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Determine_optimal_polygenic_scoring_approach_GenoPredPipe.Rmd
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Determine_optimal_polygenic_scoring_approach_GenoPredPipe.Rmd
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---
title: "Replicating previous polygenic score methods comparison using GenoPred pipeline (GenoPredPipe)"
output:
html_document:
toc: true
theme: united
toc_depth: 3
number_sections: true
toc_float: true
fig_caption: yes
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
<style>
p.caption {
font-size: 1.5em;
}
</style>
```{css, echo=F}
pre code, pre, code {
white-space: pre !important;
overflow-x: scroll !important;
word-break: keep-all !important;
word-wrap: initial !important;
}
```
***
This page replicates the results of our [previous study](https://doi.org/10.1371/journal.pgen.1009021) comparing polygenic scoring methods, but using the [GenoPredPipe](https://github.com/opain/GenoPred/tree/master/GenoPredPipe) implementation of the GenoPred scripts. We only replicate the results in the UK Biobank sample. There are only minor differences in GenoPredPipe implementation compared to the original polygenic score methods comparison study. These include:
* The output of all polygenic scoring methods are stored in a standardised .score file format
* PLINK2 is used to calculate polygenic scores in target samples, improving the computational efficiency
* The 1KG Phase 3 data is used for all methods, except where methods developers provide an LD-reference to be used with their software (e.g. SBayesR, LDpred2, PRScs)
As expected, the results are highly concordant to the original results. See below for further details.
***
# Methods
***
## GenoPredPipe
The imputed genotype data from UKB was processed using [GenoPredPipe](https://github.com/opain/GenoPred/tree/master/GenoPredPipe), a snakemake pipeline implementing all the required GenoPred scripts. In summary these include:
* Harmonisation with the the 1KG Phase 3 reference restrcited to HapMap3 variants
* Ancestry classification into super populations
* Implementation of polygenic scoring methods across GWAS sumstats
* Calculation of polygenic scores in UKB
This entire process was run using a single line of code:
```{bash, eval=F, echo=T}
snakemake --profile slurm --use-conda run_target_prs_all
```
***
## Prepare phenotype data
<details><summary>Update IDs in phenotype files to match GenoPredPipe output</summary>
```{r, echo=T, eval=F}
pheno<-c('Depression', 'Intelligence', 'BMI', 'Height', 'T2D', 'CAD', 'IBD', 'MultiScler', 'RheuArth', 'Breast_Cancer', 'Prostate_Cancer')
library(data.table)
project_specific_ids<-fread('/scratch/datasets/ukbiobank/ukb18177/imputed/wukb18177_imp_chr1_v3_s487296.sample')
project_specific_ids<-project_specific_ids[-1,]
project_specific_ids$row_id<-1:nrow(project_specific_ids)
project_specific_ids<-project_specific_ids[,c('ID_1','row_id'),with=F]
for(pheno_i in pheno){
pheno_dat<-fread(paste0('/users/k1806347/brc_scratch/Data/UKBB/Phenotype/PRS_comp_subset/UKBB.',pheno_i,'.txt'))
pheno_col<-names(pheno_dat)[3]
pheno_dat<-merge(pheno_dat, project_specific_ids, by.x='IID', by.y='ID_1')
pheno_dat<-pheno_dat[,c('row_id','row_id',pheno_col), with=F]
names(pheno_dat)<-c('FID','IID',pheno_col)
fwrite(pheno_dat, paste0('/users/k1806347/brc_scratch/Data/UKBB/Phenotype/PRS_comp_subset/UKBB.',pheno_i,'.rowID.txt'), sep=' ', na='NA')
}
```
</details>
***
## Estimating predictive ability
Models containing a single predictor were derived using generalised linear model (GLM). Models containing multiple predictors were derived using elastic-net regularisation to reduce the likelihood of overfitting and account for multicollinearity when modelling highly correlated predictors. 10-fold cross validation was performed using 80% of individuals to identify optimal parameters, with subsequent test-set validation in the remaining 20% of individuals to estimate the predictive utility among individuals not included in the parameter selection process.
Model building and evaluation was performed using an Rscript called Model_builder_V2.R (more information [here](https://github.com/opain/GenoPred/tree/master/Scripts/Model_builder)).
<details><summary>Show code</summary>
```{R, echo=T, eval=F}
pheno<-c('Depression','Intelligence','BMI','Height','T2D','CAD','IBD','MultiScler','RheuArth','Breast_Cancer','Prostate_Cancer')
gwas<-c('DEPR06','COLL01','BODY04','HEIG03','DIAB05','COAD01','CROH01','SCLE03','RHEU02','BRCA01','PRCA01')
for(i in 1:length(pheno)){
pred_file<-NULL
# pT+clump (sparse)
pred_file<-rbind(pred_file,data.frame( predictors=paste0('/scratch/groups/ukbiobank/usr/ollie_pain/GenoPredPipe/output/ukb/prs/EUR/pt_clump/',gwas[i],'/ukb.',gwas[i],'.EUR.profiles'),
group='pt_clump'))
# lassosum
pred_file<-rbind(pred_file,data.frame( predictors=paste0('/scratch/groups/ukbiobank/usr/ollie_pain/GenoPredPipe/output/ukb/prs/EUR/lassosum/',gwas[i],'/ukb.',gwas[i],'.EUR.profiles'),
group='lassosum'))
# PRScs
pred_file<-rbind(pred_file,data.frame( predictors=paste0('/scratch/groups/ukbiobank/usr/ollie_pain/GenoPredPipe/output/ukb/prs/EUR/prscs/',gwas[i],'/ukb.',gwas[i],'.EUR.profiles'),
group='prscs'))
# SBayesR
pred_file<-rbind(pred_file,data.frame( predictors=paste0('/scratch/groups/ukbiobank/usr/ollie_pain/GenoPredPipe/output/ukb/prs/EUR/sbayesr/',gwas[i],'/ukb.',gwas[i],'.EUR.profiles'),
group='sbayesr'))
# DBSLMM
pred_file<-rbind(pred_file,data.frame( predictors=paste0('/scratch/groups/ukbiobank/usr/ollie_pain/GenoPredPipe/output/ukb/prs/EUR/dbslmm/',gwas[i],'/ukb.',gwas[i],'.EUR.profiles'),
group='dbslmm'))
# LDPred2
pred_file<-rbind(pred_file,data.frame( predictors=paste0('/scratch/groups/ukbiobank/usr/ollie_pain/GenoPredPipe/output/ukb/prs/EUR/ldpred2/',gwas[i],'/ukb.',gwas[i],'.EUR.profiles'),
group='ldpred2'))
# Write out list of predictors with groups
dir.create(paste0('/scratch/groups/ukbiobank/usr/ollie_pain/GenoPredPipe/Validation/',pheno[i]))
write.table(pred_file, paste0('/scratch/groups/ukbiobank/usr/ollie_pain/GenoPredPipe/Validation/',pheno[i],'/UKBB.w_hm3.',gwas[i],'.EUR-PRSs.AllMethodComp.predictor_groups'), row.names=F, col.names=T, quote=F)
}
```
```{bash, eval=F, echo=T}
# Run Model_builder_V2.R
pheno=$(echo Depression Intelligence BMI Height T2D CAD IBD MultiScler RheuArth Breast_Cancer Prostate_Cancer)
gwas=$(echo DEPR06 COLL01 BODY04 HEIG03 DIAB05 COAD01 CROH01 SCLE03 RHEU02 BRCA01 PRCA01)
prev=$(echo 0.15 NA NA NA 0.05 0.03 0.013 0.00164 0.005 0.125 0.125)
# 1KG reference
for i in $(seq 1 11);do
pheno_i=$(echo ${pheno} | cut -f ${i} -d ' ')
gwas_i=$(echo ${gwas} | cut -f ${i} -d ' ')
prev_i=$(echo ${prev} | cut -f ${i} -d ' ')
sbatch --mem 30G -n 3 -p brc,shared /users/k1806347/brc_scratch/Software/Rscript.sh /users/k1806347/brc_scratch/Software/MyGit/GenoPred/Scripts/Model_builder/Model_builder_V2.R \
--pheno /users/k1806347/brc_scratch/Data/UKBB/Phenotype/PRS_comp_subset/UKBB.${pheno_i}.rowID.txt \
--out /scratch/groups/ukbiobank/usr/ollie_pain/GenoPredPipe/Validation/${pheno_i}/UKBB.w_hm3.${gwas_i}.EUR-PRSs.AllMethodComp \
--n_core 3 \
--compare_predictors T \
--assoc T \
--outcome_pop_prev ${prev_i} \
--predictors /scratch/groups/ukbiobank/usr/ollie_pain/GenoPredPipe/Validation/${pheno_i}/UKBB.w_hm3.${gwas_i}.EUR-PRSs.AllMethodComp.predictor_groups
done
```
</details>
***
# Results
<details><summary>Summarise and plot the results</summary>
```{R, echo=T, eval=F}
meta_res_eval<-NULL
meta_res_comp<-NULL
meta_res_eval_plots_cross<-list()
meta_res_eval_plots_indep<-list()
meta_res_eval_diff_plots_cross<-list()
meta_res_eval_diff_plots_indep<-list()
meta_res_eval_diff_matrix_cross<-list()
meta_res_eval_diff_matrix_indep<-list()
pheno<-c('Depression','Intelligence','Height','BMI','T2D','CAD','IBD', 'MultiScler','RheuArth','Prostate_Cancer','Breast_Cancer')
gwas<-c('DEPR06','COLL01','HEIG03','BODY04','DIAB05','COAD01','CROH01','SCLE03','RHEU02','PRCA01','BRCA01')
##################
# Organise, make tables and figures for per phenotype results
##################
res_eval<-NULL
res_comp<-NULL
res_eval_plots_cross<-list()
res_eval_plots_indep<-list()
res_eval_diff_plots_cross<-list()
res_eval_diff_plots_indep<-list()
res_eval_diff_matrix_cross<-list()
res_eval_diff_matrix_indep<-list()
for(i in 1:length(pheno)){
res_eval_i<-read.table(paste0('/scratch/groups/ukbiobank/usr/ollie_pain/GenoPredPipe/Validation/',pheno[i],'/UKBB.w_hm3.',gwas[i],'.EUR-PRSs.AllMethodComp.pred_eval.txt'), header=T, stringsAsFactors=F)
res_comp_i<-read.table(paste0('/scratch/groups/ukbiobank/usr/ollie_pain/GenoPredPipe/Validation/',pheno[i],'/UKBB.w_hm3.',gwas[i],'.EUR-PRSs.AllMethodComp.pred_comp.txt'), header=T, stringsAsFactors=F)
# Idenitfy model names for 10FCVal, multi-PRS and pseudoval
selected_models<-NULL
##
# pT+clump (sparse)
##
# Identify best pT in 10FCVal
res_eval_i_per_pT<-res_eval_i[grepl('pt.clump.PredFile', res_eval_i$Model),]
selected_models<-rbind(selected_models,data.frame(Test='pT+clump.10FCVal', Label=
res_eval_i_per_pT[res_eval_i_per_pT$CrossVal_R == max(res_eval_i_per_pT$CrossVal_R),]$Model[1]))
# Identify multi pT
selected_models<-rbind(selected_models,data.frame(Test='pT+clump.MultiPRS', Label='pt.clump'))
##
# lassosum
##
# Identify best pT in 10FCVal
res_eval_i_per_lassosum<-res_eval_i[grepl('lassosum.PredFile', res_eval_i$Model),]
selected_models<-rbind(selected_models,data.frame(Test='lassosum.10FCVal', Label=
res_eval_i_per_lassosum[res_eval_i_per_lassosum$CrossVal_R == max(res_eval_i_per_lassosum$CrossVal_R),]$Model[1]))
# Identify multi pT
selected_models<-rbind(selected_models,data.frame(Test='lassosum.MultiPRS', Label='lassosum'))
# Identify pseudoVal
lassosum_val<-read.table(paste0('/scratch/groups/ukbiobank/usr/ollie_pain/GenoPredPipe/GenoPred/GenoPredPipe/resources/data/1kg/prs_score_files/lassosum/',gwas[i],'/1KGPhase3.w_hm3.',gwas[i],'.log'), sep='&')
lassosum_val_s<-as.numeric(gsub('s = ','',lassosum_val$V1[grepl('s = ',lassosum_val$V1)]))
lassosum_val_lambda<-as.numeric(gsub('lambda =','',lassosum_val$V1[grepl('lambda =',lassosum_val$V1)]))
lassosum_val_param<-paste0('s',lassosum_val_s,'.lambda',lassosum_val_lambda)
lassosum_val_param<-substr(lassosum_val_param, 1, nchar(lassosum_val_param)-1)
selected_models<-rbind(selected_models,data.frame(Test='lassosum.PseudoVal', Label=res_eval_i[grepl(lassosum_val_param, res_eval_i$Model),]$Model[1]))
##
# PRScs
##
# Identify best pT in 10FCVal
res_eval_i_per_PRScs<-res_eval_i[grepl('prscs.PredFile', res_eval_i$Model),]
res_eval_i_per_PRScs<-res_eval_i_per_PRScs[!grepl('phiauto', res_eval_i_per_PRScs$Model),]
selected_models<-rbind(selected_models,data.frame(Test='PRScs.10FCVal', Label=res_eval_i_per_PRScs[res_eval_i_per_PRScs$CrossVal_R == max(res_eval_i_per_PRScs$CrossVal_R),]$Model[1]))
# Identify multi pT
selected_models<-rbind(selected_models,data.frame(Test='PRScs.MultiPRS', Label='prscs'))
# Identify pseudoval
res_eval_i_per_PRScs<-res_eval_i[grepl('prscs.PredFile', res_eval_i$Model),]
res_eval_i_per_PRScs<-res_eval_i_per_PRScs[grepl('phi.auto', res_eval_i_per_PRScs$Model),]
selected_models<-rbind(selected_models,data.frame(Test='PRScs.PseudoVal', Label=res_eval_i_per_PRScs$Model[1]))
##
# SBayesR
##
# Identify PseudoVal
selected_models<-rbind(selected_models,data.frame(Test='SBayesR.PseudoVal', Label='sbayesr'))
##
# LDPred2
##
# Identify best pT in 10FCVal
res_eval_i_per_LDPred2<-res_eval_i[grepl('ldpred2.PredFile', res_eval_i$Model),]
res_eval_i_per_LDPred2<-res_eval_i_per_LDPred2[!grepl('beta.inf|beta.auto', res_eval_i_per_LDPred2$Model),]
selected_models<-rbind(selected_models,data.frame(Test='LDpred2.10FCVal', Label=res_eval_i_per_LDPred2[res_eval_i_per_LDPred2$CrossVal_R == max(res_eval_i_per_LDPred2$CrossVal_R),]$Model[1]))
# Identify multi pT
selected_models<-rbind(selected_models,data.frame(Test='LDpred2.MultiPRS', Label='ldpred2'))
# Identify pseudoval
res_eval_i_per_LDPred2<-res_eval_i[grepl('ldpred2.PredFile', res_eval_i$Model),]
res_eval_i_per_LDPred2<-res_eval_i_per_LDPred2[grepl('beta.inf', res_eval_i_per_LDPred2$Model),]
selected_models<-rbind(selected_models,data.frame(Test='LDpred2.Inf', Label=res_eval_i_per_LDPred2$Model))
res_eval_i_per_LDPred2<-res_eval_i[grepl('ldpred2.PredFile', res_eval_i$Model),]
res_eval_i_per_LDPred2<-res_eval_i_per_LDPred2[grepl('beta.auto', res_eval_i_per_LDPred2$Model),]
selected_models<-rbind(selected_models,data.frame(Test='LDpred2.PseudoVal', Label=res_eval_i_per_LDPred2$Model))
##
# DBSLMM
##
# Identify PseudoVal
selected_models<-rbind(selected_models,data.frame(Test='DBSLMM.PseudoVal', Label='dbslmm'))
##
# All methods
##
selected_models<-rbind(selected_models,data.frame(Test='All.MultiPRS', Label='All'))
###
# Subset selected models and format
###
# Eval
selected_models$Label_2<-paste0(gsub('_group','',selected_models$Label),'_group')
res_eval_i_select<-res_eval_i[(res_eval_i$Model %in% selected_models$Label_2),]
res_eval_i_select<-merge(res_eval_i_select, selected_models[c('Test','Label_2')], by.x='Model',by.y='Label_2')
res_eval_i_select$Phenotype<-pheno[i]
if(sum(names(res_eval_i_select) == 'CrossVal_OR') == 0){
res_eval_i_select$CrossVal_OR<-NA
res_eval_i_select$CrossVal_LowCI<-NA
res_eval_i_select$CrossVal_HighCI<-NA
res_eval_i_select$Cross_LiabR2<-NA
res_eval_i_select$Cross_AUC<-NA
res_eval_i_select$CrossVal_Ncas<-NA
res_eval_i_select$CrossVal_Ncon<-NA
res_eval_i_select$IndepVal_OR<-NA
res_eval_i_select$IndepVal_LowCI<-NA
res_eval_i_select$IndepVal_HighCI<-NA
res_eval_i_select$Indep_LiabR2<-NA
res_eval_i_select$Indep_AUC<-NA
res_eval_i_select$IndepVal_Ncas<-NA
res_eval_i_select$IndepVal_Ncon<-NA
}
res_eval_i_select<-res_eval_i_select[c('Phenotype','Test',"CrossVal_R","CrossVal_R_SE",'CrossVal_OR','CrossVal_LowCI','CrossVal_HighCI','Cross_LiabR2','Cross_AUC','CrossVal_pval','CrossVal_N','CrossVal_Ncas','CrossVal_Ncon',"IndepVal_R","IndepVal_R_SE",'IndepVal_OR','IndepVal_LowCI','IndepVal_HighCI','Indep_LiabR2','Indep_AUC','IndepVal_pval','IndepVal_N','IndepVal_Ncas','IndepVal_Ncon')]
res_eval<-rbind(res_eval,res_eval_i_select)
# Comp
selected_models$Label_3<-gsub('_group','',selected_models$Label)
res_comp_i_select<-res_comp_i[(res_comp_i$Model_1 %in% selected_models$Label_3) & (res_comp_i$Model_2 %in% selected_models$Label_3),]
res_comp_i_select<-merge(res_comp_i_select, selected_models[c('Test','Label_3')], by.x='Model_1',by.y='Label_3')
names(res_comp_i_select)[names(res_comp_i_select) == 'Test']<-'Model_1_Test'
res_comp_i_select<-merge(res_comp_i_select, selected_models[c('Test','Label_3')], by.x='Model_2',by.y='Label_3')
names(res_comp_i_select)[names(res_comp_i_select) == 'Test']<-'Model_2_Test'
res_comp_i_select$Label<-NULL
res_comp_i_select$Phenotype<-pheno[i]
res_comp_i_select<-res_comp_i_select[c('Phenotype','Model_1_Test','Model_2_Test','Model_1_Cross_R','Model_2_Cross_R','Cross_R_diff','Cross_R_diff_pval','Model_1_Indep_R','Model_2_Indep_R','Indep_R_diff','Indep_R_diff_pval')]
res_comp<-rbind(res_comp,res_comp_i_select)
###
# Create plot showing performance of each method
###
library(ggplot2)
library(cowplot)
res_eval_i_select$Method<-gsub('\\..*','',res_eval_i_select$Test)
res_eval_i_select$Model<-gsub('.*\\.','',res_eval_i_select$Test)
res_eval_i_select$Method<-factor(res_eval_i_select$Method, levels=c('pT+clump','lassosum','PRScs','SBayesR','LDpred2','DBSLMM','All'))
res_eval_i_select$Model<-factor(res_eval_i_select$Model, levels=c('10FCVal','MultiPRS','PseudoVal','Inf'))
res_eval_plots_cross[[pheno[i]]] <- ggplot( res_eval_i_select, aes(x=Method, y=CrossVal_R, fill=Model)) +
geom_bar(stat="identity", position=position_dodge(preserve = "single"), width = 0.7) +
geom_errorbar(aes(ymin=CrossVal_R-CrossVal_R_SE, ymax=CrossVal_R+CrossVal_R_SE), width=.2, position=position_dodge(width = 0.7, preserve = "single")) +
labs(y="Correlation (SE)", x='', title=pheno[i]) +
coord_cartesian(ylim=c(min(res_eval_i_select$CrossVal_R[res_eval_i_select$Phenotype==pheno[i]])-0.02, max(res_eval_i_select$CrossVal_R[res_eval_i_select$Phenotype==pheno[i]])+0.025), clip="on") +
theme_half_open() +
theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
background_grid(major = 'y', minor = 'y') +
theme(legend.position="top", legend.title = element_blank(), legend.box="vertical", legend.margin=margin()) +
guides(fill=guide_legend(nrow=2))
res_eval_plots_indep[[pheno[i]]] <- ggplot( res_eval_i_select, aes(x=Method, y=IndepVal_R, fill=Model)) +
geom_bar(stat="identity", position=position_dodge(preserve = "single"), width = 0.7) +
geom_errorbar(aes(ymin=IndepVal_R-IndepVal_R_SE, ymax=IndepVal_R+IndepVal_R_SE), width=.2, position=position_dodge(width = 0.7, preserve = "single")) +
labs(y="Correlation (SE)", x='', title=pheno[i]) +
coord_cartesian(ylim=c(min(res_eval_i_select$IndepVal_R[res_eval_i_select$Phenotype==pheno[i]])-0.02, max(res_eval_i_select$IndepVal_R[res_eval_i_select$Phenotype==pheno[i]])+0.025), clip="on") +
theme_half_open() +
theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
background_grid(major = 'y', minor = 'y') +
theme(legend.position="top", legend.title = element_blank(), legend.box="vertical", legend.margin=margin()) +
guides(fill=guide_legend(nrow=2))
###
# Create plot showing performance of each method compared to pT+clump
###
res_eval_i_select$CrossVal_R_diff<-res_eval_i_select$CrossVal_R-res_eval_i_select$CrossVal_R[res_eval_i_select$Test == "pT+clump.10FCVal"]
res_eval_diff_plots_cross[[pheno[i]]] <- ggplot( res_eval_i_select, aes(x=Method, y=CrossVal_R_diff, fill=Model)) +
geom_bar(stat="identity", position=position_dodge(preserve = "single"), width = 0.7) +
geom_errorbar(aes(ymin=CrossVal_R_diff-CrossVal_R_SE, ymax=CrossVal_R_diff+CrossVal_R_SE), width=.2, position=position_dodge(width = 0.7, preserve = "single")) +
labs(y="Correlation (SE)", x='', title=pheno[i]) +
coord_cartesian(ylim=c(min(res_eval_i_select$CrossVal_R_diff[res_eval_i_select$Phenotype==pheno[i]])-0.02, max(res_eval_i_select$CrossVal_R_diff[res_eval_i_select$Phenotype==pheno[i]])+0.025), clip="on") +
theme_half_open() +
theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
background_grid(major = 'y', minor = 'y') +
theme(legend.position="top", legend.title = element_blank(), legend.box="vertical", legend.margin=margin()) +
guides(fill=guide_legend(nrow=2))
res_eval_i_select$IndepVal_R_diff<-res_eval_i_select$IndepVal_R-res_eval_i_select$IndepVal_R[res_eval_i_select$Test == "pT+clump.10FCVal"]
res_eval_diff_plots_indep[[pheno[i]]] <- ggplot( res_eval_i_select, aes(x=Method, y=IndepVal_R_diff, fill=Model)) +
geom_bar(stat="identity", position=position_dodge(preserve = "single"), width = 0.7) +
geom_errorbar(aes(ymin=IndepVal_R_diff-IndepVal_R_SE, ymax=IndepVal_R_diff+IndepVal_R_SE), width=.2, position=position_dodge(width = 0.7, preserve = "single")) +
labs(y="Correlation (SE)", x='', title=pheno[i]) +
coord_cartesian(ylim=c(min(res_eval_i_select$IndepVal_R_diff[res_eval_i_select$Phenotype==pheno[i]])-0.02, max(res_eval_i_select$IndepVal_R_diff[res_eval_i_select$Phenotype==pheno[i]])+0.025), clip="on") +
theme_half_open() +
theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
background_grid(major = 'y', minor = 'y') +
theme(legend.position="top", legend.title = element_blank(), legend.box="vertical", legend.margin=margin()) +
guides(fill=guide_legend(nrow=2))
###
# Create plot showing matrix of differences with significance
###
library(reshape2)
res_comp_i_select$Cross_R_diff[res_comp_i_select$Model_1_Test == res_comp_i_select$Model_2_Test]<-NA
res_comp_i_select$Indep_R_diff[res_comp_i_select$Model_1_Test == res_comp_i_select$Model_2_Test]<-NA
res_comp_i_select$Cross_R_diff_catagory<-'NA'
res_comp_i_select$Cross_R_diff_catagory[res_comp_i_select$Cross_R_diff < -0.002]<-'-0.025 - -0.002'
res_comp_i_select$Cross_R_diff_catagory[res_comp_i_select$Cross_R_diff < -0.025]<-'-0.08 - -0.025'
res_comp_i_select$Cross_R_diff_catagory[res_comp_i_select$Cross_R_diff < -0.08]<-'< -0.08'
res_comp_i_select$Cross_R_diff_catagory[res_comp_i_select$Cross_R_diff > -0.002 & res_comp_i_select$Cross_R_diff < 0.002]<-'-0.002 - 0.002'
res_comp_i_select$Cross_R_diff_catagory[res_comp_i_select$Cross_R_diff > 0.002]<-'0.002 - 0.025'
res_comp_i_select$Cross_R_diff_catagory[res_comp_i_select$Cross_R_diff > 0.025]<-'0.025 - 0.08'
res_comp_i_select$Cross_R_diff_catagory[res_comp_i_select$Cross_R_diff > 0.08]<-'> 0.08'
res_comp_i_select$Cross_R_diff_catagory<-factor(res_comp_i_select$Cross_R_diff_catagory, level=rev(c('< -0.08','-0.08 - -0.025','-0.025 - -0.002','-0.002 - 0.002','0.002 - 0.025','0.025 - 0.08','> 0.08')))
res_comp_i_select$Indep_R_diff_catagory<-'NA'
res_comp_i_select$Indep_R_diff_catagory[res_comp_i_select$Indep_R_diff < -0.002]<-'-0.025 - -0.002'
res_comp_i_select$Indep_R_diff_catagory[res_comp_i_select$Indep_R_diff < -0.025]<-'-0.08 - -0.025'
res_comp_i_select$Indep_R_diff_catagory[res_comp_i_select$Indep_R_diff < -0.08]<-'< -0.08'
res_comp_i_select$Indep_R_diff_catagory[res_comp_i_select$Indep_R_diff > -0.002 & res_comp_i_select$Indep_R_diff < 0.002]<-'-0.002 - 0.002'
res_comp_i_select$Indep_R_diff_catagory[res_comp_i_select$Indep_R_diff > 0.002]<-'0.002 - 0.025'
res_comp_i_select$Indep_R_diff_catagory[res_comp_i_select$Indep_R_diff > 0.025]<-'0.025 - 0.08'
res_comp_i_select$Indep_R_diff_catagory[res_comp_i_select$Indep_R_diff > 0.08]<-'> 0.08'
res_comp_i_select$Indep_R_diff_catagory<-factor(res_comp_i_select$Indep_R_diff_catagory, level=rev(c('< -0.08','-0.08 - -0.025','-0.025 - -0.002','-0.002 - 0.002','0.002 - 0.025','0.025 - 0.08','> 0.08')))
res_comp_i_select$cross_star<-' '
res_comp_i_select$cross_star[res_comp_i_select$Cross_R_diff_pval < 0.05]<-'*'
res_comp_i_select$cross_star[res_comp_i_select$Cross_R_diff_pval < 1e-3]<-'**'
res_comp_i_select$cross_star[res_comp_i_select$Cross_R_diff_pval < 1e-6]<-'***'
res_comp_i_select$indep_star<-' '
res_comp_i_select$indep_star[res_comp_i_select$Indep_R_diff_pval < 0.05]<-'*'
res_comp_i_select$indep_star[res_comp_i_select$Indep_R_diff_pval < 1e-3]<-'**'
res_comp_i_select$indep_star[res_comp_i_select$Indep_R_diff_pval < 1e-6]<-'***'
library(RColorBrewer)
res_eval_diff_matrix_cross[[pheno[i]]]<-ggplot(data = res_comp_i_select, aes(Model_2_Test, Model_1_Test, fill=Cross_R_diff_catagory))+
geom_tile(color = "white")+
labs(y='Test', x='Comparison', title=pheno[i], fill='R difference') +
theme_minimal()+
theme(axis.text.x = element_text(angle = 45, vjust = 1,
size = 8, hjust = 1))+
coord_fixed() +
geom_text(data=res_comp_i_select, aes(Model_2_Test, Model_1_Test, label = cross_star), color = "black", size = 4, angle = 0, vjust=0.8) +
scale_fill_brewer(breaks = levels(res_comp_i_select$Indep_R_diff_catagory), palette = "RdBu", drop=F, na.value='grey')
res_eval_diff_matrix_indep[[pheno[i]]]<-ggplot(data = res_comp_i_select, aes(Model_2_Test, Model_1_Test, fill=Indep_R_diff_catagory))+
geom_tile(color = "white")+
labs(y='Test', x='Comparison', title=pheno[i], fill='R difference') +
theme_minimal()+
theme(axis.text.x = element_text(angle = 45, vjust = 1,
size = 8, hjust = 1))+
coord_fixed() +
geom_text(data=res_comp_i_select, aes(Model_2_Test, Model_1_Test, label = indep_star), color = "black", size = 4, angle = 0, vjust=0.8) +
scale_fill_brewer(breaks = levels(res_comp_i_select$Indep_R_diff_catagory), palette = "RdBu", drop=F, na.value='grey')
}
write.csv(res_eval, '/scratch/groups/ukbiobank/usr/ollie_pain/GenoPredPipe/Validation/PRS_method_comp_GenoPredPipe.csv', row.names=F, quote=F)
write.csv(res_comp, '/scratch/groups/ukbiobank/usr/ollie_pain/GenoPredPipe/Validation/PRS_method_comp_GenoPredPipe_diff.csv', row.names=F, quote=F)
png('/scratch/groups/ukbiobank/usr/ollie_pain/GenoPredPipe/Validation/PRS_method_comp_CrossVal_GenoPredPipe.png', units='px', res=300, width=2450, height=1000*ceiling(length(res_eval_plots_cross)/2))
print(plot_grid(plotlist=res_eval_plots_cross, ncol = 2))
dev.off()
png('/scratch/groups/ukbiobank/usr/ollie_pain/GenoPredPipe/Validation/PRS_method_comp_IndepVal_GenoPredPipe.png', units='px', res=300, width=2450, height=1000*ceiling(length(res_eval_plots_cross)/2))
print(plot_grid(plotlist=res_eval_plots_indep, ncol = 2))
dev.off()
png('/scratch/groups/ukbiobank/usr/ollie_pain/GenoPredPipe/Validation/PRS_method_comp_pTDiff_CrossVal_GenoPredPipe.png', units='px', res=300, width=2450, height=1000*ceiling(length(res_eval_plots_cross)/2))
print(plot_grid(plotlist=res_eval_diff_plots_cross, ncol = 2))
dev.off()
png('/scratch/groups/ukbiobank/usr/ollie_pain/GenoPredPipe/Validation/PRS_method_comp_pTDiff_IndepVal_GenoPredPipe.png', units='px', res=300, width=2450, height=1000*ceiling(length(res_eval_plots_cross)/2))
print(plot_grid(plotlist=res_eval_diff_plots_indep, ncol = 2))
dev.off()
png('/scratch/groups/ukbiobank/usr/ollie_pain/GenoPredPipe/Validation/PRS_method_comp_GenoPredPipe_diff_CrossVal.png', units='px', res=300, width=2450, height=1750*length(res_eval_diff_matrix_cross))
print(plot_grid(plotlist=res_eval_diff_matrix_cross, ncol = 1))
dev.off()
png('/scratch/groups/ukbiobank/usr/ollie_pain/GenoPredPipe/Validation/PRS_method_comp_GenoPredPipe_diff_IndepVal.png', units='px', res=300, width=2450, height=1750*length(res_eval_diff_matrix_cross))
print(plot_grid(plotlist=res_eval_diff_matrix_indep, ncol = 1))
dev.off()
###################
# Average results across phenotypes, creates tables and figures
###################
res_eval$Method<-gsub('\\..*','',res_eval$Test)
res_eval$Model<-gsub('.*\\.','',res_eval$Test)
res_eval$Phenotype<-factor(res_eval$Phenotype, level=unique(res_eval$Phenotype))
res_eval$Method<-factor(res_eval$Method, level=c("pT+clump",'lassosum','PRScs','SBayesR','LDpred2','DBSLMM','All'))
res_eval$Model<-factor(res_eval$Model, level=c('10FCVal','MultiPRS','PseudoVal','Inf'))
res_eval<-res_eval[order(res_eval$Phenotype, res_eval$Method, res_eval$Model),]
library(data.table)
for(pheno_i in 1:length(pheno)){
if(pheno_i==1){
pheno_i_dat<-fread(paste0('/users/k1806347/brc_scratch/Data/UKBB/Phenotype/PRS_comp_subset/UKBB.',pheno[pheno_i],'.rowID.txt'))
names(pheno_i_dat)[3]<-pheno[pheno_i]
pheno_dat<-pheno_i_dat
} else {
pheno_i_dat<-fread(paste0('/users/k1806347/brc_scratch/Data/UKBB/Phenotype/PRS_comp_subset/UKBB.',pheno[pheno_i],'.rowID.txt'))
names(pheno_i_dat)[3]<-pheno[pheno_i]
pheno_dat<-merge(pheno_dat, pheno_i_dat, by=c('FID','IID'), all=T)
}
}
# Calculate corelation between all phenotypes
cors<-abs(cor(as.matrix(pheno_dat[,-1:-2]), use='p'))
cors<-cors[unique(res_eval$Phenotype,),unique(res_eval$Phenotype,)]
cors[is.na(cors)]<-0
# Run aggregate function
library(MAd)
meta_res_eval<-NULL
for(i in unique(res_eval$Test)){
res_eval_i<-res_eval[res_eval$Test == i,]
res_eval_i$Sample<-'Target'
cors_i<-cors[(dimnames(cors)[[1]] %in% res_eval_i$Phenotype),(dimnames(cors)[[1]] %in% res_eval_i$Phenotype)]
CrossVal_agg_res_eval_i<-agg(id=Sample, es=CrossVal_R, var=CrossVal_R_SE^2, cor=cors_i, method="BHHR", mod=NULL, data=res_eval_i)
IndepVal_agg_res_eval_i<-agg(id=Sample, es=IndepVal_R, var=IndepVal_R_SE^2, cor=cors_i, method="BHHR", mod=NULL, data=res_eval_i)
meta_res_eval<-rbind(meta_res_eval, data.frame( Test=i,
CrossVal_R=CrossVal_agg_res_eval_i$es[1],
CrossVal_R_SE=sqrt(CrossVal_agg_res_eval_i$var[1]),
IndepVal_R=IndepVal_agg_res_eval_i$es[1],
IndepVal_R_SE=sqrt(IndepVal_agg_res_eval_i$var[1])))
}
# Calculate difference between methods and p-value using aggregate
# Truncate difference p-values to stop 0 values
res_comp$Cross_R_diff_pval[res_comp$Cross_R_diff_pval == 0]<-1e-320
res_comp$Indep_R_diff_pval[res_comp$Indep_R_diff_pval == 0]<-1e-320
meta_res_diff<-NULL
for(i in unique(res_eval$Test)){
for(k in unique(res_eval$Test)){
meta_res_eval_i<-meta_res_eval[meta_res_eval$Test == i,]
meta_res_eval_k<-meta_res_eval[meta_res_eval$Test == k,]
res_comp_i_k<-res_comp[res_comp$Model_1_Test == i & res_comp$Model_2_Test == k,]
# Calculate diff SE based on p-value
for(j in 1:dim(res_comp_i_k)[1]){
if(res_comp_i_k$Cross_R_diff_pval[j] == 1){
res_comp_i_k$Cross_R_diff_pval[j]<-1-0.001
}
if(res_comp_i_k$Indep_R_diff_pval[j] == 1){
res_comp_i_k$Indep_R_diff_pval[j]<-1-0.001
}
}
res_comp_i_k$Cross_R_diff_z<-qnorm(res_comp_i_k$Cross_R_diff_pval/2)
res_comp_i_k$Cross_R_diff_SE<-res_comp_i_k$Cross_R_diff/res_comp_i_k$Cross_R_diff_z
res_comp_i_k$Indep_R_diff_z<-qnorm(res_comp_i_k$Indep_R_diff_pval/2)
res_comp_i_k$Indep_R_diff_SE<-res_comp_i_k$Indep_R_diff/res_comp_i_k$Indep_R_diff_z
res_comp_i_k$Sample<-'A'
cors_i<-cors[(dimnames(cors)[[1]] %in% res_comp_i_k$Phenotype),(dimnames(cors)[[1]] %in% res_comp_i_k$Phenotype)]
CrossVal_agg_res_comp_i_k<-agg(id=Sample, es=Cross_R_diff, var=Cross_R_diff_SE^2, cor=cors_i, method="BHHR", mod=NULL, data=res_comp_i_k)
IndepVal_agg_res_eval_i<-agg(id=Sample, es=Indep_R_diff, var=Indep_R_diff_SE^2, cor=cors_i, method="BHHR", mod=NULL, data=res_comp_i_k)
meta_res_diff<-rbind(meta_res_diff, data.frame(Test_ref=i,
Test_targ=k,
CrossVal_R_diff=CrossVal_agg_res_comp_i_k$es[1],
CrossVal_R_diff_SE=sqrt(CrossVal_agg_res_comp_i_k$var[1]),
IndepVal_R_diff=IndepVal_agg_res_eval_i$es[1],
IndepVal_R_diff_SE=sqrt(IndepVal_agg_res_eval_i$var[1])))
}
}
meta_res_diff$CrossVal_R_diff_Z<-meta_res_diff$CrossVal_R_diff/meta_res_diff$CrossVal_R_diff_SE
meta_res_diff$CrossVal_R_diff_P<-2*pnorm(-abs(meta_res_diff$CrossVal_R_diff_Z))
meta_res_diff$IndepVal_R_diff_Z<-meta_res_diff$IndepVal_R_diff/meta_res_diff$IndepVal_R_diff_SE
meta_res_diff$IndepVal_R_diff_P<-2*pnorm(-abs(meta_res_diff$IndepVal_R_diff_Z))
meta_res_diff<-meta_res_diff[,c("Test_ref","Test_targ","CrossVal_R_diff","CrossVal_R_diff_Z","CrossVal_R_diff_P","IndepVal_R_diff","IndepVal_R_diff_Z","IndepVal_R_diff_P")]
meta_res_diff$CrossVal_R_diff_Z[meta_res_diff$CrossVal_R_diff == 0]<-0
meta_res_diff$CrossVal_R_diff_P[meta_res_diff$CrossVal_R_diff == 0]<-1
meta_res_diff$IndepVal_R_diff_Z[meta_res_diff$IndepVal_R_diff == 0]<-0
meta_res_diff$IndepVal_R_diff_P[meta_res_diff$IndepVal_R_diff == 0]<-1
# Calculate percentage improvement
meta_res_diff$CrossVal_R_diff_perc<-NA
for(i in unique(meta_res_diff$Test_ref)){
meta_res_diff$CrossVal_R_diff_perc[meta_res_diff$Test_ref == i]<-meta_res_diff$CrossVal_R_diff[meta_res_diff$Test_ref == i]/meta_res_eval$CrossVal_R[meta_res_eval$Test == i]*100
}
meta_res_diff$IndepVal_R_diff_perc<-NA
for(i in unique(meta_res_diff$Test_ref)){
meta_res_diff$IndepVal_R_diff_perc[meta_res_diff$Test_ref == i]<-meta_res_diff$IndepVal_R_diff[meta_res_diff$Test_ref == i]/meta_res_eval$IndepVal_R[meta_res_eval$Test == i]*100
}
write.csv(meta_res_eval, '/scratch/groups/ukbiobank/usr/ollie_pain/GenoPredPipe/Validation/PRS_method_comp_GenoPredPipe_meta.csv', row.names=F, quote=F)
meta_res_diff<-meta_res_diff[,c("Test_ref","Test_targ","CrossVal_R_diff",'CrossVal_R_diff_perc',"CrossVal_R_diff_Z","CrossVal_R_diff_P","IndepVal_R_diff",'IndepVal_R_diff_perc',"IndepVal_R_diff_Z","IndepVal_R_diff_P")]
write.csv(meta_res_diff, '/scratch/groups/ukbiobank/usr/ollie_pain/GenoPredPipe/Validation/PRS_method_comp_GenoPredPipe_diff_meta.csv', row.names=F, quote=F)
###
# Create plot showing performance of each method
###
# To avoid unequal groups, insert NA for missing Model/Method pairs
meta_res_eval$Method<-gsub('\\..*','',meta_res_eval$Test)
meta_res_eval$Model<-gsub('.*\\.','',meta_res_eval$Test)
meta_res_eval$Method<-factor(meta_res_eval$Method, levels=c("pT+clump",'lassosum','PRScs','SBayesR','LDpred2','DBSLMM','All'))
meta_res_eval$Model<-factor(meta_res_eval$Model, levels=c('10FCVal','MultiPRS','PseudoVal','Inf'))
for(i in unique(meta_res_eval$Method)){
for(k in unique(meta_res_eval$Model)){
if(dim(meta_res_eval[meta_res_eval$Method == i & meta_res_eval$Model == k,])[1] > 0){
next
} else {
dud<-data.frame(Test=NA,
CrossVal_R=NA,
CrossVal_R_SE=NA,
IndepVal_R=NA,
IndepVal_R_SE=NA,
Method=i,
Model=k)
meta_res_eval<-rbind(meta_res_eval,dud)
}
}
}
res_eval<-res_eval[,c('Phenotype','Test','CrossVal_R','IndepVal_R','Method','Model')]
for(i in unique(res_eval$Method)){
for(k in unique(res_eval$Model)){
if(dim(res_eval[res_eval$Method == i & res_eval$Model == k,])[1] > 0){
next
} else {
dud<-data.frame(Phenotype=NA,
Test=paste0(i,'.',k),
CrossVal_R=NA,
IndepVal_R=NA,
Method=i,
Model=k)
res_eval<-rbind(res_eval,dud)
}
}
}
library(ggplot2)
library(cowplot)
meta_res_eval_plots_cross <- ggplot( meta_res_eval, aes(x=Method, y=CrossVal_R, fill=Model)) +
geom_bar(stat="identity", position=position_dodge(preserve = "single"), width = 0.7) +
geom_errorbar(aes(ymin=CrossVal_R-CrossVal_R_SE, ymax=CrossVal_R+CrossVal_R_SE), width=.2, position=position_dodge(width = 0.7, preserve = "single")) +
labs(y="Correlation (SE)", x='') +
theme_half_open() +
theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
geom_vline(xintercept = 1.5, linetype="dotted") +
geom_vline(xintercept = 2.5, linetype="dotted") +
geom_vline(xintercept = 3.5, linetype="dotted") +
geom_vline(xintercept = 4.5, linetype="dotted") +
geom_vline(xintercept = 5.5, linetype="dotted") +
geom_vline(xintercept = 6.5, linetype="dotted") +
geom_vline(xintercept = 7.5, linetype="dotted") +
geom_vline(xintercept = 8.5, linetype="dotted") +
background_grid(major = 'y', minor = 'y')
meta_res_eval_plots_indep <- ggplot( meta_res_eval, aes(x=Method, y=IndepVal_R, fill=Model)) +
geom_bar(stat="identity", position=position_dodge(preserve = "single"), width = 0.7) +
geom_errorbar(aes(ymin=IndepVal_R-IndepVal_R_SE, ymax=IndepVal_R+IndepVal_R_SE), width=.2, position=position_dodge(width = 0.7, preserve = "single")) +
labs(y="Correlation (SE)", x='') +
theme_half_open() +
theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
geom_vline(xintercept = 1.5, linetype="dotted") +
geom_vline(xintercept = 2.5, linetype="dotted") +
geom_vline(xintercept = 3.5, linetype="dotted") +
geom_vline(xintercept = 4.5, linetype="dotted") +
geom_vline(xintercept = 5.5, linetype="dotted") +
geom_vline(xintercept = 6.5, linetype="dotted") +
geom_vline(xintercept = 7.5, linetype="dotted") +
geom_vline(xintercept = 8.5, linetype="dotted") +
background_grid(major = 'y', minor = 'y')
meta_res_eval_plots_cross<-meta_res_eval_plots_cross + coord_cartesian(ylim=c(0,0.25), clip="on")
meta_res_eval_plots_indep<-meta_res_eval_plots_indep + coord_cartesian(ylim=c(0,0.25), clip="on")
###
# Create plot showing performance of each method compared to pT+clump
###
# Calculate differences at the average and phenotype specific level
meta_res_eval$CrossVal_R_diff<-meta_res_eval$CrossVal_R-meta_res_eval$CrossVal_R[which(meta_res_eval$Test == "pT+clump.10FCVal")]
meta_res_eval$IndepVal_R_diff<-meta_res_eval$IndepVal_R-meta_res_eval$IndepVal_R[which(meta_res_eval$Test == "pT+clump.10FCVal")]
res_eval$CrossVal_R_diff<-NA
res_eval$IndepVal_R_diff<-NA
for(i in pheno){
res_eval$CrossVal_R_diff[which(res_eval$Phenotype == i)]<-res_eval$CrossVal_R[which(res_eval$Phenotype == i)]-res_eval$CrossVal_R[which(res_eval$Test == "pT+clump.10FCVal" & res_eval$Phenotype == i)]
res_eval$IndepVal_R_diff[which(res_eval$Phenotype == i)]<-res_eval$IndepVal_R[which(res_eval$Phenotype == i)]-res_eval$IndepVal_R[which(res_eval$Test == "pT+clump.10FCVal" & res_eval$Phenotype == i)]
}
meta_res_eval_diff_plots_cross <- ggplot( meta_res_eval, aes(x=Method, y=CrossVal_R_diff, fill=Model)) +
geom_point(data=res_eval, mapping=aes(x=Method, y=CrossVal_R_diff, colour=Model), position=position_jitterdodge(jitter.width = 0.4, dodge.width = 0.7), alpha=0.3) +
geom_errorbar(aes(ymin=CrossVal_R_diff-CrossVal_R_SE, ymax=CrossVal_R_diff+CrossVal_R_SE), width=0.9, position=position_dodge(width = 0.7)) +
geom_point(stat="identity", position=position_dodge(0.7), size=2, shape=23, colour='black') +
labs(y="Difference in\nCorrelations (SE)", x='') +
theme_half_open() +
theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
geom_vline(xintercept = 1.5, linetype="dotted") +
geom_vline(xintercept = 2.5, linetype="dotted") +
geom_vline(xintercept = 3.5, linetype="dotted") +
geom_vline(xintercept = 4.5, linetype="dotted") +
geom_vline(xintercept = 5.5, linetype="dotted") +
geom_vline(xintercept = 6.5, linetype="dotted") +
geom_vline(xintercept = 7.5, linetype="dotted") +
geom_vline(xintercept = 8.5, linetype="dotted") +
background_grid(major = 'y', minor = 'y') +
ylim(c(-0.08,0.12))
meta_res_eval_diff_plots_indep <- ggplot(meta_res_eval, aes(x=Method, y=IndepVal_R_diff, fill=Model)) +
geom_point(data=res_eval, mapping=aes(x=Method, y=IndepVal_R_diff, colour=Model), position=position_jitterdodge(jitter.width = 0.4, dodge.width = 1), alpha=0.3) +
geom_errorbar(aes(ymin=IndepVal_R_diff-IndepVal_R_SE, ymax=IndepVal_R_diff+IndepVal_R_SE), width=0.9, position=position_dodge(width = 1)) +
geom_point(stat="identity", position=position_dodge(1), size=2, shape=23, colour='black') +
labs(y="Difference in\nCorrelations (SE)", x='') +
theme_half_open() +
theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
geom_vline(xintercept = 1.5, linetype="dotted") +
geom_vline(xintercept = 2.5, linetype="dotted") +
geom_vline(xintercept = 3.5, linetype="dotted") +
geom_vline(xintercept = 4.5, linetype="dotted") +
geom_vline(xintercept = 5.5, linetype="dotted") +
geom_vline(xintercept = 6.5, linetype="dotted") +
geom_vline(xintercept = 7.5, linetype="dotted") +
geom_vline(xintercept = 8.5, linetype="dotted") +
background_grid(major = 'y', minor = 'y') +
ylim(c(-0.08,0.12))
###
# Create plot showing matrix of differences with significance
###
library(reshape2)
meta_res_diff$CrossVal_R_diff[meta_res_diff$Test_ref == meta_res_diff$Test_targ]<-NA
meta_res_diff$IndepVal_R_diff[meta_res_diff$Test_ref == meta_res_diff$Test_targ]<-NA
meta_res_diff$Cross_R_diff_catagory<-'NA'
meta_res_diff$Cross_R_diff_catagory[meta_res_diff$CrossVal_R_diff < -0.002]<-'-0.025 - -0.002'
meta_res_diff$Cross_R_diff_catagory[meta_res_diff$CrossVal_R_diff < -0.025]<-'-0.08 - -0.025'
meta_res_diff$Cross_R_diff_catagory[meta_res_diff$CrossVal_R_diff < -0.08]<-'< -0.08'
meta_res_diff$Cross_R_diff_catagory[meta_res_diff$CrossVal_R_diff > -0.002 & meta_res_diff$CrossVal_R_diff < 0.002]<-'-0.002 - 0.002'
meta_res_diff$Cross_R_diff_catagory[meta_res_diff$CrossVal_R_diff > 0.002]<-'0.002 - 0.025'
meta_res_diff$Cross_R_diff_catagory[meta_res_diff$CrossVal_R_diff > 0.025]<-'0.025 - 0.08'
meta_res_diff$Cross_R_diff_catagory[meta_res_diff$CrossVal_R_diff > 0.08]<-'> 0.08'
meta_res_diff$Cross_R_diff_catagory<-factor(meta_res_diff$Cross_R_diff_catagory, level=rev(c('< -0.08','-0.08 - -0.025','-0.025 - -0.002','-0.002 - 0.002','0.002 - 0.025','0.025 - 0.08','> 0.08')))
meta_res_diff$Indep_R_diff_catagory<-'NA'
meta_res_diff$Indep_R_diff_catagory[meta_res_diff$IndepVal_R_diff < -0.002]<-'-0.025 - -0.002'
meta_res_diff$Indep_R_diff_catagory[meta_res_diff$IndepVal_R_diff < -0.025]<-'-0.08 - -0.025'
meta_res_diff$Indep_R_diff_catagory[meta_res_diff$IndepVal_R_diff < -0.08]<-'< -0.08'
meta_res_diff$Indep_R_diff_catagory[meta_res_diff$IndepVal_R_diff > -0.002 & meta_res_diff$IndepVal_R_diff < 0.002]<-'-0.002 - 0.002'
meta_res_diff$Indep_R_diff_catagory[meta_res_diff$IndepVal_R_diff > 0.002]<-'0.002 - 0.025'
meta_res_diff$Indep_R_diff_catagory[meta_res_diff$IndepVal_R_diff > 0.025]<-'0.025 - 0.08'
meta_res_diff$Indep_R_diff_catagory[meta_res_diff$IndepVal_R_diff > 0.08]<-'> 0.08'
meta_res_diff$Indep_R_diff_catagory<-factor(meta_res_diff$Indep_R_diff_catagory, level=rev(c('< -0.08','-0.08 - -0.025','-0.025 - -0.002','-0.002 - 0.002','0.002 - 0.025','0.025 - 0.08','> 0.08')))
meta_res_diff$cross_star<-' '
meta_res_diff$cross_star[meta_res_diff$CrossVal_R_diff_P < 0.05]<-'*'
meta_res_diff$cross_star[meta_res_diff$CrossVal_R_diff_P < 1e-3]<-'**'
meta_res_diff$cross_star[meta_res_diff$CrossVal_R_diff_P < 1e-6]<-'***'
meta_res_diff$indep_star<-' '
meta_res_diff$indep_star[meta_res_diff$IndepVal_R_diff_P < 0.05]<-'*'
meta_res_diff$indep_star[meta_res_diff$IndepVal_R_diff_P < 1e-3]<-'**'
meta_res_diff$indep_star[meta_res_diff$IndepVal_R_diff_P < 1e-6]<-'***'
library(RColorBrewer)
meta_res_eval_diff_matrix_cross<-ggplot(data = meta_res_diff, aes(Test_targ, Test_ref, fill=Cross_R_diff_catagory))+
geom_tile(color = "white")+
labs(y='Test', x='Comparison', fill='R difference') +
theme_minimal()+
theme(axis.text.x = element_text(angle = 45, vjust = 1,
size = 8, hjust = 1)) +
coord_fixed() +
geom_text(data=meta_res_diff, aes(Test_ref, Test_targ, label = cross_star), color = "black", size = 4, angle = 0, vjust=0.8) +
scale_fill_brewer(breaks = levels(meta_res_diff$Cross_R_diff_catagory), palette = "RdBu", drop=F, na.value='grey')
meta_res_eval_diff_matrix_indep<-ggplot(data = meta_res_diff, aes(Test_targ, Test_ref, fill=Indep_R_diff_catagory))+
geom_tile(color = "white")+
labs(y='Test', x='Comparison', fill='R difference') +
theme_minimal()+
theme(axis.text.x = element_text(angle = 45, vjust = 1,
size = 8, hjust = 1))+
coord_fixed() +
geom_text(data=meta_res_diff, aes(Test_targ, Test_ref, label = indep_star), color = "black", size = 4, angle = 0, vjust=0.8) +
scale_fill_brewer(breaks = levels(meta_res_diff$Indep_R_diff_catagory), palette = "RdBu", drop=F, na.value='grey')
png('/scratch/groups/ukbiobank/usr/ollie_pain/GenoPredPipe/Validation/PRS_method_comp_CrossVal_meta_GenoPredPipe.png', units='px', res=300, width=1750, height=1000)
meta_res_eval_plots_cross
dev.off()
png('/scratch/groups/ukbiobank/usr/ollie_pain/GenoPredPipe/Validation/PRS_method_comp_IndepVal_meta_GenoPredPipe.png', units='px', res=300, width=1750, height=1000)
meta_res_eval_plots_indep
dev.off()
png('/scratch/groups/ukbiobank/usr/ollie_pain/GenoPredPipe/Validation/PRS_method_comp_pTDiff_CrossVal_meta_GenoPredPipe.png', units='px', res=300, width=1750, height=1000)
meta_res_eval_diff_plots_cross
dev.off()
png('/scratch/groups/ukbiobank/usr/ollie_pain/GenoPredPipe/Validation/PRS_method_comp_pTDiff_IndepVal_meta_GenoPredPipe.png', units='px', res=300, width=1750, height=1000)
meta_res_eval_diff_plots_indep
dev.off()
png('/scratch/groups/ukbiobank/usr/ollie_pain/GenoPredPipe/Validation/PRS_method_comp_diff_CrossVal_meta_GenoPredPipe.png', units='px', res=300, width=2500, height=1500)
meta_res_eval_diff_matrix_cross
dev.off()
png('/scratch/groups/ukbiobank/usr/ollie_pain/GenoPredPipe/Validation/PRS_method_comp_diff_IndepVal_meta_GenoPredPipe.png', units='px', res=300, width=2500, height=1500)
meta_res_eval_diff_matrix_indep
dev.off()
```
</details>
***
## Phenotype-specific results
```{bash, eval=T, echo=F}
mkdir -p /users/k1806347/brc_scratch/Software/MyGit/GenoPred/Images/Determine_optimal_polygenic_scoring_approach
cp /scratch/groups/ukbiobank/usr/ollie_pain/GenoPredPipe/Validation/PRS_method_comp_CrossVal_GenoPredPipe.png /users/k1806347/brc_scratch/Software/MyGit/GenoPred/Images/Determine_optimal_polygenic_scoring_approach/
cp /scratch/groups/ukbiobank/usr/ollie_pain/GenoPredPipe/Validation/PRS_method_comp_IndepVal_GenoPredPipe.png /users/k1806347/brc_scratch/Software/MyGit/GenoPred/Images/Determine_optimal_polygenic_scoring_approach/
cp /scratch/groups/ukbiobank/usr/ollie_pain/GenoPredPipe/Validation/PRS_method_comp_pTDiff_CrossVal_GenoPredPipe.png /users/k1806347/brc_scratch/Software/MyGit/GenoPred/Images/Determine_optimal_polygenic_scoring_approach/
cp /scratch/groups/ukbiobank/usr/ollie_pain/GenoPredPipe/Validation/PRS_method_comp_pTDiff_IndepVal_GenoPredPipe.png /users/k1806347/brc_scratch/Software/MyGit/GenoPred/Images/Determine_optimal_polygenic_scoring_approach/
cp /scratch/groups/ukbiobank/usr/ollie_pain/GenoPredPipe/Validation/PRS_method_comp_GenoPredPipe_diff_CrossVal.png /users/k1806347/brc_scratch/Software/MyGit/GenoPred/Images/Determine_optimal_polygenic_scoring_approach/
cp /scratch/groups/ukbiobank/usr/ollie_pain/GenoPredPipe/Validation/PRS_method_comp_GenoPredPipe_diff_IndepVal.png /users/k1806347/brc_scratch/Software/MyGit/GenoPred/Images/Determine_optimal_polygenic_scoring_approach/
cp /scratch/groups/ukbiobank/usr/ollie_pain/GenoPredPipe/Validation/PRS_method_comp_CrossVal_meta_GenoPredPipe.png /users/k1806347/brc_scratch/Software/MyGit/GenoPred/Images/Determine_optimal_polygenic_scoring_approach/
cp /scratch/groups/ukbiobank/usr/ollie_pain/GenoPredPipe/Validation/PRS_method_comp_IndepVal_meta_GenoPredPipe.png /users/k1806347/brc_scratch/Software/MyGit/GenoPred/Images/Determine_optimal_polygenic_scoring_approach/
cp /scratch/groups/ukbiobank/usr/ollie_pain/GenoPredPipe/Validation/PRS_method_comp_pTDiff_CrossVal_meta_GenoPredPipe.png /users/k1806347/brc_scratch/Software/MyGit/GenoPred/Images/Determine_optimal_polygenic_scoring_approach/
cp /scratch/groups/ukbiobank/usr/ollie_pain/GenoPredPipe/Validation/PRS_method_comp_pTDiff_IndepVal_meta_GenoPredPipe.png /users/k1806347/brc_scratch/Software/MyGit/GenoPred/Images/Determine_optimal_polygenic_scoring_approach/
cp /scratch/groups/ukbiobank/usr/ollie_pain/GenoPredPipe/Validation/PRS_method_comp_diff_CrossVal_meta_GenoPredPipe.png /users/k1806347/brc_scratch/Software/MyGit/GenoPred/Images/Determine_optimal_polygenic_scoring_approach/
cp /scratch/groups/ukbiobank/usr/ollie_pain/GenoPredPipe/Validation/PRS_method_comp_diff_IndepVal_meta_GenoPredPipe.png /users/k1806347/brc_scratch/Software/MyGit/GenoPred/Images/Determine_optimal_polygenic_scoring_approach/
```
<details><summary>Show cross-validation results</summary>
<center>
</details>
<details><summary>Show test-validation results</summary>
\center
</details>
<details><summary>Show UKBB results table</summary>
```{r, echo=F, eval=T, results='asis'}
res<-read.csv("/scratch/groups/ukbiobank/usr/ollie_pain/GenoPredPipe/Validation/PRS_method_comp_GenoPredPipe.csv")
res$CrossVal_pval<-format(res$CrossVal_pval, scientific = TRUE, digits = 3)
res$IndepVal_pval<-format(res$IndepVal_pval, scientific = TRUE, digits = 3)
res[,!(names(res) %in% c('Phenotype','Test','CrossVal_pval','IndepVal_pval'))]<-round(res[,!(names(res) %in% c('Phenotype','Test','CrossVal_pval','IndepVal_pval'))], 3)
res$Method<-gsub('\\..*','',res$Test)
res$Model<-gsub('.*\\.','',res$Test)
res<-res[,c("Phenotype","Method","Model","CrossVal_R","CrossVal_R_SE","CrossVal_OR","CrossVal_LowCI","CrossVal_HighCI","Cross_LiabR2","Cross_AUC","CrossVal_pval","IndepVal_R","IndepVal_R_SE","IndepVal_OR","IndepVal_LowCI","IndepVal_HighCI","Indep_LiabR2","Indep_AUC","IndepVal_pval")]
library(knitr)
kable(res, rownames = FALSE, caption='Correlation between PRS model predictions and observed values in UK Biobank')
```
</details>
<details><summary>Show cross-validation results compared to pT+clump</summary>
<center>
</details>
<details><summary>Show test-validation results compared to pT+clump</summary>
\center
</details>
<details><summary>Show difference results table</summary>
```{r, echo=F, eval=T, results='asis'}
res<-read.csv("/scratch/groups/ukbiobank/usr/ollie_pain/GenoPredPipe/Validation/PRS_method_comp_GenoPredPipe_diff.csv")
res$Cross_R_diff_pval<-format(res$Cross_R_diff_pval, scientific = TRUE, digits = 3)
res$Indep_R_diff_pval<-format(res$Indep_R_diff_pval, scientific = TRUE, digits = 3)
res[,!grepl("Phenotype|_Test|_diff_pval",names(res))]<-round(res[,!grepl("Phenotype|_Test|_diff_pval",names(res))], 3)
names(res)<-c("Phenotype","Model 1 Test","Model 2 Test","Model 1 CrossVal R","Model 2 CrossVal R","CrossVal R Diff","CrossVal R Diff P","Model 1 IndepVal R","Model 2 IndepVal R","IndepVal R Diff","IndepVal R Diff P")
library(knitr)
kable(res, rownames = FALSE, caption='Difference between methods in UKBB with 1KG reference')
```
</details>
<details><summary>Show cross-validation differences between methods</summary>
<center>
</details>
<details><summary>Show test-validation differences between methods</summary>
\center
</details>
***
## Average performance
<details><summary>Show cross-validation results</summary>
<center>
</details>
<details><summary>Show test-validation results</summary>
\center
</details>
***
## Average performance relative to pT+clump
<details><summary>Show cross-validation results</summary>
<center>
</details>
<details><summary>Show test-validation results</summary>
