in% names(data)){
tt<-strsplit(data$exposure[i],split = '||',fixed = TRUE)[[1]][1]
data$exposure[i]<-trimws(tt, which = c("both", "left", "right"), whitespace = "[ \t\r\n]")
} else if('outcome' %in% names(data)){
tt<-strsplit(data$outcome[i],split = '||',fixed = TRUE)[[1]][1]
data$outcome[i]<-trimws(tt, which = c("both", "left", "right"), whitespace = "[ \t\r\n]")
} else(print('check columns'))
}
return(data)
}
action<-3
clump_r2 = 0.01
maf_threshold = 0.01
mr_fun<-function(t,id_exposure,exposure_dat){
for (i in 1:nrow(t$out_list)){
id_outcome<-t$out_list$id[i]
print(paste0('MVMR The ',i,' out of ',nrow(t$out_list),' trial!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!'))
print(paste('Exposure ID: ',id_exposure))
print(paste('Outcome ID: ',id_outcome))
# folder<-paste(whole_result_folder,'mvmr',id_outcome[i],sep='/')
if (ld_matrix_cal){
folder<-paste(whole_result_folder,'mvmr_corr',path_name,id_outcome,sep='/')
} else{
folder<-paste(whole_result_folder,'mvmr_no_corr',path_name,id_outcome,sep='/')
}
dir.create(folder, showWarnings = TRUE, recursive = TRUE)
setwd(folder)
save(exposure_dat,file=paste0('exposure_dat',action,'.RData'))
# if (file.exists(paste0('merge_exp_action',action,'_multi_presso.RData'))){
# print(paste0('This ','action ',action,' The ',i,' trial had been done before!'))
# } else{
re_log<-paste0('action',action,'_','mvmr_log.log')
#unlink(re_log,recursive = TRUE)
if (!develop){
con <- file(re_log)
sink(con, append=TRUE) # 记录output
sink(con, append=TRUE, type="message") # 记录message
}
print(rep('#',20))
print(paste0('The trial has exp SNP no.: ',nrow(exposure_dat)/2))
outcome_dat <- extract_outcome_data(exposure_dat$SNP, id_outcome, proxies = TRUE,
maf_threshold = maf_threshold)
outcome_dat$outcome<-clean_name_fun(outcome_dat$outcome,style=style)
print(paste0('mr_keep.outcome SNP no.: ', nrow(outcome_dat)))
table( outcome_dat$mr_keep.outcome)
outcome_dat<-outcome_dat[outcome_dat$mr_keep.outcome==TRUE,]
print(paste0('after mr_keep.outcome delete SNP no.: ', nrow(outcome_dat)))
mvdat <- mv_harmonise_data(exposure_dat, outcome_dat)
#print(paste0('Ori SNP No.:',nrow(Mydata)))
save(mvdat,file=paste0('mvmr_har_action',action,'.RData'))
mv_presso_data<-cbind(mvdat$exposure_beta,mvdat$outcome_beta,mvdat$exposure_se,mvdat$outcome_se)
mv_presso_data <-as.data.frame(mv_presso_data)
change_names<-change_name_mvmr(colnames(mv_presso_data),id_outcome)$renam
out_abbr<-change_name_mvmr(colnames(mv_presso_data),id_outcome)$out_abbr
colnames(mv_presso_data)<-change_names
#colnames(mv_presso_data)<-c('RCDW_effect','MCV_effect','Stroke_effect','RCDW_se','MCV_se','Stroke_se')
nrow(distinct(exposure_dat,SNP, .keep_all = TRUE))==nrow(exposure_dat)
data_for_merge<-mv_presso_data
data_for_merge$SNP<-rownames(data_for_merge)
nrow(distinct(data_for_merge,SNP, .keep_all = TRUE))==nrow(data_for_merge)
data_for_corr<-data_for_merge%>%inner_join(., distinct(exposure_dat[,c('SNP','effect_allele.exposure','other_allele.exposure')],SNP,.keep_all=TRUE),
c("SNP" = "SNP"))%>%rename(allele_0=effect_allele.exposure,allele_1=other_allele.exposure)%>%dplyr::select(allele_0,allele_1,RCDW_b,RCDW_se, MCV_b, MCV_se,everything())
stopifnot(nrow(data_for_corr)==nrow(data_for_merge))
#rownames(data_for_corr)<-data_for_corr$SNP
rawdat_mvmr<-data_for_corr
save(rawdat_mvmr,file='orginal_har.RData')
bx_matrix = as.matrix(dplyr::select(rawdat_mvmr,ends_with('_b'),-paste0(out_abbr,'_b')))
sebx_matrix = as.matrix(dplyr::select(rawdat_mvmr,ends_with('_se'),-paste0(out_abbr,'_se')))
#if (file.exists('mr_package_input_snp_correlation_raw.RData'))
if (ld_matrix_cal){
if(file.exists('mr_package_input_snp_correlation_raw.RData')){
load('mr_package_input_snp_correlation_raw.RData')
} else{
mr_package_input_snp_corr<-ieugwasr::ld_matrix(
rawdat_mvmr$SNP,
plink_bin = genetics.binaRies::get_plink_binary(),
bfile =bfile)
save(mr_package_input_snp_corr,file='mr_package_input_snp_correlation_raw.RData')
}
mr_package_lasso<-mr_mvlasso(mr_mvinput(bx = bx_matrix, bxse = sebx_matrix,
by = rawdat_mvmr[,paste0(out_abbr,'_b')],correlation = mr_package_input_snp_corr,
byse =rawdat_mvmr[,paste0(out_abbr,'_se')],snps =rawdat_mvmr$SNP,outcome = id_outcome,exposure = id_exposure,effect_allele =rawdat_mvmr$allele_0,other_allele =rawdat_mvmr$allele_1 ))
} else{
mr_package_lasso<-mr_mvlasso(mr_mvinput(bx = bx_matrix, bxse = sebx_matrix,
by = rawdat_mvmr[,paste0(out_abbr,'_b')],
byse = rawdat_mvmr[,paste0(out_abbr,'_se')],snps =rawdat_mvmr$SNP,outcome = id_outcome,exposure = id_exposure,effect_allele =rawdat_mvmr$allele_0,other_allele =rawdat_mvmr$allele_1 ))
}
print(rep(1,20))
############# #mr_package_input_snp_corr<-ld_matrix(raddat$SNP, with_alleles = TRUE, pop = "EUR")
####lasso
rm(mr_package_input_snp_corr)
gc()
save(mr_package_lasso,file=paste0('model_action',action,'_MRpackage_lasso.RData'))
print(paste0('Original SNP no.: ', mr_package_lasso@SNPs))
print(paste0('Valid SNP no.: ', mr_package_lasso@Valid))
rawdat_mvmr<-rawdat_mvmr[rawdat_mvmr$SNP %in% mr_package_lasso@ValidSNPs,]
#snp_num<-as.numeric(unlist(strsplit(mr_package_lasso@ValidSNPs,split='snp_',fixed = TRUE)))
#snp_num<-snp_num[!is.na(snp_num)]
#rawdat_mvmr<- rawdat_mvmr[snp_num,]
bx_matrix = as.matrix(as.matrix(dplyr::select(rawdat_mvmr,ends_with('_b'),-paste0(out_abbr,'_b'))))
sebx_matrix = as.matrix(as.matrix(dplyr::select(rawdat_mvmr,ends_with('_se'),-paste0(out_abbr,'_se'))))
save(rawdat_mvmr,file=paste0('Action_',action,'_after_lasso_har_data.RData'))
#
if (ld_matrix_cal){
if(file.exists('mr_package_input_snp_correlation_after_lasso.RData')){
load('mr_package_input_snp_correlation_after_lasso.RData')
}else{
mr_package_input_snp_corr_after_lasso<-ieugwasr::ld_matrix(
rawdat_mvmr$SNP,
plink_bin = genetics.binaRies::get_plink_binary(),
bfile =bfile
)
save(mr_package_input_snp_corr_after_lasso,file='mr_package_input_snp_correlation_after_lasso.RData')
}
# rmvmr
mvmr_dat_input<-mr_mvinput(bx = bx_matrix, bxse = sebx_matrix,
by = rawdat_mvmr[,paste0(out_abbr,'_b')],correlation = mr_package_input_snp_corr_after_lasso,
byse = rawdat_mvmr[,paste0(out_abbr,'_se')],snps =rawdat_mvmr$SNP,outcome = id_outcome,exposure = id_exposure,effect_allele =rawdat_mvmr$allele_0,other_allele =rawdat_mvmr$allele_1 )
} else{
mvmr_dat_input<-mr_mvinput(bx = bx_matrix, bxse = sebx_matrix,
by = rawdat_mvmr[,paste0(out_abbr,'_b')],
byse = rawdat_mvmr[,paste0(out_abbr,'_se')],snps =rawdat_mvmr$SNP,outcome = id_outcome,exposure = id_exposure,effect_allele =rawdat_mvmr$allele_0,other_allele =rawdat_mvmr$allele_1 )
}
# rmvmr
# rmvmr_dat <- mrmvinput_to_rmvmr_format(mvmr_dat_input)
# head(rmvmr_dat)
# class(rmvmr_dat)
#
#
# print(rep('#',20))
# print(paste0('The ',i,' after har SNP No.:',nrow(rawdat_mvmr)))
# F.rmvmr<-format_rmvmr(BXGs=dplyr::select(rawdat_mvmr,ends_with('_b'),-paste0(out_abbr,'_b')),
# BYG=rawdat_mvmr[,paste0(out_abbr,'_b')],
# seBXGs=dplyr::select(rawdat_mvmr,ends_with('_se'),-paste0(out_abbr,'_se')),
# seBYG=rawdat_mvmr[,paste0(out_abbr,'_se')],
# RSID=rawdat_mvmr$SNP)
# if (gencov_cal){
# covv<-read.table(corfile,header=F)
# covv<-as.matrix(covv)
#
# phecov<-phenocov_mvmr(covv, rmvmr_dat[,c('sebetaX1' , 'sebetaX2', 'sebetaX3')])
# print(rep('#',10))
# print('F')
# F_stre_rmvmr<-strength_rmvmr(r_input=rmvmr_dat,gencov=phecov)
# } else{
# print(rep('#',10))
# print('F')
# F_stre_rmvmr<-strength_rmvmr(F.rmvmr)
# }
#
# print(F_stre_rmvmr$f)
#
# if (server=='hpc'|server=='featurize'){
# pdf(file = "rmvmr_strength_plot_rcdw.pdf", paper = "a4")
# print(F_stre_rmvmr$plot[[1]])
# dev.off()
# pdf(file = "rmvmr_strength_plot_mcv.pdf", paper = "a4")
# print(F_stre_rmvmr$plot[[2]])
# dev.off()
# } else {
# print(F_stre_rmvmr$plot[[1]])
# dev.copy2pdf(file = "rmvmr_strength_plot_rcdw.pdf", paper = "a4r",out.type = "pdf")
# dev.off()
# print(F_stre_rmvmr$plot[[2]])
# dev.copy2pdf(file = "rmvmr_strength_plot_mcv.pdf", paper = "a4r",out.type = "pdf")
# dev.off()
# }
#
# ivw_rmvmr_result<-ivw_rmvmr(r_input = rmvmr_dat, summary = TRUE)
# save(ivw_rmvmr_result,file=paste0('model_action',action,'_ivw_rmvmr_result.RData'))
# plot_rmvmr_result<-plot_rmvmr(mvmr_dat_input,ivw_rmvmr_result)
# if (server=='hpc'|server=='featurize'){
# pdf(file = "rmvmr_ivw_plot_wo_correction.pdf", paper = "a4")
# print(plot_rmvmr_result$p1)
# dev.off()
# pdf(file = "rmvmr_ivw_plot_correction.pdf", paper = "a4")
# print(plot_rmvmr_result$p2)
# dev.off()
# } else {
# print(plot_rmvmr_result$p1)
# dev.copy2pdf(file = "rmvmr_ivw_plot_wo_correction.pdf", paper = "a4r",out.type = "pdf")
# dev.off()
# print(plot_rmvmr_result$p2)
# dev.copy2pdf(file = "rmvmr_ivw_plot_correction.pdf", paper = "a4r",out.type = "pdf")
# dev.off()
# }
# pleiotropy_rmvmr_result<- pleiotropy_rmvmr(mvmr_dat_input,ivw_rmvmr_result)
#####other model
####ivw fe
mvmr_ivw_fe = mr_mvivw(mvmr_dat_input,model='fixed')
save(mvmr_ivw_fe,file=paste0('model_action',action,'_MRpackage_ivw_fe.RData'))
####ivw re
mvmr_ivw_re = mr_mvivw(mvmr_dat_input,model='random')
save( mvmr_ivw_re,file=paste0('model_action',action,'_MRpackage_ivw_re.RData'))
### median
set.seed(20200706)
mvmr_MR_median<-mr_mvmedian(mvmr_dat_input,
seed = 3
)
save(mvmr_MR_median,file=paste0('model_action',action,'_MRpackage_median.RData'))
# egger
mvmr_egger_AD = mr_mvegger(mvmr_dat_input)
save(mvmr_egger_AD,file=paste0('model_action',action,'_egger.RData'))
# robust
mvmr_robust_AD = mvmr_robust(bx_matrix, rawdat_mvmr[,paste0(out_abbr,'_b')],
rawdat_mvmr[,paste0(out_abbr,'_se')], k.max = 1000, maxit.scale = 1000)
save(mvmr_robust_AD,file=paste0('model_action',action,'_robust.RData'))
dat = data.frame("by" = rawdat_mvmr[,paste0(out_abbr,'_b')], "seby" =rawdat_mvmr[,paste0(out_abbr,'_se')],
"bx" = bx_matrix, "sebx" = sebx_matrix)
# median
set.seed(20200706)
mvmr_median_AD = mvmr_median(bx_matrix, sebx_matrix, rawdat_mvmr[,paste0(out_abbr,'_b')],
rawdat_mvmr[,paste0(out_abbr,'_se')], boot = TRUE, boot_it = 1000)
save(mvmr_median_AD,file=paste0('model_action',action,'_median.RData'))
#自制模型 lasso
set.seed(20200707)
mvmr_lasso_AD = mvmr_lasso(bx_matrix,rawdat_mvmr[,paste0(out_abbr,'_b')],
rawdat_mvmr[,paste0(out_abbr,'_se')])
save(mvmr_lasso_AD,file=paste0('model_action',action,'_lasso.RData'))
# go on to build other models
print(rep('#',20))
print(paste0('The ',i,' after har SNP No.:',nrow(rawdat_mvmr)))
F.data<-format_mvmr(BXGs=dplyr::select(rawdat_mvmr,ends_with('_b'),-paste0(out_abbr,'_b')),
BYG=rawdat_mvmr[,paste0(out_abbr,'_b')],
seBXGs=dplyr::select(rawdat_mvmr,ends_with('_se'),-paste0(out_abbr,'_se')),
seBYG=rawdat_mvmr[,paste0(out_abbr,'_se')],
RSID=rawdat_mvmr$SNP)
save(F.data,file=paste0('action',action,'_har','.RData'))
head(F.data)
if (gencov_cal){
covv<-read.table(corfile,header=F)
covv<-as.matrix(covv)
phecov<-phenocov_mvmr(covv, F.data[,c('sebetaX1' , 'sebetaX2', 'sebetaX3')])
print(rep('#',10))
print('F')
F_stre<-strength_mvmr(r_input=F.data,gencov=phecov)
print(rep('#',10))
# print('F by minimisation of Q-statistics')
# F_qhe<-strhet_mvmr(r_input=F.data,gencov=phecov)
print(rep('#',10))
print('Pleiotropy')
plei<-pleiotropy_mvmr(r_input=F.data,gencov=phecov)
print(rep('#',10))
print('ivw mvmr')
mvmr_ivw<-ivw_mvmr(r_input=F.data,gencov = covv)
save(mvmr_ivw,file=paste0('merge_exp_action',action,'_mvmr_ivw.RData'))
print(rep('#',10))
}else{
print(rep('#',10))
print('F')
F_stre<-strength_mvmr(r_input=F.data,gencov=0)
print(rep('#',10))
# print('F by minimisation of Q-statistics')
# F_qhe<-strhet_mvmr(r_input=F.data,gencov=phecov)
print(rep('#',10))
print('Pleiotropy')
plei<-pleiotropy_mvmr(r_input=F.data,gencov=0)
print(rep('#',10))
print('ivw mvmr')
mvmr_ivw<-ivw_mvmr(r_input=F.data,gencov = 0)
save(mvmr_ivw,file=paste0('merge_exp_action',action,'_mvmr_ivw.RData'))
print(rep('#',10))
}
vars<-gsub('_b','',names(dplyr::select(rawdat_mvmr,ends_with('_b'))))
F_stats=list('F_stre'=F_stre,'plei'=plei,'vars'=vars)
# F_stats=list('F_stre'=F_stre,'F_qhe'=F_qhe,'plei'=plei,'F_stre_rmvmr'=F_stre_rmvmr,'pleiotropy_rmvmr_result'=pleiotropy_rmvmr_result)
save(F_stats,file=paste0('action',action,'_F_stats.RData'))
print(rep('#',10))
print('TSMR mvmr')
#pdf(file = "mv_mutiple.pdf")
res_mv_mutiple <- mv_multiple(mvdat,plots = TRUE)
res_mv_mutiple_intecept<-mv_multiple(
mvdat,
intercept = TRUE,
plots = TRUE
)
#print(res_mv_mutiple)
# dev.off()
save(res_mv_mutiple ,file=paste0('merge_exp_action',action,'_res_mv_mutiple.RData'))
save(res_mv_mutiple_intecept ,file=paste0('merge_exp_action',action,'_res_mv_mutiple_intecept.RData'))
if (server=='hpc'|server=='featurize'){
pdf(file = "mv_mutiple1.pdf", paper = "a4")
print(res_mv_mutiple$plots[[1]])
dev.off()
pdf(file = "mv_mutiple2.pdf", paper = "a4")
print(res_mv_mutiple$plots[[2]])
dev.off()
pdf(file = "mv_mutiple_intecept1.pdf", paper = "a4")
print(res_mv_mutiple_intecept$plots[[1]])
dev.off()
pdf(file = "mv_mutiple_intecept2.pdf", paper = "a4")
print(res_mv_mutiple_intecept$plots[[2]])
dev.off()
} else {
print(res_mv_mutiple$plots[[1]])
dev.copy2pdf(file = "mv_mutiple1.pdf", paper = "a4r",out.type = "pdf")
dev.off()
print(res_mv_mutiple$plots[[2]])
dev.copy2pdf(file = "mv_multiple2.pdf", paper = "a4r",out.type = "pdf")
dev.off()
print(res_mv_mutiple_intecept$plots[[1]])
dev.copy2pdf(file = "mv_mutiple_intecept1.pdf", paper = "a4r",out.type = "pdf")
dev.off()
print(res_mv_mutiple_intecept$plots[[2]])
dev.copy2pdf(file = "mv_multiple_intecept2.pdf", paper = "a4r",out.type = "pdf")
dev.off()
}
print(rep('#',10))
print('presso')
data_presso<-rawdat_mvmr
rownames(data_presso)<-NULL
set.seed(20200705)
file_presso<-paste0('merge_exp_action',action,'_multi_presso_nb_5000.RData')
if(file.exists(file_presso)){
load(file_presso)
}else{
presso<-mr_presso(BetaOutcome =paste0(out_abbr,'_b'), BetaExposure = names(dplyr::select(rawdat_mvmr,ends_with('_b'),-paste0(out_abbr,'_b'))), SdOutcome =paste0(out_abbr,'_se'),
SdExposure = names(dplyr::select(rawdat_mvmr,ends_with('_se'),-paste0(out_abbr,'_se'))), OUTLIERtest = TRUE,DISTORTIONtest = TRUE,
data =data_presso, NbDistribution = 5000, SignifThreshold = 0.05)
save(presso,file=file_presso)
}
print(presso)
if (!develop){
sink()
sink(type="message")
cat(readLines(re_log), sep="\n")
}
}
}
if (b=='b2'){
for (j in 1:nrow(t$med_list)){
id_exposure<-c(t$exp_id,t$med_list$id[j])
path_name<-paste(c(t$exp_list$abbr,t$med_list$abbr[j]),collapse='_')
print(id_exposure)
print('\n')
exposure_dat <- mv_extract_exposures(id_exposure,harmonise_strictness =3,clump_r2 = clump_r2)
nrow(distinct(exposure_dat,SNP, .keep_all = TRUE))==nrow(exposure_dat)
exposure_dat$exposure<-clean_name_fun(exposure_dat$exposure,style=style)
mr_fun(t,id_exposure,exposure_dat)
}
}else if((b=='b0')|(b=='b1')){
id_exposure<-t$exp_id
print(id_exposure)
print('\n')
exposure_dat <- mv_extract_exposures(id_exposure,harmonise_strictness =3,clump_r2 = clump_r2)
nrow(distinct(exposure_dat,SNP, .keep_all = TRUE))==nrow(exposure_dat)
exposure_dat$exposure<-clean_name_fun(exposure_dat$exposure,style=style)
path_name<-paste(t$exp_list$abbr,collapse='_')
mr_fun(t,id_exposure,exposure_dat)
}
###################################################################################################
####################################################################################################################
print(paste0('time spent:',' ',proc.time()-ptm))
print('Done!!!!')