• Baseline Setting 50% Shared Causal SNP
    • Feature of 95% credible set
    • PIP of signal in at least one ancestry
    • PIP of shared signal
    • PIP of ancestry-specific signal

Last updated: 2023-10-09

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File Version Author Date Message
Rmd 62ce4b3 borangao 2023-10-09 Update my analysis

Baseline Setting 50% Shared Causal SNP

Feature of 95% credible set

library(ggplot2)
library(ggrepel)
library(grid)
library(egg)
library(dplyr)
library(forcats)
library(gridExtra)
library(patchwork)
library(ggpattern)
library(data.table)
library(ggpubr)
source("/net/fantasia/home/borang/Susie_Mult/Revision_Round_1/Simulation/091223/code/Function/utility.R")
###################
#
#Set Size & Power
#
###################
load("/net/fantasia/home/borang/Susie_Mult/Revision_Round_1/Simulation/091223/res_summary/shared_50_baseline.RData")   
upper_limit<-round(all_Set_data_dataframe%>%filter(Method == "Paintor",causal_num=="Num~Causal  == 5 ",h2=="~h^2 == 10^-4")%>%summarise(upper = quantile(Size,0.75))%>%pull(upper))+50
p_size_box<-Set_Size_fun(all_Set_data_dataframe%>%mutate(Size = log2(Size+1)),upper_limit = log2(upper_limit))
p_size_box<-p_size_box+ ylab("log2(Set Size + 1)")

p_power_bar<-Set_Power_fun(set_power_summary)
size_power<-p_size_box/p_power_bar+plot_annotation(tag_levels = 'a')& 
  theme(plot.tag = element_text(size = 7,face="bold"))
size_power

PIP of signal in at least one ancestry

##########################################################
#
#             Either ancestry 
#  PR curve | FDR Power | Calibration
#
##########################################################

      ###################
      #
      #ROC
      #
      ###################
      either_all_ROC_data_dataframe<-either_all_ROC_data_dataframe%>%mutate(Method = fct_relevel(Method,"MESuSiE","SuSiE","Paintor"))
      p_ROC_Either<-ROC_shared_fun(either_all_ROC_data_dataframe)
       ###################
      #
      #FDR&Power
      #
      ###################
      power_upper_limit<-FDR_Power_either%>%filter(FDR!=0.5)%>%ungroup(Method,h2,causal_num)%>%summarise(upper_limit = min(ceiling(max(Power)*10)/10+0.1,1))%>%pull(upper_limit)
      p_FDR_Power_Either<-FDR_Power_shared_fun(FDR_Power_either%>%filter(FDR!=0.5))+ylim(0,power_upper_limit)
      ####################
      #
      #PIP calibration
      #
      ####################
      PIP_calibration_either_byh2<-create_obs_frq_byh2(data_all%>%select(Signal,h2,MESuSiE_Either,SuSiE_Either, Paintor_PIP),c(1,2,3),c("MESuSiE_Either","SuSiE_Either","Paintor_PIP"))
      PIP_calibration_either_byh2<- PIP_calibration_either_byh2%>%mutate(Method = fct_recode(Method, "MESuSiE" = "MESuSiE_Either","SuSiE" = "SuSiE_Either","Paintor" = "Paintor_PIP"))%>%mutate(Method = fct_relevel(Method,"MESuSiE","SuSiE","Paintor"))
      
      p_calibration_byh2<-PIP_calibration_shared_byh2_fun(PIP_calibration_either_byh2)
      
      ROC_FDR_Power_Calibration_Either_Plot<-ggarrange(p_ROC_Either,p_FDR_Power_Either,p_calibration_byh2,nrow = 3,ncol=1,
                                                       common.legend = TRUE, legend="bottom",labels = c("a","b","c"),font.label=list(color="black",size=7))
      
ROC_FDR_Power_Calibration_Either_Plot

PIP of shared signal

##########################################################
#
#             Shared Signal
#  PR curve | FDR Power | Calibration
#
##########################################################
      ###################
      #
      #ROC
      #
      ###################
      shared_all_ROC_data_dataframe<-shared_all_ROC_data_dataframe%>%mutate(Method = fct_relevel(Method,"MESuSiE","SuSiE","Paintor"))
      p_ROC_shared<-ROC_shared_fun(shared_all_ROC_data_dataframe)
      ###################
      #
      #FDR&Power
      #
      ###################
       
      power_upper_limit<-FDR_Power_shared%>%filter(FDR!=0.5)%>%ungroup(Method,h2,causal_num)%>%summarise(upper_limit = min(ceiling(max(Power)*10)/10+0.1,1))%>%pull(upper_limit)
      p_FDR_Power_shared<-FDR_Power_shared_fun(FDR_Power_shared%>%filter(FDR!=0.5))+ylim(0,power_upper_limit)
      
       ####################
      #
      #PIP calibration
      #
      ####################
      
      PIP_calibration_shared_byh2<-create_obs_frq_byh2(data_all%>%select(Signal,h2,MESuSiE_Shared,SuSiE_Shared, Paintor_PIP),c(3),c("MESuSiE_Shared","SuSiE_Shared","Paintor_PIP"))
      PIP_calibration_shared_byh2<- PIP_calibration_shared_byh2%>%mutate(Method = fct_recode(Method, "MESuSiE" = "MESuSiE_Shared","SuSiE" = "SuSiE_Shared","Paintor" = "Paintor_PIP"))%>%mutate(Method = fct_relevel(Method,"MESuSiE","SuSiE","Paintor"))
      
      p_calibration_shared_byh2<-PIP_calibration_shared_byh2_fun(PIP_calibration_shared_byh2)
      
      ROC_FDR_Power_Calibration_shared_Plot<-ggarrange(p_ROC_shared,p_FDR_Power_shared,p_calibration_shared_byh2,nrow = 3,ncol=1,common.legend = TRUE, legend="bottom",labels = c("a","b","c"),font.label=list(color="black",size=7))
    ROC_FDR_Power_Calibration_shared_Plot

PIP of ancestry-specific signal

##########################################################
#
# Ancestry-specific Signal 
#  ROC | FDR Power | PIP calibration  
#
##########################################################
      ###################
      #
      #ROC
      #
      ###################
     
      ancestry_all_ROC_data_dataframe <- ancestry_all_ROC_data_dataframe %>% 
        mutate(Method = as.character(Method))
      split_list <- strsplit(ancestry_all_ROC_data_dataframe %>% pull(Method), " +")
      ancestry_all_ROC_data_dataframe <- ancestry_all_ROC_data_dataframe %>%mutate(
          Method = sapply(split_list, `[`, 1),
          Ancestry = sapply(split_list, `[`, 2)
        )%>%mutate(Method = fct_relevel(Method,"MESuSiE","SuSiE","Paintor"),Ancestry = fct_relevel(Ancestry, "WB","BB"))%>%mutate(Ancestry = fct_recode(Ancestry, "White British" = "WB"   , "Black British" = "BB" ))
      p_ROC_ancestry<-ROC_ancestry_fun(ancestry_all_ROC_data_dataframe)
  
      ###################
      #
      #FDR&Power
      #
      ###################
      FDR_Power_ancestry <- FDR_Power_ancestry %>% mutate(Method = as.character(Method))
      split_list <- strsplit(FDR_Power_ancestry %>% pull(Method), " +")
      FDR_Power_ancestry <- FDR_Power_ancestry%>%ungroup(h2,causal_num,Method) %>%mutate(
        Method = sapply(split_list, `[`, 1),
        Ancestry = sapply(split_list, `[`, 2)
      )%>%mutate(Method = fct_relevel(Method,"MESuSiE","SuSiE","Paintor"),Ancestry = fct_relevel(Ancestry, "WB","BB"))%>%mutate(Ancestry = fct_recode(Ancestry, "White British" = "WB"   , "Black British" = "BB" ))
      
      power_upper_limit<-FDR_Power_ancestry%>%filter(FDR!=0.5)%>%summarise(upper_limit = min(ceiling(max(Power)*10)/10+0.1,1))%>%pull(upper_limit)
      
       p_FDR_Power_ancestry<-FDR_Power_ancestry_fun(FDR_Power_ancestry%>%filter(FDR!=0.5))+ylim(0, power_upper_limit)
       ROC_FDR_Power_ancestry<- (p_ROC_ancestry / p_FDR_Power_ancestry) +plot_annotation(tag_levels = 'a')&theme(plot.tag = element_text(size = 7, face = "bold"))
       ROC_FDR_Power_ancestry<-ROC_FDR_Power_ancestry+ plot_layout(heights = c(1, 1))
        ROC_FDR_Power_ancestry

      ####################
      #
      #PIP calibration
      #
      ###################   
         
       PIP_calibration_ancestry<- PIP_calibration_ancestry%>%group_by(causal_num)%>%mutate(Method = fct_recode(Method, "MESuSiE White British" = "MESuSiE~WB", "MESuSiE Black British" = "MESuSiE~BB", "Paintor White British" = "Paintor~WB","Paintor Black British" = "Paintor~BB"))
       levels(PIP_calibration_ancestry$Method)<-c(paste0("MESuSiE~","White~","British"),paste0("MESuSiE~","Black~","British"),paste0("Paintor~","White~","British"),paste0("Paintor~","Black~","British"))
       p_calibration_ancestry<-PIP_calibration_ancestry_fun(PIP_calibration_ancestry)

       p_calibration_ancestry


sessionInfo()
R version 4.3.1 (2023-06-16)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 20.04.6 LTS

Matrix products: default
BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 
LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/liblapack.so.3;  LAPACK version 3.9.0

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

time zone: America/New_York
tzcode source: system (glibc)

attached base packages:
[1] grid      stats     graphics  grDevices utils     datasets  methods  
[8] base     

other attached packages:
 [1] ggpubr_0.6.0      data.table_1.14.8 ggpattern_0.4.3-3 patchwork_1.1.1  
 [5] forcats_1.0.0     dplyr_1.1.2       egg_0.4.5         gridExtra_2.3    
 [9] ggrepel_0.9.1     ggplot2_3.4.2     workflowr_1.7.0  

loaded via a namespace (and not attached):
 [1] gtable_0.3.1        xfun_0.39           bslib_0.5.0        
 [4] processx_3.8.0      rstatix_0.7.2       gridpattern_0.5.4-1
 [7] callr_3.7.3         vctrs_0.6.2         tools_4.3.1        
[10] ps_1.7.2            generics_0.1.3      proxy_0.4-27       
[13] tibble_3.2.1        fansi_1.0.5         highr_0.10         
[16] pkgconfig_2.0.3     KernSmooth_2.23-21  lifecycle_1.0.3    
[19] compiler_4.3.1      farver_2.1.1        stringr_1.5.0      
[22] git2r_0.32.0        munsell_0.5.0       getPass_0.2-2      
[25] carData_3.0-5       httpuv_1.6.11       class_7.3-20       
[28] htmltools_0.5.5     sass_0.4.6          yaml_2.3.7         
[31] later_1.3.1         pillar_1.9.0        car_3.1-2          
[34] jquerylib_0.1.4     whisker_0.4.1       tidyr_1.3.0        
[37] classInt_0.4-9      cachem_1.0.8        abind_1.4-5        
[40] tidyselect_1.2.0    digest_0.6.30       stringi_1.7.12     
[43] sf_1.0-13           purrr_1.0.1         labeling_0.4.2     
[46] cowplot_1.1.1       rprojroot_2.0.3     fastmap_1.1.1      
[49] colorspace_2.1-0    cli_3.6.1           magrittr_2.0.3     
[52] utf8_1.2.3          e1071_1.7-13        broom_1.0.5        
[55] withr_2.5.1         scales_1.2.1        promises_1.2.0.1   
[58] backports_1.4.1     rmarkdown_2.22      httr_1.4.6         
[61] ggsignif_0.6.4      memoise_2.0.1       evaluate_0.18      
[64] knitr_1.39          rlang_1.1.1         Rcpp_1.0.11        
[67] DBI_1.1.3           glue_1.6.2          rstudioapi_0.14    
[70] jsonlite_1.8.3      R6_2.5.1            units_0.8-2        
[73] fs_1.6.2