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Introduction

Settings are:

  • THINS = [“0.1”,“0.5”,“1”]
  • GROUP_PRIOR_VAR_STRUCTURES = [“shared_all”, “shared_type”]
  • NUMBER_OF_SIGNALS = [“1”,“5”]
  • WITH_ST = [“with_ST”, “without_ST”]
library(ctwas)
library(ggplot2)
library(dplyr)
library(tidyr)
library(scales)

source("/project/xinhe/xsun/multi_group_ctwas/data/samplesize.R")

mapping_predictdb <- readRDS("/project2/xinhe/shared_data/multigroup_ctwas/weights/mapping_files/PredictDB_mapping.RDS")
mapping_munro <- readRDS("/project2/xinhe/shared_data/multigroup_ctwas/weights/mapping_files/Munro_mapping.RDS")
mapping_two <- rbind(mapping_predictdb,mapping_munro)

traits_silver <- c("T2D","LDL","BMI","RBC","IBD")
names(traits_silver) <- c("T2D-panukb","LDL-ukb-d-30780_irnt","BMI-panukb","RBC-panukb","IBD-ebi-a-GCST004131")

trait <- "LDL-ukb-d-30780_irnt"

folder_results <- "/project/xinhe/shengqian/ctwas_GWAS_analysis/snakemake_outputs/"

thins <- c("0.1", "0.5", "1")
var_strucs <- c("shared_all", "shared_type")
Ls <- c("5","1")
sts <- c("without_ST","with_ST")

# st <- "without_ST"
# var_struc  <- "shared_all"
# L  <- 5
# thin  <- 0.1

create_summary_plot_withTP <- function(df, columns_to_plot, x_var = "setting", x_order = NULL, title = NULL) {

  # Reshape data
  df_long <- df %>%
    pivot_longer(
      cols = all_of(columns_to_plot),
      names_to = "variable",
      values_to = "value"
    )
  
  # Convert to factor with specified order if x_order is provided
  if (!is.null(x_order)) {
    df_long <- df_long %>%
      mutate(across(all_of(x_var), ~factor(., levels = x_order)))
  }
  
  # Identify the max value for scaling
  max_main <- max(df_long$value[df_long$variable != "TP_rate"], na.rm = TRUE)
  max_tp_rate <- max(df_long$value[df_long$variable == "TP_rate"], na.rm = TRUE)
  
  # Rescale TP_rate
  df_long <- df_long %>%
    mutate(scaled_value = ifelse(variable == "TP_rate", 
                                 value * (max_main / max_tp_rate), value))
  
  # Create plot
  ggplot(df_long, aes(x = .data[[x_var]], y = scaled_value, color = variable, shape = variable)) +
    #geom_point(size = 3, position = position_jitter(width = 0.2)) +
    geom_point(size = 3) + 
    scale_y_continuous(
      name = "Count",
      sec.axis = sec_axis(~ . * (max_tp_rate / max_main), name = "TP Rate")
    ) +
    labs(x = "Settings", title = title) +
    theme_minimal() +
    theme(
      axis.text.x = element_text(angle = 45, hjust = 1, size = 10),
      legend.position = "right",
      legend.title = element_blank()
    ) +
    scale_color_brewer(palette = "Set1")
}


create_summary_plot <- function(df, columns_to_plot, x_var = "setting", x_order = NULL, title = NULL) {
  # Reshape data
  df_long <- df %>%
    pivot_longer(
      cols = all_of(columns_to_plot),
      names_to = "variable",
      values_to = "value"
    )
  
  # Convert to factor with specified order if x_order is provided
  if (!is.null(x_order)) {
    df_long <- df_long %>%
      mutate(across(all_of(x_var), ~factor(., levels = x_order)))
  }
  
  # Create plot
  ggplot(df_long, aes(x = .data[[x_var]], y = value, color = variable)) +
    #geom_point(size = 3, position = position_jitter(width = 0.2)) +
    geom_point(size = 3) + 
    labs(x = "Settings", y = "Count/Value", title = title) +
    theme_minimal() +
    theme(
      axis.text.x = element_text(angle = 45, hjust = 1, size = 10),
      legend.position = "right",
      legend.title = element_blank()
    ) +
    scale_color_brewer(palette = "Set1")
}

LDL-ukb-d-30780_irnt

trait <- "LDL-ukb-d-30780_irnt"

setting_names <- c()
prop_h2g_nonsnp_all <- c()
num_gene_pip08_all <- c()
num_silver_pip08_all <- c()
num_bystander_pip08_all <- c()
imputable_known_all <- c()
imputable_bystander_all <- c()
for (st in sts) {
  for (thin in thins) {
    for (var_struc in var_strucs) {
      for (L in Ls) {

        setting_names <- c(setting_names, paste0(st,"_thin",thin,"_",var_struc,"_L",L))
        
        # non-snp %h2g
        param <- readRDS(paste0(folder_results,"/",trait,"/",trait,".",st,".thin",thin,".",var_struc,".param.RDS"))
        
        ctwas_parameters <- summarize_param(param, samplesize[trait])
        prop_h2g <- ctwas_parameters$prop_heritability
        prop_h2g_nonsnp <- 1 - ctwas_parameters$prop_heritability["SNP"]
        prop_h2g_nonsnp_all <- c(prop_h2g_nonsnp_all,prop_h2g_nonsnp)
        
        # num_gene_pip08
        combined_pip_by_group <- readRDS(paste0(folder_results,"/",trait,"/",trait,".",st,".thin",thin,".",var_struc,".L",L, ".combined_pip_bygroup_final.RDS"))
        combined_pip_sig <- combined_pip_by_group[combined_pip_by_group$combined_pip > 0.8,]
        
        num_gene_pip08_all <- c(num_gene_pip08_all, nrow(combined_pip_sig))
        
        # silver_standard genes
        known <- readRDS(paste0("/project/xinhe/xsun/multi_group_ctwas/data/silverstandard/known_annotations_",traits_silver[trait],".RDS"))
        bystander <- readRDS(paste0("/project/xinhe/xsun/multi_group_ctwas/data/silverstandard/bystanders_",traits_silver[trait],".RDS"))

        num_silver_pip08_all <- c(num_silver_pip08_all,sum(combined_pip_sig$gene_name %in% known))
        num_bystander_pip08_all <- c(num_bystander_pip08_all,sum(combined_pip_sig$gene_name %in% bystander))
        
        # imputable genes
        z_gene <-  readRDS(paste0(folder_results,"/",trait,"/",trait,".",st,".z_gene.RDS"))
        z_gene <- z_gene %>%
                  mutate(molecular_id = sub("\\|.*", "", id)) %>%  # Extract ENSG ID from id
                  left_join(mapping_two %>% dplyr::select(molecular_id, gene_name), by = "molecular_id")
        
        imputable_known_all <- c(imputable_known_all,sum(unique(z_gene$gene_name) %in% known))
        imputable_bystander_all <- c(imputable_bystander_all,sum(unique(z_gene$gene_name) %in% bystander))
        
      }
    }
  }
}


df <- data.frame(setting = setting_names,prop_h2g_nonsnp = prop_h2g_nonsnp_all ,num_gene_pip08 = num_gene_pip08_all,
                 num_silver_total = rep(length(known),length(setting_names)), num_imputable_silver = imputable_known_all, num_silver_pip08 = num_silver_pip08_all,
                 num_bystander_total = rep(length(bystander),length(setting_names)), num_imputable_bystander = imputable_bystander_all, num_bystander_pip08 = num_bystander_pip08_all,
                 TP_rate = num_silver_pip08_all/(num_silver_pip08_all+num_bystander_pip08_all))

## plot Non-SNP %h2g
create_summary_plot(df,x_order = setting_names, 
                    columns_to_plot = c("prop_h2g_nonsnp"), title = "%h2g -- NON-SNP")

Version Author Date
4c80969 XSun 2025-02-24 
# 
# ## silver standard gene
# create_summary_plot(df,x_order = setting_names, 
#                     columns_to_plot = c("num_gene_pip08","num_imputable_silver","num_silver_pip08"),
#                     title = "Silver standard genes")
# 
# ## bystander genes
# create_summary_plot(df,x_order = setting_names, 
#                     columns_to_plot = c("num_gene_pip08","num_bystander_pip08"),
#                     title = "Bystander genes")
# 
# ## tp
# create_summary_plot(df,x_order = setting_names, 
#                     columns_to_plot = c("num_silver_pip08"),
#                     title = "True positive rate")

create_summary_plot_withTP(df,x_order = setting_names, 
                    columns_to_plot =  c("num_gene_pip08","num_silver_pip08","TP_rate","num_imputable_silver"))

Version Author Date
4c80969 XSun 2025-02-24 
if(max(as.numeric(df$num_bystander_pip08)) != 0){
  create_summary_plot_withTP(df,x_order = setting_names, 
                    columns_to_plot =  c("num_bystander_pip08","TP_rate"))
}

Version Author Date
4c80969 XSun 2025-02-24 
DT::datatable(df,caption = htmltools::tags$caption( style = 'caption-side: left; text-align: left; color:black;  font-size:150% ;',trait),options = list(pageLength = 10) )
db <- "GO_Biological_Process_2023"

num_go_adj005 <- c()
setting_names <- c()
for (st in sts) {
  for (thin in thins) {
    for (var_struc in var_strucs) {
      for (L in Ls) {

        setting_names <- c(setting_names, paste0(st,"_thin",thin,"_",var_struc,"_L",L))
        
        file_enrich <- paste0(folder_results,trait,"/",trait,".",st,".thin",thin,".",var_struc,".L",L, ".enrichr_",db,".RDS")
        if(file.exists(file_enrich)) {
          
          enrich_results <- readRDS(file_enrich)
          num_go_adj005 <- c(num_go_adj005,nrow(enrich_results))
        }else{
          num_go_adj005 <- c(num_go_adj005,0)
        }
        
        
        
      }
    }
  }
}


df <- data.frame(setting = setting_names, num_go_adj005 = num_go_adj005)
create_summary_plot(df,x_order = setting_names, 
                    columns_to_plot = c("num_go_adj005"), title = "Number of enriched GO terms, adj_p < 0.05")

Version Author Date
7673ad0 XSun 2025-02-28
4c80969 XSun 2025-02-24

IBD-ebi-a-GCST004131

trait <- "IBD-ebi-a-GCST004131"

setting_names <- c()
prop_h2g_nonsnp_all <- c()
num_gene_pip08_all <- c()
num_silver_pip08_all <- c()
num_bystander_pip08_all <- c()
imputable_known_all <- c()
imputable_bystander_all <- c()
for (st in sts) {
  for (thin in thins) {
    for (var_struc in var_strucs) {
      for (L in Ls) {

        setting_names <- c(setting_names, paste0(st,"_thin",thin,"_",var_struc,"_L",L))
        
        # non-snp %h2g
        param <- readRDS(paste0(folder_results,"/",trait,"/",trait,".",st,".thin",thin,".",var_struc,".param.RDS"))
        
        ctwas_parameters <- summarize_param(param, samplesize[trait])
        prop_h2g <- ctwas_parameters$prop_heritability
        prop_h2g_nonsnp <- 1 - ctwas_parameters$prop_heritability["SNP"]
        prop_h2g_nonsnp_all <- c(prop_h2g_nonsnp_all,prop_h2g_nonsnp)
        
        # num_gene_pip08
        combined_pip_by_group <- readRDS(paste0(folder_results,"/",trait,"/",trait,".",st,".thin",thin,".",var_struc,".L",L, ".combined_pip_bygroup_final.RDS"))
        combined_pip_sig <- combined_pip_by_group[combined_pip_by_group$combined_pip > 0.8,]
        
        num_gene_pip08_all <- c(num_gene_pip08_all, nrow(combined_pip_sig))
        
        # silver_standard genes
        known <- readRDS(paste0("/project/xinhe/xsun/multi_group_ctwas/data/silverstandard/known_annotations_",traits_silver[trait],".RDS"))
        bystander <- readRDS(paste0("/project/xinhe/xsun/multi_group_ctwas/data/silverstandard/bystanders_",traits_silver[trait],".RDS"))

        num_silver_pip08_all <- c(num_silver_pip08_all,sum(combined_pip_sig$gene_name %in% known))
        num_bystander_pip08_all <- c(num_bystander_pip08_all,sum(combined_pip_sig$gene_name %in% bystander))
        
        # imputable genes
        z_gene <-  readRDS(paste0(folder_results,"/",trait,"/",trait,".",st,".z_gene.RDS"))
        z_gene <- z_gene %>%
                  mutate(molecular_id = sub("\\|.*", "", id)) %>%  # Extract ENSG ID from id
                  left_join(mapping_two %>% dplyr::select(molecular_id, gene_name), by = "molecular_id")
        
        imputable_known_all <- c(imputable_known_all,sum(unique(z_gene$gene_name) %in% known))
        imputable_bystander_all <- c(imputable_bystander_all,sum(unique(z_gene$gene_name) %in% bystander))
        
      }
    }
  }
}


df <- data.frame(setting = setting_names,prop_h2g_nonsnp = prop_h2g_nonsnp_all ,num_gene_pip08 = num_gene_pip08_all,
                 num_silver_total = rep(length(known),length(setting_names)), num_imputable_silver = imputable_known_all, num_silver_pip08 = num_silver_pip08_all,
                 num_bystander_total = rep(length(bystander),length(setting_names)), num_imputable_bystander = imputable_bystander_all, num_bystander_pip08 = num_bystander_pip08_all,
                 TP_rate = num_silver_pip08_all/(num_silver_pip08_all+num_bystander_pip08_all))

## plot Non-SNP %h2g
create_summary_plot(df,x_order = setting_names, 
                    columns_to_plot = c("prop_h2g_nonsnp"), title = "%h2g -- NON-SNP")

Version Author Date
7673ad0 XSun 2025-02-28
4c80969 XSun 2025-02-24 
# 
# ## silver standard gene
# create_summary_plot(df,x_order = setting_names, 
#                     columns_to_plot = c("num_gene_pip08","num_imputable_silver","num_silver_pip08"),
#                     title = "Silver standard genes")
# 
# ## bystander genes
# create_summary_plot(df,x_order = setting_names, 
#                     columns_to_plot = c("num_gene_pip08","num_bystander_pip08"),
#                     title = "Bystander genes")
# 
# ## tp
# create_summary_plot(df,x_order = setting_names, 
#                     columns_to_plot = c("num_silver_pip08"),
#                     title = "True positive rate")

create_summary_plot_withTP(df,x_order = setting_names, 
                    columns_to_plot =  c("num_gene_pip08","num_silver_pip08","TP_rate","num_imputable_silver"))

Version Author Date
7673ad0 XSun 2025-02-28
4c80969 XSun 2025-02-24 
if(max(as.numeric(df$num_bystander_pip08)) != 0){
  create_summary_plot_withTP(df,x_order = setting_names, 
                    columns_to_plot =  c("num_bystander_pip08","TP_rate"))
}

DT::datatable(df,caption = htmltools::tags$caption( style = 'caption-side: left; text-align: left; color:black;  font-size:150% ;',trait),options = list(pageLength = 10) )
db <- "GO_Biological_Process_2023"

num_go_adj005 <- c()
setting_names <- c()
for (st in sts) {
  for (thin in thins) {
    for (var_struc in var_strucs) {
      for (L in Ls) {

        setting_names <- c(setting_names, paste0(st,"_thin",thin,"_",var_struc,"_L",L))
        
        file_enrich <- paste0(folder_results,trait,"/",trait,".",st,".thin",thin,".",var_struc,".L",L, ".enrichr_",db,".RDS")
        if(file.exists(file_enrich)) {
          
          enrich_results <- readRDS(file_enrich)
          num_go_adj005 <- c(num_go_adj005,nrow(enrich_results))
        }else{
          num_go_adj005 <- c(num_go_adj005,0)
        }
        
        
        
      }
    }
  }
}


df <- data.frame(setting = setting_names, num_go_adj005 = num_go_adj005)
create_summary_plot(df,x_order = setting_names, 
                    columns_to_plot = c("num_go_adj005"), title = "Number of enriched GO terms, adj_p < 0.05")

Version Author Date
7673ad0 XSun 2025-02-28
4c80969 XSun 2025-02-24

T2D-panukb

trait <- "T2D-panukb"

setting_names <- c()
prop_h2g_nonsnp_all <- c()
num_gene_pip08_all <- c()
num_silver_pip08_all <- c()
num_bystander_pip08_all <- c()
imputable_known_all <- c()
imputable_bystander_all <- c()
for (st in sts) {
  for (thin in thins) {
    for (var_struc in var_strucs) {
      for (L in Ls) {

        setting_names <- c(setting_names, paste0(st,"_thin",thin,"_",var_struc,"_L",L))

        # non-snp %h2g
        param <- readRDS(paste0(folder_results,"/",trait,"/",trait,".",st,".thin",thin,".",var_struc,".param.RDS"))

        ctwas_parameters <- summarize_param(param, samplesize[trait])
        prop_h2g <- ctwas_parameters$prop_heritability
        prop_h2g_nonsnp <- 1 - ctwas_parameters$prop_heritability["SNP"]
        prop_h2g_nonsnp_all <- c(prop_h2g_nonsnp_all,prop_h2g_nonsnp)

        # num_gene_pip08
        combined_pip_by_group <- readRDS(paste0(folder_results,"/",trait,"/",trait,".",st,".thin",thin,".",var_struc,".L",L, ".combined_pip_bygroup_final.RDS"))
        combined_pip_sig <- combined_pip_by_group[combined_pip_by_group$combined_pip > 0.8,]

        num_gene_pip08_all <- c(num_gene_pip08_all, nrow(combined_pip_sig))

        # silver_standard genes
        known <- readRDS(paste0("/project/xinhe/xsun/multi_group_ctwas/data/silverstandard/known_annotations_",traits_silver[trait],".RDS"))
        bystander <- readRDS(paste0("/project/xinhe/xsun/multi_group_ctwas/data/silverstandard/bystanders_",traits_silver[trait],".RDS"))

        num_silver_pip08_all <- c(num_silver_pip08_all,sum(combined_pip_sig$gene_name %in% known))
        num_bystander_pip08_all <- c(num_bystander_pip08_all,sum(combined_pip_sig$gene_name %in% bystander))

        # imputable genes
        z_gene <-  readRDS(paste0(folder_results,"/",trait,"/",trait,".",st,".z_gene.RDS"))
        z_gene <- z_gene %>%
                  mutate(molecular_id = sub("\\|.*", "", id)) %>%  # Extract ENSG ID from id
                  left_join(mapping_two %>% dplyr::select(molecular_id, gene_name), by = "molecular_id")

        imputable_known_all <- c(imputable_known_all,sum(unique(z_gene$gene_name) %in% known))
        imputable_bystander_all <- c(imputable_bystander_all,sum(unique(z_gene$gene_name) %in% bystander))

      }
    }
  }
}


df <- data.frame(setting = setting_names,prop_h2g_nonsnp = prop_h2g_nonsnp_all ,num_gene_pip08 = num_gene_pip08_all,
                 num_silver_total = rep(length(known),length(setting_names)), num_imputable_silver = imputable_known_all, num_silver_pip08 = num_silver_pip08_all,
                 num_bystander_total = rep(length(bystander),length(setting_names)), num_imputable_bystander = imputable_bystander_all, num_bystander_pip08 = num_bystander_pip08_all,
                 TP_rate = num_silver_pip08_all/(num_silver_pip08_all+num_bystander_pip08_all))

## plot Non-SNP %h2g
create_summary_plot(df,x_order = setting_names,
                    columns_to_plot = c("prop_h2g_nonsnp"), title = "%h2g -- NON-SNP")

Version Author Date
7673ad0 XSun 2025-02-28
4c80969 XSun 2025-02-24 
#
# ## silver standard gene
# create_summary_plot(df,x_order = setting_names,
#                     columns_to_plot = c("num_gene_pip08","num_imputable_silver","num_silver_pip08"),
#                     title = "Silver standard genes")
#
# ## bystander genes
# create_summary_plot(df,x_order = setting_names,
#                     columns_to_plot = c("num_gene_pip08","num_bystander_pip08"),
#                     title = "Bystander genes")
#
# ## tp
# create_summary_plot(df,x_order = setting_names,
#                     columns_to_plot = c("num_silver_pip08"),
#                     title = "True positive rate")

create_summary_plot_withTP(df,x_order = setting_names,
                    columns_to_plot =  c("num_gene_pip08","num_silver_pip08","TP_rate","num_imputable_silver"))

Version Author Date
7673ad0 XSun 2025-02-28
4c80969 XSun 2025-02-24 
if(max(as.numeric(df$num_bystander_pip08)) != 0){
  create_summary_plot_withTP(df,x_order = setting_names, 
                    columns_to_plot =  c("num_bystander_pip08","TP_rate"))
}

Version Author Date
7673ad0 XSun 2025-02-28
4c80969 XSun 2025-02-24 
DT::datatable(df,caption = htmltools::tags$caption( style = 'caption-side: left; text-align: left; color:black;  font-size:150% ;',trait),options = list(pageLength = 10) )
db <- "GO_Biological_Process_2023"

num_go_adj005 <- c()
setting_names <- c()
for (st in sts) {
  for (thin in thins) {
    for (var_struc in var_strucs) {
      for (L in Ls) {

        setting_names <- c(setting_names, paste0(st,"_thin",thin,"_",var_struc,"_L",L))
        
        file_enrich <- paste0(folder_results,trait,"/",trait,".",st,".thin",thin,".",var_struc,".L",L, ".enrichr_",db,".RDS")
        if(file.exists(file_enrich)) {
          
          enrich_results <- readRDS(file_enrich)
          num_go_adj005 <- c(num_go_adj005,nrow(enrich_results))
        }else{
          num_go_adj005 <- c(num_go_adj005,0)
        }
        
        
        
      }
    }
  }
}


df <- data.frame(setting = setting_names, num_go_adj005 = num_go_adj005)
create_summary_plot(df,x_order = setting_names, 
                    columns_to_plot = c("num_go_adj005"), title = "Number of enriched GO terms, adj_p < 0.05")

Version Author Date
7673ad0 XSun 2025-02-28

BMI-panukb

trait <- "BMI-panukb"

setting_names <- c()
prop_h2g_nonsnp_all <- c()
num_gene_pip08_all <- c()
num_silver_pip08_all <- c()
num_bystander_pip08_all <- c()
imputable_known_all <- c()
imputable_bystander_all <- c()
for (st in sts) {
  for (thin in thins) {
    for (var_struc in var_strucs) {
      for (L in Ls) {

        setting_names <- c(setting_names, paste0(st,"_thin",thin,"_",var_struc,"_L",L))

        # non-snp %h2g
        param <- readRDS(paste0(folder_results,"/",trait,"/",trait,".",st,".thin",thin,".",var_struc,".param.RDS"))

        ctwas_parameters <- summarize_param(param, samplesize[trait])
        prop_h2g <- ctwas_parameters$prop_heritability
        prop_h2g_nonsnp <- 1 - ctwas_parameters$prop_heritability["SNP"]
        prop_h2g_nonsnp_all <- c(prop_h2g_nonsnp_all,prop_h2g_nonsnp)

        # num_gene_pip08
        combined_pip_by_group <- readRDS(paste0(folder_results,"/",trait,"/",trait,".",st,".thin",thin,".",var_struc,".L",L, ".combined_pip_bygroup_final.RDS"))
        combined_pip_sig <- combined_pip_by_group[combined_pip_by_group$combined_pip > 0.8,]

        num_gene_pip08_all <- c(num_gene_pip08_all, nrow(combined_pip_sig))

        # silver_standard genes
        known <- readRDS(paste0("/project/xinhe/xsun/multi_group_ctwas/data/silverstandard/known_annotations_",traits_silver[trait],".RDS"))
        bystander <- readRDS(paste0("/project/xinhe/xsun/multi_group_ctwas/data/silverstandard/bystanders_",traits_silver[trait],".RDS"))

        num_silver_pip08_all <- c(num_silver_pip08_all,sum(combined_pip_sig$gene_name %in% known))
        num_bystander_pip08_all <- c(num_bystander_pip08_all,sum(combined_pip_sig$gene_name %in% bystander))

        # imputable genes
        z_gene <-  readRDS(paste0(folder_results,"/",trait,"/",trait,".",st,".z_gene.RDS"))
        z_gene <- z_gene %>%
                  mutate(molecular_id = sub("\\|.*", "", id)) %>%  # Extract ENSG ID from id
                  left_join(mapping_two %>% dplyr::select(molecular_id, gene_name), by = "molecular_id")

        imputable_known_all <- c(imputable_known_all,sum(unique(z_gene$gene_name) %in% known))
        imputable_bystander_all <- c(imputable_bystander_all,sum(unique(z_gene$gene_name) %in% bystander))

      }
    }
  }
}


df <- data.frame(setting = setting_names,prop_h2g_nonsnp = prop_h2g_nonsnp_all ,num_gene_pip08 = num_gene_pip08_all,
                 num_silver_total = rep(length(known),length(setting_names)), num_imputable_silver = imputable_known_all, num_silver_pip08 = num_silver_pip08_all,
                 num_bystander_total = rep(length(bystander),length(setting_names)), num_imputable_bystander = imputable_bystander_all, num_bystander_pip08 = num_bystander_pip08_all,
                 TP_rate = num_silver_pip08_all/(num_silver_pip08_all+num_bystander_pip08_all))

## plot Non-SNP %h2g
create_summary_plot(df,x_order = setting_names,
                    columns_to_plot = c("prop_h2g_nonsnp"), title = "%h2g -- NON-SNP")

Version Author Date
7673ad0 XSun 2025-02-28 
#
# ## silver standard gene
# create_summary_plot(df,x_order = setting_names,
#                     columns_to_plot = c("num_gene_pip08","num_imputable_silver","num_silver_pip08"),
#                     title = "Silver standard genes")
#
# ## bystander genes
# create_summary_plot(df,x_order = setting_names,
#                     columns_to_plot = c("num_gene_pip08","num_bystander_pip08"),
#                     title = "Bystander genes")
#
# ## tp
# create_summary_plot(df,x_order = setting_names,
#                     columns_to_plot = c("num_silver_pip08"),
#                     title = "True positive rate")

create_summary_plot_withTP(df,x_order = setting_names,
                    columns_to_plot =  c("num_gene_pip08","num_silver_pip08","TP_rate","num_imputable_silver"))

Version Author Date
7673ad0 XSun 2025-02-28 
if(max(as.numeric(df$num_bystander_pip08)) != 0){
  create_summary_plot_withTP(df,x_order = setting_names, 
                    columns_to_plot =  c("num_bystander_pip08","TP_rate"))
}

Version Author Date
7673ad0 XSun 2025-02-28 
DT::datatable(df,caption = htmltools::tags$caption( style = 'caption-side: left; text-align: left; color:black;  font-size:150% ;',trait),options = list(pageLength = 10) )
db <- "GO_Biological_Process_2023"

num_go_adj005 <- c()
setting_names <- c()
for (st in sts) {
  for (thin in thins) {
    for (var_struc in var_strucs) {
      for (L in Ls) {

        setting_names <- c(setting_names, paste0(st,"_thin",thin,"_",var_struc,"_L",L))
        
        file_enrich <- paste0(folder_results,trait,"/",trait,".",st,".thin",thin,".",var_struc,".L",L, ".enrichr_",db,".RDS")
        if(file.exists(file_enrich)) {
          
          enrich_results <- readRDS(file_enrich)
          num_go_adj005 <- c(num_go_adj005,nrow(enrich_results))
        }else{
          num_go_adj005 <- c(num_go_adj005,0)
        }
        
        
        
      }
    }
  }
}


df <- data.frame(setting = setting_names, num_go_adj005 = num_go_adj005)
create_summary_plot(df,x_order = setting_names, 
                    columns_to_plot = c("num_go_adj005"), title = "Number of enriched GO terms, adj_p < 0.05")

Version Author Date
7673ad0 XSun 2025-02-28

RBC-panukb

trait <- "RBC-panukb"

setting_names <- c()
prop_h2g_nonsnp_all <- c()
num_gene_pip08_all <- c()
num_silver_pip08_all <- c()
num_bystander_pip08_all <- c()
imputable_known_all <- c()
imputable_bystander_all <- c()
for (st in sts) {
  for (thin in thins) {
    for (var_struc in var_strucs) {
      for (L in Ls) {

        setting_names <- c(setting_names, paste0(st,"_thin",thin,"_",var_struc,"_L",L))

        # non-snp %h2g
        param <- readRDS(paste0(folder_results,"/",trait,"/",trait,".",st,".thin",thin,".",var_struc,".param.RDS"))

        ctwas_parameters <- summarize_param(param, samplesize[trait])
        prop_h2g <- ctwas_parameters$prop_heritability
        prop_h2g_nonsnp <- 1 - ctwas_parameters$prop_heritability["SNP"]
        prop_h2g_nonsnp_all <- c(prop_h2g_nonsnp_all,prop_h2g_nonsnp)

        # num_gene_pip08
        combined_pip_by_group <- readRDS(paste0(folder_results,"/",trait,"/",trait,".",st,".thin",thin,".",var_struc,".L",L, ".combined_pip_bygroup_final.RDS"))
        combined_pip_sig <- combined_pip_by_group[combined_pip_by_group$combined_pip > 0.8,]

        num_gene_pip08_all <- c(num_gene_pip08_all, nrow(combined_pip_sig))

        # silver_standard genes
        known <- readRDS(paste0("/project/xinhe/xsun/multi_group_ctwas/data/silverstandard/known_annotations_",traits_silver[trait],".RDS"))
        bystander <- readRDS(paste0("/project/xinhe/xsun/multi_group_ctwas/data/silverstandard/bystanders_",traits_silver[trait],".RDS"))

        num_silver_pip08_all <- c(num_silver_pip08_all,sum(combined_pip_sig$gene_name %in% known))
        num_bystander_pip08_all <- c(num_bystander_pip08_all,sum(combined_pip_sig$gene_name %in% bystander))

        # imputable genes
        z_gene <-  readRDS(paste0(folder_results,"/",trait,"/",trait,".",st,".z_gene.RDS"))
        z_gene <- z_gene %>%
                  mutate(molecular_id = sub("\\|.*", "", id)) %>%  # Extract ENSG ID from id
                  left_join(mapping_two %>% dplyr::select(molecular_id, gene_name), by = "molecular_id")

        imputable_known_all <- c(imputable_known_all,sum(unique(z_gene$gene_name) %in% known))
        imputable_bystander_all <- c(imputable_bystander_all,sum(unique(z_gene$gene_name) %in% bystander))

      }
    }
  }
}


df <- data.frame(setting = setting_names,prop_h2g_nonsnp = prop_h2g_nonsnp_all ,num_gene_pip08 = num_gene_pip08_all,
                 num_silver_total = rep(length(known),length(setting_names)), num_imputable_silver = imputable_known_all, num_silver_pip08 = num_silver_pip08_all,
                 num_bystander_total = rep(length(bystander),length(setting_names)), num_imputable_bystander = imputable_bystander_all, num_bystander_pip08 = num_bystander_pip08_all,
                 TP_rate = num_silver_pip08_all/(num_silver_pip08_all+num_bystander_pip08_all))

## plot Non-SNP %h2g
create_summary_plot(df,x_order = setting_names,
                    columns_to_plot = c("prop_h2g_nonsnp"), title = "%h2g -- NON-SNP")

Version Author Date
7673ad0 XSun 2025-02-28 
#
# ## silver standard gene
# create_summary_plot(df,x_order = setting_names,
#                     columns_to_plot = c("num_gene_pip08","num_imputable_silver","num_silver_pip08"),
#                     title = "Silver standard genes")
#
# ## bystander genes
# create_summary_plot(df,x_order = setting_names,
#                     columns_to_plot = c("num_gene_pip08","num_bystander_pip08"),
#                     title = "Bystander genes")
#
# ## tp
# create_summary_plot(df,x_order = setting_names,
#                     columns_to_plot = c("num_silver_pip08"),
#                     title = "True positive rate")

create_summary_plot_withTP(df,x_order = setting_names,
                    columns_to_plot =  c("num_gene_pip08","num_silver_pip08","TP_rate","num_imputable_silver"))

Version Author Date
7673ad0 XSun 2025-02-28 
if(max(as.numeric(df$num_bystander_pip08)) != 0){
  create_summary_plot_withTP(df,x_order = setting_names, 
                    columns_to_plot =  c("num_bystander_pip08","TP_rate"))
}

Version Author Date
7673ad0 XSun 2025-02-28 
DT::datatable(df,caption = htmltools::tags$caption( style = 'caption-side: left; text-align: left; color:black;  font-size:150% ;',trait),options = list(pageLength = 10) )
db <- "GO_Biological_Process_2023"

num_go_adj005 <- c()
setting_names <- c()
for (st in sts) {
  for (thin in thins) {
    for (var_struc in var_strucs) {
      for (L in Ls) {

        setting_names <- c(setting_names, paste0(st,"_thin",thin,"_",var_struc,"_L",L))
        
        file_enrich <- paste0(folder_results,trait,"/",trait,".",st,".thin",thin,".",var_struc,".L",L, ".enrichr_",db,".RDS")
        if(file.exists(file_enrich)) {
          
          enrich_results <- readRDS(file_enrich)
          num_go_adj005 <- c(num_go_adj005,nrow(enrich_results))
        }else{
          num_go_adj005 <- c(num_go_adj005,0)
        }
        
        
        
      }
    }
  }
}


df <- data.frame(setting = setting_names, num_go_adj005 = num_go_adj005)
create_summary_plot(df,x_order = setting_names, 
                    columns_to_plot = c("num_go_adj005"), title = "Number of enriched GO terms, adj_p < 0.05")

Version Author Date
7673ad0 XSun 2025-02-28 

sessionInfo()
R version 4.2.0 (2022-04-22)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: CentOS Linux 7 (Core)

Matrix products: default
BLAS/LAPACK: /software/openblas-0.3.13-el7-x86_64/lib/libopenblas_haswellp-r0.3.13.so

locale:
[1] C

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

other attached packages:
[1] scales_1.3.0     tidyr_1.3.0      dplyr_1.1.4      ggplot2_3.5.1   
[5] ctwas_0.5.4.9000

loaded via a namespace (and not attached):
  [1] colorspace_2.0-3            rjson_0.2.21               
  [3] ellipsis_0.3.2              rprojroot_2.0.3            
  [5] XVector_0.36.0              locuszoomr_0.2.1           
  [7] GenomicRanges_1.48.0        base64enc_0.1-3            
  [9] fs_1.5.2                    rstudioapi_0.13            
 [11] farver_2.1.0                DT_0.22                    
 [13] ggrepel_0.9.1               bit64_4.0.5                
 [15] AnnotationDbi_1.58.0        fansi_1.0.3                
 [17] xml2_1.3.3                  codetools_0.2-18           
 [19] logging_0.10-108            cachem_1.0.6               
 [21] knitr_1.39                  jsonlite_1.8.0             
 [23] workflowr_1.7.0             Rsamtools_2.12.0           
 [25] dbplyr_2.1.1                png_0.1-7                  
 [27] readr_2.1.2                 compiler_4.2.0             
 [29] httr_1.4.3                  assertthat_0.2.1           
 [31] Matrix_1.5-3                fastmap_1.1.0              
 [33] lazyeval_0.2.2              cli_3.6.1                  
 [35] later_1.3.0                 htmltools_0.5.2            
 [37] prettyunits_1.1.1           tools_4.2.0                
 [39] gtable_0.3.0                glue_1.6.2                 
 [41] GenomeInfoDbData_1.2.8      rappdirs_0.3.3             
 [43] Rcpp_1.0.12                 Biobase_2.56.0             
 [45] jquerylib_0.1.4             vctrs_0.6.5                
 [47] Biostrings_2.64.0           rtracklayer_1.56.0         
 [49] crosstalk_1.2.0             xfun_0.41                  
 [51] stringr_1.5.1               irlba_2.3.5                
 [53] lifecycle_1.0.4             restfulr_0.0.14            
 [55] ensembldb_2.20.2            XML_3.99-0.14              
 [57] zlibbioc_1.42.0             zoo_1.8-10                 
 [59] gggrid_0.2-0                hms_1.1.1                  
 [61] promises_1.2.0.1            MatrixGenerics_1.8.0       
 [63] ProtGenerics_1.28.0         parallel_4.2.0             
 [65] SummarizedExperiment_1.26.1 RColorBrewer_1.1-3         
 [67] AnnotationFilter_1.20.0     LDlinkR_1.2.3              
 [69] yaml_2.3.5                  curl_4.3.2                 
 [71] memoise_2.0.1               sass_0.4.1                 
 [73] biomaRt_2.54.1              stringi_1.7.6              
 [75] RSQLite_2.3.1               highr_0.9                  
 [77] S4Vectors_0.34.0            BiocIO_1.6.0               
 [79] GenomicFeatures_1.48.3      BiocGenerics_0.42.0        
 [81] filelock_1.0.2              BiocParallel_1.30.3        
 [83] repr_1.1.4                  GenomeInfoDb_1.39.9        
 [85] rlang_1.1.2                 pkgconfig_2.0.3            
 [87] matrixStats_0.62.0          bitops_1.0-7               
 [89] evaluate_0.15               lattice_0.20-45            
 [91] purrr_1.0.2                 labeling_0.4.2             
 [93] GenomicAlignments_1.32.0    htmlwidgets_1.5.4          
 [95] cowplot_1.1.1               bit_4.0.4                  
 [97] tidyselect_1.2.0            magrittr_2.0.3             
 [99] AMR_2.1.1                   R6_2.5.1                   
[101] IRanges_2.30.0              generics_0.1.2             
[103] DelayedArray_0.22.0         DBI_1.2.2                  
[105] withr_2.5.0                 pgenlibr_0.3.3             
[107] pillar_1.9.0                whisker_0.4                
[109] mixsqp_0.3-43               KEGGREST_1.36.3            
[111] RCurl_1.98-1.7              tibble_3.2.1               
[113] crayon_1.5.1                utf8_1.2.2                 
[115] BiocFileCache_2.4.0         plotly_4.10.0              
[117] tzdb_0.4.0                  rmarkdown_2.25             
[119] progress_1.2.2              grid_4.2.0                 
[121] data.table_1.14.2           blob_1.2.3                 
[123] git2r_0.30.1                digest_0.6.29              
[125] httpuv_1.6.5                stats4_4.2.0               
[127] munsell_0.5.0               viridisLite_0.4.0          
[129] skimr_2.1.4                 bslib_0.3.1