Comparing results from predictdb eQTL + sQTL / predictdb eQTL + sQTL + Munro rsQTL + apaQTL / predictdb eQTL + sQTL + Munro stQTL for 4 traits

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Last updated: 2024-10-14

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---

Settings

6 modalities from Munro

The stQTL was a combination of Munro apa + rs QTL, if a gene has both rs-QTL and APA-QTL, we use rs-QTL.

1. Weight processing:

PredictDB:

all the PredictDB are converted from FUSION weights

• drop_strand_ambig = TRUE,
• scale_by_ld_variance = F (FUSION converted weights)
• load_predictdb_LD = F,

2. Parameter estimation and fine-mapping

• niter_prefit = 5,
• niter = 30(default),
• L: determined by uniform susie,
• group_prior_var_structure = “shared_type”,
• maxSNP = 20000,
• min_nonSNP_PIP = 0.5,

weights from predictdb

1. Weight processing:

PredictDB (eqtl, sqtl)

• drop_strand_ambig = TRUE,
• scale_by_ld_variance = T
• load_predictdb_LD = F,

2. Parameter estimation and fine-mapping

• group_prior_var_structure = “shared_type”,
• filter_L = TRUE,
• filter_nonSNP_PIP = FALSE,
• min_nonSNP_PIP = 0.5,
• min_abs_corr = 0.1,

mem: 100g 10cores

library(ctwas)
library(EnsDb.Hsapiens.v86)
library(VennDiagram)
library(ggplot2)
library(gridExtra)
library(pheatmap)
library(dplyr)

ens_db <- EnsDb.Hsapiens.v86

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)

load("/project2/xinhe/shared_data/multigroup_ctwas/gwas/samplesize.rdata")


colors <- c("#1b9e77", "#d95f02", "#7570b3", "#e7298a", "#66a61e", 
               "#e6ab02", "#a6761d", "#666666", "#a6cee3")

source("/project/xinhe/xsun/r_functions/ctwas_combine_pip_nocs.R")

plot_piechart <- function(ctwas_parameters, colors) {
  data <- data.frame(
    category = names(ctwas_parameters$prop_heritability),
    percentage = ctwas_parameters$prop_heritability
  )
  
  # Calculate percentage labels for the chart
  data$percentage_label <- paste0(round(data$percentage * 100, 1), "%")
  
  pie <- ggplot(data, aes(x = "", y = percentage, fill = category)) +
    geom_bar(stat = "identity", width = 1) +
    coord_polar("y", start = 0) +
    theme_void() +  # Remove background and axes
    geom_text(aes(label = percentage_label), 
              position = position_stack(vjust = 0.5), size = 2) +
    scale_fill_manual(values = colors) +  # Custom colors
    labs(fill = "Category") +  # Legend title
    ggtitle("Percent of heritability")  # Title
  
  return(pie)
}

rename_heatmap_columns <- function(heatmap_data, column_order) {
  # Select columns that are in the column_order and exist in heatmap_data
  selected_columns <- intersect(column_order, colnames(heatmap_data))
  heatmap_data <- heatmap_data[, selected_columns]
  
  # Initialize new_column_names as a copy of selected_columns
  new_column_names <- selected_columns

  # Assign the new column names to heatmap_data
  colnames(heatmap_data) <- new_column_names
  
  return(heatmap_data)
}

plot_heatmap <- function(heatmap_data, main) {
  
  rownames(heatmap_data) <- heatmap_data$gene_name
  heatmap_data <- heatmap_data %>% dplyr::select(-gene_name, -combined_pip)
  
  if(nrow(heatmap_data) ==1){
    
    heatmap_data <- rbind(heatmap_data,rep(0,ncol(heatmap_data)))
    rownames(heatmap_data)[2] <- "fake_gene_for_plotting"
    
  }
  
  heatmap_matrix <- as.matrix(heatmap_data)
  
  p <- pheatmap(heatmap_matrix,
         cluster_rows = F,   # Cluster the rows (genes)
         cluster_cols = F,   # Cluster the columns (QTL types)
         color = colorRampPalette(c("white", "red"))(50), # Color gradient
         display_numbers = TRUE, # Display numbers in cells
         main = main,labels_row = rownames(heatmap_data), silent = T)
  
  return(p)
}


plot_3venn <- function(es, esra, ess) {
  
  venn.plot <- draw.triple.venn(
      area1 = length(es),
      area2 = length(esra),
      area3 = length(ess),
      n12 = length(intersect(es, esra)),
      n23 = length(intersect(esra, ess)),
      n13 = length(intersect(es, ess)),
      n123 = length(Reduce(intersect, list(es, esra, ess))),
      category = c("e+s", "e+s+rs+apa", "e+s+st"),
      fill = c("red", "green", "blue"),
      lty = "dashed",
      cex = 2,
      cat.cex = 1.5,
      cat.col = c("red", "green", "blue"),
      scaled = T
      )
  
  return(venn.plot)
  
}

LDL-ukb-d-30780_irnt

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

gwas_n <- samplesize[trait]

results_dir_ess <- paste0("/project/xinhe/xsun/multi_group_ctwas/9.deciding_weights_4traits/results/",trait,"/ess/")

snp_map_ess <- readRDS(paste0(results_dir_ess,trait,".snp_map.RDS"))
ctwas_res_ess <- readRDS(paste0(results_dir_ess,trait,".ctwas.res.RDS"))

param_ess <- ctwas_res_ess$param
finemap_res_ess <- ctwas_res_ess$finemap_res


p_conv_ess <- make_convergence_plots(param_ess, gwas_n, ncol = 1, colors = colors)
ctwas_parameters_ess <- summarize_param(param_ess, gwas_n)
pve_pie_ess <- plot_piechart(ctwas_parameters = ctwas_parameters_ess, colors = colors)

susie_alpha_res_ess <- ctwas_res_ess$susie_alpha_res

susie_alpha_res_ess <- anno_susie_alpha_res(susie_alpha_res_ess,
                                        mapping_table = mapping_two,
                                        map_by = "molecular_id",
                                        drop_unmapped = TRUE)

2024-10-14 10:06:30 INFO::Annotating susie alpha result ...
2024-10-14 10:06:30 INFO::Map molecular traits to genes
2024-10-14 10:06:34 INFO::Split PIPs for molecular traits mapped to multiple genes

combined_pip_by_type_ess <- combine_gene_pips(susie_alpha_res_ess, 
                                             group_by = "gene_name",
                                             by = "type",
                                             method = "combine_cs",
                                             filter_cs = TRUE,
                                             include_cs_id = F)

results_dir_espred <- paste0("/project/xinhe/xsun/multi_group_ctwas/9.deciding_weights_4traits/results/",trait,"/espred/")

snp_map_espred <- readRDS(paste0(results_dir_espred,trait,".snp_map.RDS"))
ctwas_res_espred <- readRDS(paste0(results_dir_espred,trait,".ctwas.res.RDS"))

param_espred <- ctwas_res_espred$param
finemap_res_espred <- ctwas_res_espred$finemap_res


p_conv_espred <- make_convergence_plots(param_espred, gwas_n, ncol = 1, colors = colors)
ctwas_parameters_espred <- summarize_param(param_espred, gwas_n)
pve_pie_espred <- plot_piechart(ctwas_parameters = ctwas_parameters_espred, colors = colors)


finemap_res_espred$molecular_id <- get_molecular_ids(finemap_res_espred)
finemap_res_espred <- anno_finemap_res(finemap_res_espred,
                              snp_map = snp_map_espred,
                              mapping_table = mapping_two,
                              add_gene_annot = TRUE,
                              map_by = "molecular_id",
                              drop_unmapped = TRUE,
                              add_position = TRUE,
                              use_gene_pos = "mid")

2024-10-14 10:07:02 INFO::Annotating fine-mapping result ...
2024-10-14 10:07:02 INFO::Map molecular traits to genes
2024-10-14 10:07:03 INFO::Split PIPs for molecular traits mapped to multiple genes
2024-10-14 10:07:09 INFO::Add gene positions
2024-10-14 10:07:10 INFO::Add SNP positions

combined_pip_by_type_espred <- combine_gene_pips_nocs(finemap_res =finemap_res_espred,
                                  group_by = "gene_name",
                                  by = "type", 
                                  method = "combine_cs",
                                  filter_cs = T )

2024-10-14 10:07:23 INFO::Limit gene results to credible sets

results_dir_4W <- paste0("/project/xinhe/xsun/multi_group_ctwas/9.deciding_weights_4traits/results/",trait,"/4W/")

snp_map_4W <- readRDS(paste0(results_dir_4W,trait,".snp_map.RDS"))
ctwas_res_4W <- readRDS(paste0(results_dir_4W,trait,".ctwas.res.RDS"))

param_4W <- ctwas_res_4W$param
finemap_res_4W <- ctwas_res_4W$finemap_res

p_conv_4W <- make_convergence_plots(param_4W, gwas_n, ncol = 1, colors = colors)
ctwas_parameters_4W <- summarize_param(param_4W, gwas_n)
pve_pie_4W <- plot_piechart(ctwas_parameters = ctwas_parameters_4W, colors = colors)

finemap_res_4W$molecular_id <- get_molecular_ids(finemap_res_4W)
finemap_res_4W <- anno_finemap_res(finemap_res_4W,
                              snp_map = snp_map_4W,
                              mapping_table = mapping_two,
                              add_gene_annot = TRUE,
                              map_by = "molecular_id",
                              drop_unmapped = TRUE,
                              add_position = TRUE,
                              use_gene_pos = "mid")

2024-10-14 10:07:45 INFO::Annotating fine-mapping result ...
2024-10-14 10:07:45 INFO::Map molecular traits to genes
2024-10-14 10:07:46 INFO::Split PIPs for molecular traits mapped to multiple genes
2024-10-14 10:07:49 INFO::Add gene positions
2024-10-14 10:07:50 INFO::Add SNP positions

combined_pip_by_type_4W <- combine_gene_pips_nocs(finemap_res =finemap_res_4W,
                                  group_by = "gene_name",
                                  by = "type", 
                                  method = "combine_cs",
                                  filter_cs = T )

2024-10-14 10:07:59 INFO::Limit gene results to credible sets

Parameters

print("each column represents one setting: predictdb e+s, predictdb e+s + Munro apa+rs, predictdb e+s + Munro st QTL")

[1] "each column represents one setting: predictdb e+s, predictdb e+s + Munro apa+rs, predictdb e+s + Munro st QTL"

grid.arrange(p_conv_espred,p_conv_4W,p_conv_ess, ncol = 3)

Past versions of unnamed-chunk-5-1.png

Version	Author	Date
06c5331	XSun	2024-10-14

######pve
group_pve_espred <- ctwas_parameters_espred$group_pve
group_pve_espred <- group_pve_espred[-length(group_pve_espred)]
group_pve_espred <- c(group_pve_espred, rep(NA,3))
group_pve_espred <- c(group_pve_espred, ctwas_parameters_espred$total_pve)
names(group_pve_espred) <- c("eQTL_pred","sQTL_pred","apaQTL_munro","rsQTL_munro","stQTL_munro","TOTAL")

group_pve_4W <- ctwas_parameters_4W$group_pve
group_pve_4W <- group_pve_4W[-length(group_pve_4W)]
group_pve_4W <- c(group_pve_4W, rep(NA,1))
group_pve_4W <- c(group_pve_4W, ctwas_parameters_4W$total_pve)
names(group_pve_4W) <- c("eQTL_pred","sQTL_pred","apaQTL_munro","rsQTL_munro","stQTL_munro","TOTAL")

group_pve_ess <- ctwas_parameters_ess$group_pve
group_pve_ess <- group_pve_ess[-length(group_pve_ess)]
group_pve_ess <- c(group_pve_ess[1:2], rep(NA,2),group_pve_ess[3])
group_pve_ess <- c(group_pve_ess, ctwas_parameters_ess$total_pve)
names(group_pve_ess) <- c("eQTL_pred","sQTL_pred","apaQTL_munro","rsQTL_munro","stQTL_munro","TOTAL")

grouppve <- cbind(group_pve_espred,group_pve_4W,group_pve_ess)
grouppve <- round(grouppve,digits = 4)

######size
group_size_espred <- ctwas_parameters_espred$group_size
group_size_espred <- group_size_espred[-length(group_size_espred)]
group_size_espred <- c(group_size_espred, rep(NA,3))
names(group_size_espred) <- c("eQTL_pred","sQTL_pred","apaQTL_munro","rsQTL_munro","stQTL_munro")

group_size_4W <- ctwas_parameters_4W$group_size
group_size_4W <- group_size_4W[-length(group_size_4W)]
group_size_4W <- c(group_size_4W, rep(NA,1))
names(group_size_4W) <-  c("eQTL_pred","sQTL_pred","apaQTL_munro","rsQTL_munro","stQTL_munro")

group_size_ess <- ctwas_parameters_ess$group_size
group_size_ess <- group_size_ess[-length(group_size_ess)]
group_size_ess <- c(group_size_ess[1:2], rep(NA,2),group_size_ess[3])
names(group_size_ess) <- c("eQTL_pred","sQTL_pred","apaQTL_munro","rsQTL_munro","stQTL_munro")

groupsize <- cbind(group_size_espred,group_size_4W,group_size_ess)

group_info <- cbind(grouppve,rbind(groupsize,c(rep(NA,3))))

DT::datatable(group_info,caption = htmltools::tags$caption( style = 'caption-side: topleft; text-align = left; color:black;','Group PVE and Group Size'),options = list(pageLength = 10) )

print("each pie chart represents one setting: predictdb e+s, predictdb e+s + Munro apa+rs, predictdb e+s + Munro st QTL")

[1] "each pie chart represents one setting: predictdb e+s, predictdb e+s + Munro apa+rs, predictdb e+s + Munro st QTL"

grid.arrange(pve_pie_espred,pve_pie_4W,pve_pie_ess, ncol =3)

Past versions of unnamed-chunk-5-3.png

Version	Author	Date
06c5331	XSun	2024-10-14

Fine-mapping results

combined_sig_espred <- combined_pip_by_type_espred[combined_pip_by_type_espred$combined_pip > 0.8,]
combined_sig_4W <- combined_pip_by_type_4W[combined_pip_by_type_4W$combined_pip > 0.8,]
combined_sig_ess <- combined_pip_by_type_ess[combined_pip_by_type_ess$combined_pip > 0.8,]

sprintf("# of genes with PIP > 0.8 = %s -- predictdb e +s", nrow(combined_sig_espred))

[1] "# of genes with PIP > 0.8 = 42 -- predictdb e +s"

sprintf("# of genes with PIP > 0.8 = %s -- predictdb e + s + Munro apa + rs", nrow(combined_sig_4W))

[1] "# of genes with PIP > 0.8 = 60 -- predictdb e + s + Munro apa + rs"

sprintf("# of genes with PIP > 0.8 = %s -- predictdb e+s + Munro st QTL", nrow(combined_sig_ess))

[1] "# of genes with PIP > 0.8 = 58 -- predictdb e+s + Munro st QTL"

venn.plot <- plot_3venn(es = combined_sig_espred$gene_name,esra = combined_sig_4W$gene_name,ess = combined_sig_ess$gene_name)

Past versions of unnamed-chunk-6-1.png

Version	Author	Date
06c5331	XSun	2024-10-14

###1
heatmap_data <- combined_sig_ess[!combined_sig_ess$gene_name %in%combined_sig_espred$gene_name, ]
column_order <- c("gene_name","combined_pip",
                  "eQTL_pip", "sQTL_pip", "stQTL_pip")
heatmap_data <- rename_heatmap_columns(heatmap_data = heatmap_data, column_order = column_order)
p1 <- plot_heatmap(heatmap_data = heatmap_data,main = "PIP partition for the genes reported by e + s + st setting but not by e+s setting")

###2
heatmap_data <- combined_sig_4W[!combined_sig_4W$gene_name %in%combined_sig_ess$gene_name, ]
column_order <- c("gene_name","combined_pip",
                  "eQTL_pip", "sQTL_pip", "rsQTL_pip","apaQTL_pip")
heatmap_data <- rename_heatmap_columns(heatmap_data = heatmap_data, column_order = column_order)
p2 <- plot_heatmap(heatmap_data = heatmap_data,main = "PIP partition for the genes reported by e + s + apa + rs setting but not by e+s+st setting")

###3
heatmap_data <- combined_sig_ess[!combined_sig_ess$gene_name %in%combined_sig_4W$gene_name, ]
column_order <- c("gene_name","combined_pip",
                  "eQTL_pip", "sQTL_pip", "stQTL_pip")
heatmap_data <- rename_heatmap_columns(heatmap_data = heatmap_data, column_order = column_order)


p3 <- plot_heatmap(heatmap_data = heatmap_data,main = "PIP partition for the genes reported by e + s + st setting but not by e + s + apa + rs setting")

g1 <- p1$gtable
g2 <- p2$gtable
g3 <- p3$gtable
grid.arrange(g1, g2, g3, ncol=3)

Past versions of unnamed-chunk-7-1.png

Version	Author	Date
06c5331	XSun	2024-10-14

IBD-ebi-a-GCST004131

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

gwas_n <- samplesize[trait]

results_dir_ess <- paste0("/project/xinhe/xsun/multi_group_ctwas/9.deciding_weights_4traits/results/",trait,"/ess/")

snp_map_ess <- readRDS(paste0(results_dir_ess,trait,".snp_map.RDS"))
ctwas_res_ess <- readRDS(paste0(results_dir_ess,trait,".ctwas.res.RDS"))

param_ess <- ctwas_res_ess$param
finemap_res_ess <- ctwas_res_ess$finemap_res


p_conv_ess <- make_convergence_plots(param_ess, gwas_n, ncol = 1, colors = colors)
ctwas_parameters_ess <- summarize_param(param_ess, gwas_n)
pve_pie_ess <- plot_piechart(ctwas_parameters = ctwas_parameters_ess, colors = colors)

susie_alpha_res_ess <- ctwas_res_ess$susie_alpha_res

susie_alpha_res_ess <- anno_susie_alpha_res(susie_alpha_res_ess,
                                        mapping_table = mapping_two,
                                        map_by = "molecular_id",
                                        drop_unmapped = TRUE)

2024-10-14 10:08:27 INFO::Annotating susie alpha result ...
2024-10-14 10:08:27 INFO::Map molecular traits to genes
2024-10-14 10:08:28 INFO::Split PIPs for molecular traits mapped to multiple genes

combined_pip_by_type_ess <- combine_gene_pips(susie_alpha_res_ess, 
                                             group_by = "gene_name",
                                             by = "type",
                                             method = "combine_cs",
                                             filter_cs = TRUE,
                                             include_cs_id = F)

results_dir_espred <- paste0("/project/xinhe/xsun/multi_group_ctwas/9.deciding_weights_4traits/results/",trait,"/espred/")

snp_map_espred <- readRDS(paste0(results_dir_espred,trait,".snp_map.RDS"))
ctwas_res_espred <- readRDS(paste0(results_dir_espred,trait,".ctwas.res.RDS"))

param_espred <- ctwas_res_espred$param
finemap_res_espred <- ctwas_res_espred$finemap_res


p_conv_espred <- make_convergence_plots(param_espred, gwas_n, ncol = 1, colors = colors)
ctwas_parameters_espred <- summarize_param(param_espred, gwas_n)
pve_pie_espred <- plot_piechart(ctwas_parameters = ctwas_parameters_espred, colors = colors)


finemap_res_espred$molecular_id <- get_molecular_ids(finemap_res_espred)
finemap_res_espred <- anno_finemap_res(finemap_res_espred,
                              snp_map = snp_map_espred,
                              mapping_table = mapping_two,
                              add_gene_annot = TRUE,
                              map_by = "molecular_id",
                              drop_unmapped = TRUE,
                              add_position = TRUE,
                              use_gene_pos = "mid")

2024-10-14 10:08:48 INFO::Annotating fine-mapping result ...
2024-10-14 10:08:49 INFO::Map molecular traits to genes
2024-10-14 10:08:49 INFO::Split PIPs for molecular traits mapped to multiple genes
2024-10-14 10:08:56 INFO::Add gene positions
2024-10-14 10:08:56 INFO::Add SNP positions

combined_pip_by_type_espred <- combine_gene_pips_nocs(finemap_res =finemap_res_espred,
                                  group_by = "gene_name",
                                  by = "type", 
                                  method = "combine_cs",
                                  filter_cs = T )

2024-10-14 10:09:00 INFO::Limit gene results to credible sets

results_dir_4W <- paste0("/project/xinhe/xsun/multi_group_ctwas/9.deciding_weights_4traits/results/",trait,"/4W/")

snp_map_4W <- readRDS(paste0(results_dir_4W,trait,".snp_map.RDS"))
ctwas_res_4W <- readRDS(paste0(results_dir_4W,trait,".ctwas.res.RDS"))

param_4W <- ctwas_res_4W$param
finemap_res_4W <- ctwas_res_4W$finemap_res

p_conv_4W <- make_convergence_plots(param_4W, gwas_n, ncol = 1, colors = colors)
ctwas_parameters_4W <- summarize_param(param_4W, gwas_n)
pve_pie_4W <- plot_piechart(ctwas_parameters = ctwas_parameters_4W, colors = colors)

finemap_res_4W$molecular_id <- get_molecular_ids(finemap_res_4W)
finemap_res_4W <- anno_finemap_res(finemap_res_4W,
                              snp_map = snp_map_4W,
                              mapping_table = mapping_two,
                              add_gene_annot = TRUE,
                              map_by = "molecular_id",
                              drop_unmapped = TRUE,
                              add_position = TRUE,
                              use_gene_pos = "mid")

2024-10-14 10:09:19 INFO::Annotating fine-mapping result ...
2024-10-14 10:09:19 INFO::Map molecular traits to genes
2024-10-14 10:09:19 INFO::Split PIPs for molecular traits mapped to multiple genes
2024-10-14 10:09:23 INFO::Add gene positions
2024-10-14 10:09:24 INFO::Add SNP positions

combined_pip_by_type_4W <- combine_gene_pips_nocs(finemap_res =finemap_res_4W,
                                  group_by = "gene_name",
                                  by = "type", 
                                  method = "combine_cs",
                                  filter_cs = T )

2024-10-14 10:09:32 INFO::Limit gene results to credible sets

Parameters

print("each column represents one setting: predictdb e+s, predictdb e+s + Munro apa+rs, predictdb e+s + Munro st QTL")

[1] "each column represents one setting: predictdb e+s, predictdb e+s + Munro apa+rs, predictdb e+s + Munro st QTL"

grid.arrange(p_conv_espred,p_conv_4W,p_conv_ess, ncol = 3)

Past versions of unnamed-chunk-11-1.png

Version	Author	Date
06c5331	XSun	2024-10-14

######pve
group_pve_espred <- ctwas_parameters_espred$group_pve
group_pve_espred <- group_pve_espred[-length(group_pve_espred)]
group_pve_espred <- c(group_pve_espred, rep(NA,3))
group_pve_espred <- c(group_pve_espred, ctwas_parameters_espred$total_pve)
names(group_pve_espred) <- c("eQTL_pred","sQTL_pred","apaQTL_munro","rsQTL_munro","stQTL_munro","TOTAL")

group_pve_4W <- ctwas_parameters_4W$group_pve
group_pve_4W <- group_pve_4W[-length(group_pve_4W)]
group_pve_4W <- c(group_pve_4W, rep(NA,1))
group_pve_4W <- c(group_pve_4W, ctwas_parameters_4W$total_pve)
names(group_pve_4W) <- c("eQTL_pred","sQTL_pred","apaQTL_munro","rsQTL_munro","stQTL_munro","TOTAL")

group_pve_ess <- ctwas_parameters_ess$group_pve
group_pve_ess <- group_pve_ess[-length(group_pve_ess)]
group_pve_ess <- c(group_pve_ess[1:2], rep(NA,2),group_pve_ess[3])
group_pve_ess <- c(group_pve_ess, ctwas_parameters_ess$total_pve)
names(group_pve_ess) <- c("eQTL_pred","sQTL_pred","apaQTL_munro","rsQTL_munro","stQTL_munro","TOTAL")

grouppve <- cbind(group_pve_espred,group_pve_4W,group_pve_ess)
grouppve <- round(grouppve,digits = 4)

######size
group_size_espred <- ctwas_parameters_espred$group_size
group_size_espred <- group_size_espred[-length(group_size_espred)]
group_size_espred <- c(group_size_espred, rep(NA,3))
names(group_size_espred) <- c("eQTL_pred","sQTL_pred","apaQTL_munro","rsQTL_munro","stQTL_munro")

group_size_4W <- ctwas_parameters_4W$group_size
group_size_4W <- group_size_4W[-length(group_size_4W)]
group_size_4W <- c(group_size_4W, rep(NA,1))
names(group_size_4W) <-  c("eQTL_pred","sQTL_pred","apaQTL_munro","rsQTL_munro","stQTL_munro")

group_size_ess <- ctwas_parameters_ess$group_size
group_size_ess <- group_size_ess[-length(group_size_ess)]
group_size_ess <- c(group_size_ess[1:2], rep(NA,2),group_size_ess[3])
names(group_size_ess) <- c("eQTL_pred","sQTL_pred","apaQTL_munro","rsQTL_munro","stQTL_munro")

groupsize <- cbind(group_size_espred,group_size_4W,group_size_ess)

group_info <- cbind(grouppve,rbind(groupsize,c(rep(NA,3))))

DT::datatable(group_info,caption = htmltools::tags$caption( style = 'caption-side: topleft; text-align = left; color:black;','Group PVE and Group Size'),options = list(pageLength = 10) )

print("each pie chart represents one setting: predictdb e+s, predictdb e+s + Munro apa+rs, predictdb e+s + Munro st QTL")

[1] "each pie chart represents one setting: predictdb e+s, predictdb e+s + Munro apa+rs, predictdb e+s + Munro st QTL"

grid.arrange(pve_pie_espred,pve_pie_4W,pve_pie_ess, ncol =3)

Past versions of unnamed-chunk-11-3.png

Version	Author	Date
06c5331	XSun	2024-10-14

Fine-mapping results

combined_sig_espred <- combined_pip_by_type_espred[combined_pip_by_type_espred$combined_pip > 0.8,]
combined_sig_4W <- combined_pip_by_type_4W[combined_pip_by_type_4W$combined_pip > 0.8,]
combined_sig_ess <- combined_pip_by_type_ess[combined_pip_by_type_ess$combined_pip > 0.8,]

sprintf("# of genes with PIP > 0.8 = %s -- predictdb e +s", nrow(combined_sig_espred))

[1] "# of genes with PIP > 0.8 = 17 -- predictdb e +s"

sprintf("# of genes with PIP > 0.8 = %s -- predictdb e + s + Munro apa + rs", nrow(combined_sig_4W))

[1] "# of genes with PIP > 0.8 = 24 -- predictdb e + s + Munro apa + rs"

sprintf("# of genes with PIP > 0.8 = %s -- predictdb e+s + Munro st QTL", nrow(combined_sig_ess))

[1] "# of genes with PIP > 0.8 = 24 -- predictdb e+s + Munro st QTL"

venn.plot <- plot_3venn(es = combined_sig_espred$gene_name,esra = combined_sig_4W$gene_name,ess = combined_sig_ess$gene_name)

Past versions of unnamed-chunk-12-1.png

Version	Author	Date
06c5331	XSun	2024-10-14

###1
heatmap_data <- combined_sig_ess[!combined_sig_ess$gene_name %in%combined_sig_espred$gene_name, ]
column_order <- c("gene_name","combined_pip",
                  "eQTL_pip", "sQTL_pip", "stQTL_pip")
heatmap_data <- rename_heatmap_columns(heatmap_data = heatmap_data, column_order = column_order)
p1 <- plot_heatmap(heatmap_data = heatmap_data,main = "PIP partition for the genes reported by e + s + st setting but not by e+s setting")

###2
heatmap_data <- combined_sig_4W[!combined_sig_4W$gene_name %in%combined_sig_ess$gene_name, ]
column_order <- c("gene_name","combined_pip",
                  "eQTL_pip", "sQTL_pip", "rsQTL_pip","apaQTL_pip")
heatmap_data <- rename_heatmap_columns(heatmap_data = heatmap_data, column_order = column_order)
p2 <- plot_heatmap(heatmap_data = heatmap_data,main = "PIP partition for the genes reported by e + s + apa + rs setting but not by e+s+st setting")

###3
heatmap_data <- combined_sig_ess[!combined_sig_ess$gene_name %in%combined_sig_4W$gene_name, ]
column_order <- c("gene_name","combined_pip",
                  "eQTL_pip", "sQTL_pip", "stQTL_pip")
heatmap_data <- rename_heatmap_columns(heatmap_data = heatmap_data, column_order = column_order)


p3 <- plot_heatmap(heatmap_data = heatmap_data,main = "PIP partition for the genes reported by e + s + st setting but not by e + s + apa + rs setting")

g1 <- p1$gtable
g2 <- p2$gtable
g3 <- p3$gtable
grid.arrange(g1, g2, g3, ncol=3)

Past versions of unnamed-chunk-13-1.png

Version	Author	Date
06c5331	XSun	2024-10-14

SBP-ukb-a-360

trait <- "SBP-ukb-a-360"
tissue <- "Artery_Tibial"

gwas_n <- samplesize[trait]

results_dir_ess <- paste0("/project/xinhe/xsun/multi_group_ctwas/9.deciding_weights_4traits/results/",trait,"/ess/")

snp_map_ess <- readRDS(paste0(results_dir_ess,trait,".snp_map.RDS"))
ctwas_res_ess <- readRDS(paste0(results_dir_ess,trait,".ctwas.res.RDS"))

param_ess <- ctwas_res_ess$param
finemap_res_ess <- ctwas_res_ess$finemap_res


p_conv_ess <- make_convergence_plots(param_ess, gwas_n, ncol = 1, colors = colors)
ctwas_parameters_ess <- summarize_param(param_ess, gwas_n)
pve_pie_ess <- plot_piechart(ctwas_parameters = ctwas_parameters_ess, colors = colors)

susie_alpha_res_ess <- ctwas_res_ess$susie_alpha_res

susie_alpha_res_ess <- anno_susie_alpha_res(susie_alpha_res_ess,
                                        mapping_table = mapping_two,
                                        map_by = "molecular_id",
                                        drop_unmapped = TRUE)

2024-10-14 10:10:02 INFO::Annotating susie alpha result ...
2024-10-14 10:10:02 INFO::Map molecular traits to genes
2024-10-14 10:10:02 INFO::Split PIPs for molecular traits mapped to multiple genes

combined_pip_by_type_ess <- combine_gene_pips(susie_alpha_res_ess, 
                                             group_by = "gene_name",
                                             by = "type",
                                             method = "combine_cs",
                                             filter_cs = TRUE,
                                             include_cs_id = F)

results_dir_espred <- paste0("/project/xinhe/xsun/multi_group_ctwas/9.deciding_weights_4traits/results/",trait,"/espred/")

snp_map_espred <- readRDS(paste0(results_dir_espred,trait,".snp_map.RDS"))
ctwas_res_espred <- readRDS(paste0(results_dir_espred,trait,".ctwas.res.RDS"))

param_espred <- ctwas_res_espred$param
finemap_res_espred <- ctwas_res_espred$finemap_res


p_conv_espred <- make_convergence_plots(param_espred, gwas_n, ncol = 1, colors = colors)
ctwas_parameters_espred <- summarize_param(param_espred, gwas_n)
pve_pie_espred <- plot_piechart(ctwas_parameters = ctwas_parameters_espred, colors = colors)


finemap_res_espred$molecular_id <- get_molecular_ids(finemap_res_espred)
finemap_res_espred <- anno_finemap_res(finemap_res_espred,
                              snp_map = snp_map_espred,
                              mapping_table = mapping_two,
                              add_gene_annot = TRUE,
                              map_by = "molecular_id",
                              drop_unmapped = TRUE,
                              add_position = TRUE,
                              use_gene_pos = "mid")

2024-10-14 10:10:30 INFO::Annotating fine-mapping result ...
2024-10-14 10:10:30 INFO::Map molecular traits to genes
2024-10-14 10:10:31 INFO::Split PIPs for molecular traits mapped to multiple genes
2024-10-14 10:10:35 INFO::Add gene positions
2024-10-14 10:10:35 INFO::Add SNP positions

combined_pip_by_type_espred <- combine_gene_pips_nocs(finemap_res =finemap_res_espred,
                                  group_by = "gene_name",
                                  by = "type", 
                                  method = "combine_cs",
                                  filter_cs = T )

2024-10-14 10:10:41 INFO::Limit gene results to credible sets

results_dir_4W <- paste0("/project/xinhe/xsun/multi_group_ctwas/9.deciding_weights_4traits/results/",trait,"/4W/")

snp_map_4W <- readRDS(paste0(results_dir_4W,trait,".snp_map.RDS"))
ctwas_res_4W <- readRDS(paste0(results_dir_4W,trait,".ctwas.res.RDS"))

param_4W <- ctwas_res_4W$param
finemap_res_4W <- ctwas_res_4W$finemap_res

p_conv_4W <- make_convergence_plots(param_4W, gwas_n, ncol = 1, colors = colors)
ctwas_parameters_4W <- summarize_param(param_4W, gwas_n)
pve_pie_4W <- plot_piechart(ctwas_parameters = ctwas_parameters_4W, colors = colors)

finemap_res_4W$molecular_id <- get_molecular_ids(finemap_res_4W)
finemap_res_4W <- anno_finemap_res(finemap_res_4W,
                              snp_map = snp_map_4W,
                              mapping_table = mapping_two,
                              add_gene_annot = TRUE,
                              map_by = "molecular_id",
                              drop_unmapped = TRUE,
                              add_position = TRUE,
                              use_gene_pos = "mid")

2024-10-14 10:11:03 INFO::Annotating fine-mapping result ...
2024-10-14 10:11:03 INFO::Map molecular traits to genes
2024-10-14 10:11:05 INFO::Split PIPs for molecular traits mapped to multiple genes
2024-10-14 10:11:09 INFO::Add gene positions
2024-10-14 10:11:09 INFO::Add SNP positions

combined_pip_by_type_4W <- combine_gene_pips_nocs(finemap_res =finemap_res_4W,
                                  group_by = "gene_name",
                                  by = "type", 
                                  method = "combine_cs",
                                  filter_cs = T )

2024-10-14 10:11:19 INFO::Limit gene results to credible sets

Parameters

print("each column represents one setting: predictdb e+s, predictdb e+s + Munro apa+rs, predictdb e+s + Munro st QTL")

[1] "each column represents one setting: predictdb e+s, predictdb e+s + Munro apa+rs, predictdb e+s + Munro st QTL"

grid.arrange(p_conv_espred,p_conv_4W,p_conv_ess, ncol = 3)

Past versions of unnamed-chunk-17-1.png

Version	Author	Date
06c5331	XSun	2024-10-14

######pve
group_pve_espred <- ctwas_parameters_espred$group_pve
group_pve_espred <- group_pve_espred[-length(group_pve_espred)]
group_pve_espred <- c(group_pve_espred, rep(NA,3))
group_pve_espred <- c(group_pve_espred, ctwas_parameters_espred$total_pve)
names(group_pve_espred) <- c("eQTL_pred","sQTL_pred","apaQTL_munro","rsQTL_munro","stQTL_munro","TOTAL")

group_pve_4W <- ctwas_parameters_4W$group_pve
group_pve_4W <- group_pve_4W[-length(group_pve_4W)]
group_pve_4W <- c(group_pve_4W, rep(NA,1))
group_pve_4W <- c(group_pve_4W, ctwas_parameters_4W$total_pve)
names(group_pve_4W) <- c("eQTL_pred","sQTL_pred","apaQTL_munro","rsQTL_munro","stQTL_munro","TOTAL")

group_pve_ess <- ctwas_parameters_ess$group_pve
group_pve_ess <- group_pve_ess[-length(group_pve_ess)]
group_pve_ess <- c(group_pve_ess[1:2], rep(NA,2),group_pve_ess[3])
group_pve_ess <- c(group_pve_ess, ctwas_parameters_ess$total_pve)
names(group_pve_ess) <- c("eQTL_pred","sQTL_pred","apaQTL_munro","rsQTL_munro","stQTL_munro","TOTAL")

grouppve <- cbind(group_pve_espred,group_pve_4W,group_pve_ess)
grouppve <- round(grouppve,digits = 4)

######size
group_size_espred <- ctwas_parameters_espred$group_size
group_size_espred <- group_size_espred[-length(group_size_espred)]
group_size_espred <- c(group_size_espred, rep(NA,3))
names(group_size_espred) <- c("eQTL_pred","sQTL_pred","apaQTL_munro","rsQTL_munro","stQTL_munro")

group_size_4W <- ctwas_parameters_4W$group_size
group_size_4W <- group_size_4W[-length(group_size_4W)]
group_size_4W <- c(group_size_4W, rep(NA,1))
names(group_size_4W) <-  c("eQTL_pred","sQTL_pred","apaQTL_munro","rsQTL_munro","stQTL_munro")

group_size_ess <- ctwas_parameters_ess$group_size
group_size_ess <- group_size_ess[-length(group_size_ess)]
group_size_ess <- c(group_size_ess[1:2], rep(NA,2),group_size_ess[3])
names(group_size_ess) <- c("eQTL_pred","sQTL_pred","apaQTL_munro","rsQTL_munro","stQTL_munro")

groupsize <- cbind(group_size_espred,group_size_4W,group_size_ess)

group_info <- cbind(grouppve,rbind(groupsize,c(rep(NA,3))))

DT::datatable(group_info,caption = htmltools::tags$caption( style = 'caption-side: topleft; text-align = left; color:black;','Group PVE and Group Size'),options = list(pageLength = 10) )

print("each pie chart represents one setting: predictdb e+s, predictdb e+s + Munro apa+rs, predictdb e+s + Munro st QTL")

[1] "each pie chart represents one setting: predictdb e+s, predictdb e+s + Munro apa+rs, predictdb e+s + Munro st QTL"

grid.arrange(pve_pie_espred,pve_pie_4W,pve_pie_ess, ncol =3)

Past versions of unnamed-chunk-17-3.png

Version	Author	Date
06c5331	XSun	2024-10-14

Fine-mapping results

combined_sig_espred <- combined_pip_by_type_espred[combined_pip_by_type_espred$combined_pip > 0.8,]
combined_sig_4W <- combined_pip_by_type_4W[combined_pip_by_type_4W$combined_pip > 0.8,]
combined_sig_ess <- combined_pip_by_type_ess[combined_pip_by_type_ess$combined_pip > 0.8,]

sprintf("# of genes with PIP > 0.8 = %s -- predictdb e +s", nrow(combined_sig_espred))

[1] "# of genes with PIP > 0.8 = 38 -- predictdb e +s"

sprintf("# of genes with PIP > 0.8 = %s -- predictdb e + s + Munro apa + rs", nrow(combined_sig_4W))

[1] "# of genes with PIP > 0.8 = 46 -- predictdb e + s + Munro apa + rs"

sprintf("# of genes with PIP > 0.8 = %s -- predictdb e+s + Munro st QTL", nrow(combined_sig_ess))

[1] "# of genes with PIP > 0.8 = 41 -- predictdb e+s + Munro st QTL"

venn.plot <- plot_3venn(es = combined_sig_espred$gene_name,esra = combined_sig_4W$gene_name,ess = combined_sig_ess$gene_name)

Past versions of unnamed-chunk-18-1.png

Version	Author	Date
06c5331	XSun	2024-10-14

###1
heatmap_data <- combined_sig_ess[!combined_sig_ess$gene_name %in%combined_sig_espred$gene_name, ]
column_order <- c("gene_name","combined_pip",
                  "eQTL_pip", "sQTL_pip", "stQTL_pip")
heatmap_data <- rename_heatmap_columns(heatmap_data = heatmap_data, column_order = column_order)
p1 <- plot_heatmap(heatmap_data = heatmap_data,main = "PIP partition for the genes reported by e + s + st setting but not by e+s setting")

###2
heatmap_data <- combined_sig_4W[!combined_sig_4W$gene_name %in%combined_sig_ess$gene_name, ]
column_order <- c("gene_name","combined_pip",
                  "eQTL_pip", "sQTL_pip", "rsQTL_pip","apaQTL_pip")
heatmap_data <- rename_heatmap_columns(heatmap_data = heatmap_data, column_order = column_order)
p2 <- plot_heatmap(heatmap_data = heatmap_data,main = "PIP partition for the genes reported by e + s + apa + rs setting but not by e+s+st setting")

###3
heatmap_data <- combined_sig_ess[!combined_sig_ess$gene_name %in%combined_sig_4W$gene_name, ]
column_order <- c("gene_name","combined_pip",
                  "eQTL_pip", "sQTL_pip", "stQTL_pip")
heatmap_data <- rename_heatmap_columns(heatmap_data = heatmap_data, column_order = column_order)


p3 <- plot_heatmap(heatmap_data = heatmap_data,main = "PIP partition for the genes reported by e + s + st setting but not by e + s + apa + rs setting")

g1 <- p1$gtable
g2 <- p2$gtable
g3 <- p3$gtable
grid.arrange(g1, g2, g3, ncol=3)

Past versions of unnamed-chunk-19-1.png

Version	Author	Date
06c5331	XSun	2024-10-14

WBC-ieu-b-30

trait <- "WBC-ieu-b-30"
tissue <- "Whole_Blood"

gwas_n <- samplesize[trait]

results_dir_ess <- paste0("/project/xinhe/xsun/multi_group_ctwas/9.deciding_weights_4traits/results/",trait,"/ess/")

snp_map_ess <- readRDS(paste0(results_dir_ess,trait,".snp_map.RDS"))
ctwas_res_ess <- readRDS(paste0(results_dir_ess,trait,".ctwas.res.RDS"))

param_ess <- ctwas_res_ess$param
finemap_res_ess <- ctwas_res_ess$finemap_res


p_conv_ess <- make_convergence_plots(param_ess, gwas_n, ncol = 1, colors = colors)
ctwas_parameters_ess <- summarize_param(param_ess, gwas_n)
pve_pie_ess <- plot_piechart(ctwas_parameters = ctwas_parameters_ess, colors = colors)

susie_alpha_res_ess <- ctwas_res_ess$susie_alpha_res

susie_alpha_res_ess <- anno_susie_alpha_res(susie_alpha_res_ess,
                                        mapping_table = mapping_two,
                                        map_by = "molecular_id",
                                        drop_unmapped = TRUE)

2024-10-14 10:11:56 INFO::Annotating susie alpha result ...
2024-10-14 10:11:56 INFO::Map molecular traits to genes
2024-10-14 10:11:56 INFO::Split PIPs for molecular traits mapped to multiple genes

combined_pip_by_type_ess <- combine_gene_pips(susie_alpha_res_ess, 
                                             group_by = "gene_name",
                                             by = "type",
                                             method = "combine_cs",
                                             filter_cs = TRUE,
                                             include_cs_id = F)

results_dir_espred <- paste0("/project/xinhe/xsun/multi_group_ctwas/9.deciding_weights_4traits/results/",trait,"/espred/")

snp_map_espred <- readRDS(paste0(results_dir_espred,trait,".snp_map.RDS"))
ctwas_res_espred <- readRDS(paste0(results_dir_espred,trait,".ctwas.res.RDS"))

param_espred <- ctwas_res_espred$param
finemap_res_espred <- ctwas_res_espred$finemap_res


p_conv_espred <- make_convergence_plots(param_espred, gwas_n, ncol = 1, colors = colors)
ctwas_parameters_espred <- summarize_param(param_espred, gwas_n)
pve_pie_espred <- plot_piechart(ctwas_parameters = ctwas_parameters_espred, colors = colors)


finemap_res_espred$molecular_id <- get_molecular_ids(finemap_res_espred)
finemap_res_espred <- anno_finemap_res(finemap_res_espred,
                              snp_map = snp_map_espred,
                              mapping_table = mapping_two,
                              add_gene_annot = TRUE,
                              map_by = "molecular_id",
                              drop_unmapped = TRUE,
                              add_position = TRUE,
                              use_gene_pos = "mid")

2024-10-14 10:12:28 INFO::Annotating fine-mapping result ...
2024-10-14 10:12:28 INFO::Map molecular traits to genes
2024-10-14 10:12:29 INFO::Split PIPs for molecular traits mapped to multiple genes
2024-10-14 10:12:43 INFO::Add gene positions
2024-10-14 10:12:43 INFO::Add SNP positions

combined_pip_by_type_espred <- combine_gene_pips_nocs(finemap_res =finemap_res_espred,
                                  group_by = "gene_name",
                                  by = "type", 
                                  method = "combine_cs",
                                  filter_cs = T )

2024-10-14 10:12:56 INFO::Limit gene results to credible sets

results_dir_4W <- paste0("/project/xinhe/xsun/multi_group_ctwas/9.deciding_weights_4traits/results/",trait,"/4W/")

snp_map_4W <- readRDS(paste0(results_dir_4W,trait,".snp_map.RDS"))
ctwas_res_4W <- readRDS(paste0(results_dir_4W,trait,".ctwas.res.RDS"))

param_4W <- ctwas_res_4W$param
finemap_res_4W <- ctwas_res_4W$finemap_res

p_conv_4W <- make_convergence_plots(param_4W, gwas_n, ncol = 1, colors = colors)
ctwas_parameters_4W <- summarize_param(param_4W, gwas_n)
pve_pie_4W <- plot_piechart(ctwas_parameters = ctwas_parameters_4W, colors = colors)

finemap_res_4W$molecular_id <- get_molecular_ids(finemap_res_4W)
finemap_res_4W <- anno_finemap_res(finemap_res_4W,
                              snp_map = snp_map_4W,
                              mapping_table = mapping_two,
                              add_gene_annot = TRUE,
                              map_by = "molecular_id",
                              drop_unmapped = TRUE,
                              add_position = TRUE,
                              use_gene_pos = "mid")

2024-10-14 10:13:24 INFO::Annotating fine-mapping result ...
2024-10-14 10:13:24 INFO::Map molecular traits to genes
2024-10-14 10:13:28 INFO::Split PIPs for molecular traits mapped to multiple genes
2024-10-14 10:13:35 INFO::Add gene positions
2024-10-14 10:13:35 INFO::Add SNP positions

combined_pip_by_type_4W <- combine_gene_pips_nocs(finemap_res =finemap_res_4W,
                                  group_by = "gene_name",
                                  by = "type", 
                                  method = "combine_cs",
                                  filter_cs = T )

2024-10-14 10:13:44 INFO::Limit gene results to credible sets

Parameters

print("each column represents one setting: predictdb e+s, predictdb e+s + Munro apa+rs, predictdb e+s + Munro st QTL")

[1] "each column represents one setting: predictdb e+s, predictdb e+s + Munro apa+rs, predictdb e+s + Munro st QTL"

grid.arrange(p_conv_espred,p_conv_4W,p_conv_ess, ncol = 3)

Past versions of unnamed-chunk-23-1.png

Version	Author	Date
06c5331	XSun	2024-10-14

######pve
group_pve_espred <- ctwas_parameters_espred$group_pve
group_pve_espred <- group_pve_espred[-length(group_pve_espred)]
group_pve_espred <- c(group_pve_espred, rep(NA,3))
group_pve_espred <- c(group_pve_espred, ctwas_parameters_espred$total_pve)
names(group_pve_espred) <- c("eQTL_pred","sQTL_pred","apaQTL_munro","rsQTL_munro","stQTL_munro","TOTAL")

group_pve_4W <- ctwas_parameters_4W$group_pve
group_pve_4W <- group_pve_4W[-length(group_pve_4W)]
group_pve_4W <- c(group_pve_4W, rep(NA,1))
group_pve_4W <- c(group_pve_4W, ctwas_parameters_4W$total_pve)
names(group_pve_4W) <- c("eQTL_pred","sQTL_pred","apaQTL_munro","rsQTL_munro","stQTL_munro","TOTAL")

group_pve_ess <- ctwas_parameters_ess$group_pve
group_pve_ess <- group_pve_ess[-length(group_pve_ess)]
group_pve_ess <- c(group_pve_ess[1:2], rep(NA,2),group_pve_ess[3])
group_pve_ess <- c(group_pve_ess, ctwas_parameters_ess$total_pve)
names(group_pve_ess) <- c("eQTL_pred","sQTL_pred","apaQTL_munro","rsQTL_munro","stQTL_munro","TOTAL")

grouppve <- cbind(group_pve_espred,group_pve_4W,group_pve_ess)
grouppve <- round(grouppve,digits = 4)

######size
group_size_espred <- ctwas_parameters_espred$group_size
group_size_espred <- group_size_espred[-length(group_size_espred)]
group_size_espred <- c(group_size_espred, rep(NA,3))
names(group_size_espred) <- c("eQTL_pred","sQTL_pred","apaQTL_munro","rsQTL_munro","stQTL_munro")

group_size_4W <- ctwas_parameters_4W$group_size
group_size_4W <- group_size_4W[-length(group_size_4W)]
group_size_4W <- c(group_size_4W, rep(NA,1))
names(group_size_4W) <-  c("eQTL_pred","sQTL_pred","apaQTL_munro","rsQTL_munro","stQTL_munro")

group_size_ess <- ctwas_parameters_ess$group_size
group_size_ess <- group_size_ess[-length(group_size_ess)]
group_size_ess <- c(group_size_ess[1:2], rep(NA,2),group_size_ess[3])
names(group_size_ess) <- c("eQTL_pred","sQTL_pred","apaQTL_munro","rsQTL_munro","stQTL_munro")

groupsize <- cbind(group_size_espred,group_size_4W,group_size_ess)

group_info <- cbind(grouppve,rbind(groupsize,c(rep(NA,3))))

DT::datatable(group_info,caption = htmltools::tags$caption( style = 'caption-side: topleft; text-align = left; color:black;','Group PVE and Group Size'),options = list(pageLength = 10) )

print("each pie chart represents one setting: predictdb e+s, predictdb e+s + Munro apa+rs, predictdb e+s + Munro st QTL")

[1] "each pie chart represents one setting: predictdb e+s, predictdb e+s + Munro apa+rs, predictdb e+s + Munro st QTL"

grid.arrange(pve_pie_espred,pve_pie_4W,pve_pie_ess, ncol =3)

Past versions of unnamed-chunk-23-3.png

Version	Author	Date
06c5331	XSun	2024-10-14

Fine-mapping results

combined_sig_espred <- combined_pip_by_type_espred[combined_pip_by_type_espred$combined_pip > 0.8,]
combined_sig_4W <- combined_pip_by_type_4W[combined_pip_by_type_4W$combined_pip > 0.8,]
combined_sig_ess <- combined_pip_by_type_ess[combined_pip_by_type_ess$combined_pip > 0.8,]

sprintf("# of genes with PIP > 0.8 = %s -- predictdb e +s", nrow(combined_sig_espred))

[1] "# of genes with PIP > 0.8 = 121 -- predictdb e +s"

sprintf("# of genes with PIP > 0.8 = %s -- predictdb e + s + Munro apa + rs", nrow(combined_sig_4W))

[1] "# of genes with PIP > 0.8 = 147 -- predictdb e + s + Munro apa + rs"

sprintf("# of genes with PIP > 0.8 = %s -- predictdb e+s + Munro st QTL", nrow(combined_sig_ess))

[1] "# of genes with PIP > 0.8 = 135 -- predictdb e+s + Munro st QTL"

venn.plot <- plot_3venn(es = combined_sig_espred$gene_name,esra = combined_sig_4W$gene_name,ess = combined_sig_ess$gene_name)

Past versions of unnamed-chunk-24-1.png

Version	Author	Date
06c5331	XSun	2024-10-14

###1
heatmap_data <- combined_sig_ess[!combined_sig_ess$gene_name %in%combined_sig_espred$gene_name, ]
column_order <- c("gene_name","combined_pip",
                  "eQTL_pip", "sQTL_pip", "stQTL_pip")
heatmap_data <- rename_heatmap_columns(heatmap_data = heatmap_data, column_order = column_order)
p1 <- plot_heatmap(heatmap_data = heatmap_data,main = "PIP partition for the genes reported by e + s + st setting but not by e+s setting")

###2
heatmap_data <- combined_sig_4W[!combined_sig_4W$gene_name %in%combined_sig_ess$gene_name, ]
column_order <- c("gene_name","combined_pip",
                  "eQTL_pip", "sQTL_pip", "rsQTL_pip","apaQTL_pip")
heatmap_data <- rename_heatmap_columns(heatmap_data = heatmap_data, column_order = column_order)
p2 <- plot_heatmap(heatmap_data = heatmap_data,main = "PIP partition for the genes reported by e + s + apa + rs setting but not by e+s+st setting")

###3
heatmap_data <- combined_sig_ess[!combined_sig_ess$gene_name %in%combined_sig_4W$gene_name, ]
column_order <- c("gene_name","combined_pip",
                  "eQTL_pip", "sQTL_pip", "stQTL_pip")
heatmap_data <- rename_heatmap_columns(heatmap_data = heatmap_data, column_order = column_order)


p3 <- plot_heatmap(heatmap_data = heatmap_data,main = "PIP partition for the genes reported by e + s + st setting but not by e + s + apa + rs setting")

g1 <- p1$gtable
g2 <- p2$gtable
g3 <- p3$gtable
grid.arrange(g1, g2, g3, ncol=3)

Past versions of unnamed-chunk-25-1.png

Version	Author	Date
06c5331	XSun	2024-10-14

Session information

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] 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       

attached base packages:
[1] grid      stats4    stats     graphics  grDevices utils     datasets 
[8] methods   base     

other attached packages:
 [1] logging_0.10-108          dplyr_1.1.4              
 [3] pheatmap_1.0.12           gridExtra_2.3            
 [5] ggplot2_3.5.1             VennDiagram_1.7.3        
 [7] futile.logger_1.4.3       EnsDb.Hsapiens.v86_2.99.0
 [9] ensembldb_2.20.2          AnnotationFilter_1.20.0  
[11] GenomicFeatures_1.48.3    AnnotationDbi_1.58.0     
[13] Biobase_2.56.0            GenomicRanges_1.48.0     
[15] GenomeInfoDb_1.39.9       IRanges_2.30.0           
[17] S4Vectors_0.34.0          BiocGenerics_0.42.0      
[19] ctwas_0.4.15             

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] fs_1.5.2                    rstudioapi_0.13            
  [9] farver_2.1.0                DT_0.22                    
 [11] ggrepel_0.9.1               bit64_4.0.5                
 [13] fansi_1.0.3                 xml2_1.3.3                 
 [15] codetools_0.2-18            cachem_1.0.6               
 [17] knitr_1.39                  jsonlite_1.8.0             
 [19] workflowr_1.7.0             Rsamtools_2.12.0           
 [21] dbplyr_2.1.1                png_0.1-7                  
 [23] readr_2.1.2                 compiler_4.2.0             
 [25] httr_1.4.3                  assertthat_0.2.1           
 [27] Matrix_1.5-3                fastmap_1.1.0              
 [29] lazyeval_0.2.2              cli_3.6.1                  
 [31] formatR_1.12                later_1.3.0                
 [33] htmltools_0.5.2             prettyunits_1.1.1          
 [35] tools_4.2.0                 gtable_0.3.0               
 [37] glue_1.6.2                  GenomeInfoDbData_1.2.8     
 [39] rappdirs_0.3.3              Rcpp_1.0.12                
 [41] jquerylib_0.1.4             vctrs_0.6.5                
 [43] Biostrings_2.64.0           rtracklayer_1.56.0         
 [45] crosstalk_1.2.0             xfun_0.41                  
 [47] stringr_1.5.1               lifecycle_1.0.4            
 [49] irlba_2.3.5                 restfulr_0.0.14            
 [51] XML_3.99-0.14               zlibbioc_1.42.0            
 [53] zoo_1.8-10                  scales_1.3.0               
 [55] gggrid_0.2-0                hms_1.1.1                  
 [57] promises_1.2.0.1            MatrixGenerics_1.8.0       
 [59] ProtGenerics_1.28.0         parallel_4.2.0             
 [61] SummarizedExperiment_1.26.1 RColorBrewer_1.1-3         
 [63] lambda.r_1.2.4              LDlinkR_1.2.3              
 [65] yaml_2.3.5                  curl_4.3.2                 
 [67] memoise_2.0.1               sass_0.4.1                 
 [69] biomaRt_2.54.1              stringi_1.7.6              
 [71] RSQLite_2.3.1               highr_0.9                  
 [73] BiocIO_1.6.0                filelock_1.0.2             
 [75] BiocParallel_1.30.3         rlang_1.1.2                
 [77] pkgconfig_2.0.3             matrixStats_0.62.0         
 [79] bitops_1.0-7                evaluate_0.15              
 [81] lattice_0.20-45             purrr_1.0.2                
 [83] labeling_0.4.2              GenomicAlignments_1.32.0   
 [85] htmlwidgets_1.5.4           cowplot_1.1.1              
 [87] bit_4.0.4                   tidyselect_1.2.0           
 [89] magrittr_2.0.3              R6_2.5.1                   
 [91] generics_0.1.2              DelayedArray_0.22.0        
 [93] DBI_1.2.2                   withr_2.5.0                
 [95] pgenlibr_0.3.3              pillar_1.9.0               
 [97] whisker_0.4                 KEGGREST_1.36.3            
 [99] RCurl_1.98-1.7              mixsqp_0.3-43              
[101] tibble_3.2.1                crayon_1.5.1               
[103] futile.options_1.0.1        utf8_1.2.2                 
[105] BiocFileCache_2.4.0         plotly_4.10.0              
[107] tzdb_0.4.0                  rmarkdown_2.25             
[109] progress_1.2.2              data.table_1.14.2          
[111] blob_1.2.3                  git2r_0.30.1               
[113] digest_0.6.29               tidyr_1.3.0                
[115] httpuv_1.6.5                munsell_0.5.0              
[117] viridisLite_0.4.0           bslib_0.3.1