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Introduction

We estimated the parameters for the e+s+apa model in this analysis. The apa component follows the approach described in this study https://www.nature.com/articles/s41588-021-00864-5. For each gene, we used the lead QTL to construct a PredictDB model.

library(ctwas)
library(ggplot2)
library(tidyverse)
library(dplyr)
library(EnsDb.Hsapiens.v86)
ens_db <- EnsDb.Hsapiens.v86

source("/project/xinhe/xsun/multi_group_ctwas/data/samplesize.R")
source("/project/xinhe/xsun/multi_group_ctwas/functions/0.functions.R")

folder_results_susieST <- "/project/xinhe/xsun/multi_group_ctwas/16.apa_li_weights/snakemake_outputs/"
folder_results_apaonly <- "/project/xinhe/xsun/multi_group_ctwas/16.apa_li_weights/snakemake_outputs_apaonly/"
folder_results_single <- "/project/xinhe/xsun/multi_group_ctwas/16.apa_li_weights/ctwas_output/apa/"


folder_results_susieST_susie <- "/project/xinhe/xsun/multi_group_ctwas/15.susie_weights/snakemake_outputs/"
folder_results_apaonly_susie <- "/project/xinhe/xsun/multi_group_ctwas/15.susie_weights/snakemake_outputs_marginaltissue/"
folder_results_single_susie <- "/project/xinhe/xsun/multi_group_ctwas/17.single_eQTL/ctwas_output/stability_weight_unscaled/"

# 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)

colors <- c("#ff7f0e", "#2ca02c", "#d62728",  "#9467bd", "#8c564b", "#e377c2", "#7f7f7f",  "#bcbd22",  "#17becf",  "#f7b6d2",  "#c5b0d5",  "#9edae5", "#ffbb78",  "#98df8a",  "#ff9896" )

top_tissues <- c("Liver","Whole_Blood","Brain_Cerebellar_Hemisphere","Adipose_Subcutaneous","Brain_Cerebellum","Heart_Atrial_Appendage","Pituitary")
traits <- c("LDL-ukb-d-30780_irnt","IBD-ebi-a-GCST004131","BMI-panukb","RBC-panukb","SCZ-ieu-b-5102","aFib-ebi-a-GCST006414","T2D-panukb")
names(top_tissues) <- traits


plot_piechart <- function(ctwas_parameters, colors, by, title) {
  # Create the initial data frame
  data <- data.frame(
    category = names(ctwas_parameters$prop_heritability),
    percentage = ctwas_parameters$prop_heritability
  )
  
  # Split the category into context and type
  data <- data %>%
    mutate(
      context = sub("\\|.*", "", category),
      type = sub(".*\\|", "", category)
    )
  
  # Aggregate the data based on the 'by' parameter
  if (by == "type") {
    data <- data %>%
      group_by(type) %>%
      summarize(percentage = sum(percentage)) %>%
      mutate(category = type)  # Use type as the new category
  } else if (by == "context") {
    data <- data %>%
      group_by(context) %>%
      summarize(percentage = sum(percentage)) %>%
      mutate(category = context)  # Use context as the new category
  } else {
    stop("Invalid 'by' parameter. Use 'type' or 'context'.")
  }
  
  # Calculate percentage labels for the chart
  data$percentage_label <- paste0(round(data$percentage * 100, 1), "%")
  
  # Create the pie chart
  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 = 3) +  # Adjust size as needed
    scale_fill_manual(values = colors) +  # Custom colors
    labs(fill = "Category") +  # Legend title
    ggtitle(title)  # Title
  
  return(pie)
}

plot_multi <- function(p1,p2,p3,title=NULL) {
  
  fix_panel_size <- function(plot, width = 2.1, height = 2) {
    set_panel_size(plot, width = unit(width, "in"), height = unit(height, "in"))
  }
  
  # Apply fixed panel size
  pie1 <- fix_panel_size(p1)
  pie2 <- fix_panel_size(p2)
  pie3 <- fix_panel_size(p3)
  
  # Compute natural widths
  widths <- unit.c(grobWidth(pie1), grobWidth(pie2), grobWidth(pie3))
  
  # Arrange
  p <- grid.arrange(pie1, pie2, pie3,
                    ncol = 3,
                    widths = widths,
                    top = title)
  
  return(p)
  
  
}

apaQTL summary

cis_files <- list.files(path = "/project2/xinhe/shared_data/multigroup_ctwas/weights/apa_li/",pattern = "cis.3aQTL.txt")

sum <- c()
for (file in cis_files){
  
  tissue <- gsub(pattern = ".cis.3aQTL.txt",replacement = "",x = file)
  
  cisdf <- data.table::fread(paste0("/project2/xinhe/shared_data/multigroup_ctwas/weights/apa_li/",file))
  
  cisdf$fdr <- p.adjust(as.numeric(cisdf$p.value), method = "fdr")
  cisdf_fdr005 <- cisdf[cisdf$fdr < 0.05,]
  count_df <- cisdf_fdr005[, .N, by = transcript]
  avg <- sum(count_df$N)/nrow(count_df)
  
  tmp <- c(tissue,avg,nrow(count_df))
  sum <- rbind(sum,tmp)
}

rownames(sum) <- NULL
colnames(sum) <- c("Tissue","avg_qtl_fdr005","num_gene")

DT::datatable(sum,caption = htmltools::tags$caption( style = 'caption-side: left; text-align: left; color:black;  font-size:150% ;','Average number of apaQTL per gene'),options = list(pageLength = 10) )

LDL-ukb-d-30780_irnt

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

gwas_n <- samplesize[trait]

%h2g for setting: shared_all, thin = 1

thin <- 1
var_struc <- "shared_all"
param_susieST <- readRDS(paste0(folder_results_susieST,"/",trait,"/",trait,".",st,".thin",thin,".",var_struc,".param.RDS"))


ctwas_parameters_susieST <- summarize_param(param_susieST, gwas_n)
total_nonSNPpve_susieST <- 1- ctwas_parameters_susieST$prop_heritability["SNP"]
pve_pie_by_type_susieST <- plot_piechart_topn(ctwas_parameters = ctwas_parameters_susieST, colors = colors, by = "type", title = "top-apa, multi-group")


param_apaonly <- readRDS(paste0(folder_results_apaonly,"/",trait,"/",trait,".",st,".thin",thin,".",var_struc,".param.RDS"))

ctwas_parameters_apaonly <- summarize_param(param_apaonly,gwas_n)
total_nonSNPpve_apaonly <- 1- ctwas_parameters_apaonly$prop_heritability["SNP"]
pve_pie_by_type_apaonly <- plot_piechart_topn(ctwas_parameters = ctwas_parameters_apaonly, colors = colors, by = "type", title = "top-apa, multi tissue apa")


top_tissue <- top_tissues[trait]
file_param_single <- paste0(folder_results_single, trait, "/", trait, "_", top_tissue, ".thin", thin, ".", var_struc, ".param.RDS")

param_single <- readRDS(file_param_single)
ctwas_parameters_single <- summarize_param(param_single, gwas_n)
total_nonSNPpve_single <- 1- ctwas_parameters_single$prop_heritability["SNP"]

pve_pie_by_type_single <- plot_piechart_topn(ctwas_parameters = ctwas_parameters_single, colors = colors, by = "context", title = "top-apa, single tissue apa")


plot_multi(pve_pie_by_type_single,pve_pie_by_type_apaonly,pve_pie_by_type_susieST, title=trait)

Version Author Date
278bbd9 XSun 2025-04-17
bda6e43 XSun 2025-04-09 
TableGrob (2 x 3) "arrange": 4 grobs
  z     cells    name                grob
1 1 (2-2,1-1) arrange      gtable[layout]
2 2 (2-2,2-2) arrange      gtable[layout]
3 3 (2-2,3-3) arrange      gtable[layout]
4 4 (1-1,1-3) arrange text[GRID.text.136]
param_susieST <- readRDS(paste0(folder_results_susieST_susie,"/",trait,"/",trait,".",st,".thin",thin,".",var_struc,".param.RDS"))


ctwas_parameters_susieST <- summarize_param(param_susieST, gwas_n)
total_nonSNPpve_susieST <- 1- ctwas_parameters_susieST$prop_heritability["SNP"]
pve_pie_by_type_susieST <- plot_piechart_topn(ctwas_parameters = ctwas_parameters_susieST, colors = colors, by = "type", title = "multi-group")


param_apaonly <- readRDS(paste0(folder_results_apaonly_susie,"/",trait,"/",trait,".",st,".thin",thin,".",var_struc,".param.RDS"))

ctwas_parameters_apaonly <- summarize_param(param_apaonly,gwas_n)
total_nonSNPpve_apaonly <- 1- ctwas_parameters_apaonly$prop_heritability["SNP"]
pve_pie_by_type_apaonly <- plot_piechart_topn(ctwas_parameters = ctwas_parameters_apaonly, colors = colors, by = "type", title = "multi tissue susie st")


top_tissue <- top_tissues[trait]
file_param_single <- paste0(folder_results_single_susie, trait, "/", trait, "_", top_tissue, ".thin", thin, ".", var_struc, ".param.RDS")

param_single <- readRDS(file_param_single)
ctwas_parameters_single <- summarize_param(param_single, gwas_n)
total_nonSNPpve_single <- 1- ctwas_parameters_single$prop_heritability["SNP"]

pve_pie_by_type_single <- plot_piechart_topn(ctwas_parameters = ctwas_parameters_single, colors = colors, by = "context", title = "single tissue susie st")


plot_multi(pve_pie_by_type_single,pve_pie_by_type_apaonly,pve_pie_by_type_susieST, title=trait)

TableGrob (2 x 3) "arrange": 4 grobs
  z     cells    name                grob
1 1 (2-2,1-1) arrange      gtable[layout]
2 2 (2-2,2-2) arrange      gtable[layout]
3 3 (2-2,3-3) arrange      gtable[layout]
4 4 (1-1,1-3) arrange text[GRID.text.275]

IBD-ebi-a-GCST004131

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

gwas_n <- samplesize[trait]

%h2g for setting: shared_all, thin = 1

thin <- 1
var_struc <- "shared_all"
param_susieST <- readRDS(paste0(folder_results_susieST,"/",trait,"/",trait,".",st,".thin",thin,".",var_struc,".param.RDS"))


ctwas_parameters_susieST <- summarize_param(param_susieST, gwas_n)
total_nonSNPpve_susieST <- 1- ctwas_parameters_susieST$prop_heritability["SNP"]
pve_pie_by_type_susieST <- plot_piechart_topn(ctwas_parameters = ctwas_parameters_susieST, colors = colors, by = "type", title = "top-apa, multi-group")


param_apaonly <- readRDS(paste0(folder_results_apaonly,"/",trait,"/",trait,".",st,".thin",thin,".",var_struc,".param.RDS"))

ctwas_parameters_apaonly <- summarize_param(param_apaonly,gwas_n)
total_nonSNPpve_apaonly <- 1- ctwas_parameters_apaonly$prop_heritability["SNP"]
pve_pie_by_type_apaonly <- plot_piechart_topn(ctwas_parameters = ctwas_parameters_apaonly, colors = colors, by = "type", title = "top-apa, multi tissue apa")


top_tissue <- top_tissues[trait]
file_param_single <- paste0(folder_results_single, trait, "/", trait, "_", top_tissue, ".thin", thin, ".", var_struc, ".param.RDS")

param_single <- readRDS(file_param_single)
ctwas_parameters_single <- summarize_param(param_single, gwas_n)
total_nonSNPpve_single <- 1- ctwas_parameters_single$prop_heritability["SNP"]

pve_pie_by_type_single <- plot_piechart_topn(ctwas_parameters = ctwas_parameters_single, colors = colors, by = "context", title = "top-apa, single tissue apa")


plot_multi(pve_pie_by_type_single,pve_pie_by_type_apaonly,pve_pie_by_type_susieST, title=trait)

Version Author Date
bda6e43 XSun 2025-04-09 
TableGrob (2 x 3) "arrange": 4 grobs
  z     cells    name                grob
1 1 (2-2,1-1) arrange      gtable[layout]
2 2 (2-2,2-2) arrange      gtable[layout]
3 3 (2-2,3-3) arrange      gtable[layout]
4 4 (1-1,1-3) arrange text[GRID.text.414]
param_susieST <- readRDS(paste0(folder_results_susieST_susie,"/",trait,"/",trait,".",st,".thin",thin,".",var_struc,".param.RDS"))


ctwas_parameters_susieST <- summarize_param(param_susieST, gwas_n)
total_nonSNPpve_susieST <- 1- ctwas_parameters_susieST$prop_heritability["SNP"]
pve_pie_by_type_susieST <- plot_piechart_topn(ctwas_parameters = ctwas_parameters_susieST, colors = colors, by = "type", title = "multi-group")


param_apaonly <- readRDS(paste0(folder_results_apaonly_susie,"/",trait,"/",trait,".",st,".thin",thin,".",var_struc,".param.RDS"))

ctwas_parameters_apaonly <- summarize_param(param_apaonly,gwas_n)
total_nonSNPpve_apaonly <- 1- ctwas_parameters_apaonly$prop_heritability["SNP"]
pve_pie_by_type_apaonly <- plot_piechart_topn(ctwas_parameters = ctwas_parameters_apaonly, colors = colors, by = "type", title = "multi tissue susie st")


top_tissue <- top_tissues[trait]
file_param_single <- paste0(folder_results_single_susie, trait, "/", trait, "_", top_tissue, ".thin", thin, ".", var_struc, ".param.RDS")

param_single <- readRDS(file_param_single)
ctwas_parameters_single <- summarize_param(param_single, gwas_n)
total_nonSNPpve_single <- 1- ctwas_parameters_single$prop_heritability["SNP"]

pve_pie_by_type_single <- plot_piechart_topn(ctwas_parameters = ctwas_parameters_single, colors = colors, by = "context", title = "single tissue susie st")


plot_multi(pve_pie_by_type_single,pve_pie_by_type_apaonly,pve_pie_by_type_susieST, title=trait)

Version Author Date
6e49457 XSun 2025-04-17 
TableGrob (2 x 3) "arrange": 4 grobs
  z     cells    name                grob
1 1 (2-2,1-1) arrange      gtable[layout]
2 2 (2-2,2-2) arrange      gtable[layout]
3 3 (2-2,3-3) arrange      gtable[layout]
4 4 (1-1,1-3) arrange text[GRID.text.553]

gene level results – whole blood

results_li <- read.table("/project/xinhe/xsun/multi_group_ctwas/16.apa_li_weights/data/IBD_genes_li.txt", header = T)

tissues_target <- c("Whole_Blood")
results_li_overlaptissue <- results_li[results_li$Tissue %in% tissues_target,]

ctwas_res <- readRDS("/project/xinhe/xsun/multi_group_ctwas/16.apa_li_weights/ctwas_output/apa//IBD-ebi-a-GCST004131/IBD-ebi-a-GCST004131_Whole_Blood.thin1.shared_all.L5.finemap_regions_res.RDS")

mapping_table <- readRDS("/project2/xinhe/shared_data/multigroup_ctwas/weights/mapping_files/apa_li.RDS")

susie_alpha_res <- ctwas_res$susie_alpha_res
susie_alpha_res$molecular_id <- sub("\\|[^|]*$", "", susie_alpha_res$id)
susie_alpha_res <- anno_susie_alpha_res(susie_alpha_res,
                                        mapping_table = mapping_table,
                                        map_by = "molecular_id",
                                        drop_unmapped = F)
2025-04-25 09:37:25 INFO::Annotating susie alpha result ...
2025-04-25 09:37:25 INFO::Map molecular traits to genes
susie_alpha_res_uniq <- susie_alpha_res[!duplicated(susie_alpha_res$id),]

susie_summary <- susie_alpha_res_uniq %>%
  group_by(gene_name) %>%
  summarise(
    susie_z = mean(z, na.rm = TRUE),             # or median(z) if you prefer
    susie_pip = max(susie_pip, na.rm = TRUE),     # max to reflect strongest evidence
    region_id = region_id,
    id = id,
  )

# Step 2: Merge with results_li
results_li_merged <- results_li_overlaptissue %>%
  left_join(susie_summary, by = c("GeneName" = "gene_name"))

DT::datatable(results_li_merged,caption = htmltools::tags$caption( style = 'caption-side: left; text-align: left; color:black;  font-size:150% ;',''),options = list(pageLength = 10) )
results_li_merged <- results_li_merged[complete.cases(results_li_merged$region_id),]


weights <- readRDS("/project/xinhe/xsun/multi_group_ctwas/16.apa_li_weights/ctwas_output/apa//IBD-ebi-a-GCST004131/IBD-ebi-a-GCST004131_Whole_Blood.preprocessed.weights.ST.RDS")
snp_map <- readRDS("/project2/xinhe/shared_data/multigroup_ctwas/LD_region_info/snp_map.RDS")


finemap_res <- ctwas_res$finemap_res
finemap_res$molecular_id <- get_molecular_ids(finemap_res)
finemap_res <- anno_finemap_res(finemap_res,
                                snp_map = snp_map,
                                mapping_table = mapping_table,
                                add_gene_annot = TRUE,
                                map_by = "molecular_id",
                                drop_unmapped = TRUE,
                                add_position = TRUE,
                                use_gene_pos = "mid")
2025-04-25 09:37:43 INFO::Annotating fine-mapping result ...
2025-04-25 09:37:45 INFO::Map molecular traits to genes
2025-04-25 09:37:45 INFO::Drop 966 unmapped molecular traits
2025-04-25 09:38:00 INFO::Add gene positions
2025-04-25 09:38:00 INFO::Add SNP positions
for (i in 1:nrow(results_li_merged)) {
  
  region_id <- results_li_merged$region_id[i]
  
  p <- make_locusplot(finemap_res,
               region_id = region_id,
               ens_db = ens_db,
               weights = weights,
               highlight_pip = 0.8,
               filter_protein_coding_genes = TRUE,
               filter_cs = F,
               focal_id = results_li_merged$id[i],
               color_pval_by = "cs",
               color_pip_by = "cs")
  
  print(p)
  
}
2025-04-25 09:38:11 INFO::Limit to protein coding genes
2025-04-25 09:38:11 INFO::focal id: NM_181698|CCNY|chr10|+|Whole_Blood_stQTL
2025-04-25 09:38:11 INFO::focal molecular trait:
2025-04-25 09:38:11 INFO::Range of locus: chr10:34820327-36282921
2025-04-25 09:38:12 INFO::focal molecular trait QTL positions: 35456424

2025-04-25 09:38:12 INFO::Limit to protein coding genes
2025-04-25 09:38:12 INFO::focal id: NM_005253|FOSL2|chr2|+|Whole_Blood_stQTL
2025-04-25 09:38:12 INFO::focal molecular trait:
2025-04-25 09:38:12 INFO::Range of locus: chr2:28376782-28994103
2025-04-25 09:38:13 INFO::focal molecular trait QTL positions: 28412873

2025-04-25 09:38:14 INFO::Limit to protein coding genes
2025-04-25 09:38:14 INFO::focal id: NM_022349|MS4A6A|chr11|-|Whole_Blood_stQTL
2025-04-25 09:38:14 INFO::focal molecular trait:
2025-04-25 09:38:14 INFO::Range of locus: chr11:59012976-62455196
2025-04-25 09:38:14 INFO::focal molecular trait QTL positions: 60200909

2025-04-25 09:38:16 INFO::Limit to protein coding genes
2025-04-25 09:38:16 INFO::focal id: NM_152851|MS4A6A|chr11|-|Whole_Blood_stQTL
2025-04-25 09:38:16 INFO::focal molecular trait:
2025-04-25 09:38:16 INFO::Range of locus: chr11:59012976-62455196
2025-04-25 09:38:16 INFO::focal molecular trait QTL positions: 60088555

2025-04-25 09:38:19 INFO::Limit to protein coding genes
2025-04-25 09:38:19 INFO::focal id: NM_177939|P4HTM|chr3|+|Whole_Blood_stQTL
2025-04-25 09:38:19 INFO::focal molecular trait:
2025-04-25 09:38:19 INFO::Range of locus: chr3:49279805-51794719
2025-04-25 09:38:19 INFO::focal molecular trait QTL positions: 49970685

2025-04-25 09:38:20 INFO::Limit to protein coding genes
2025-04-25 09:38:20 INFO::focal id: NM_053055|THEM4|chr1|-|Whole_Blood_stQTL
2025-04-25 09:38:20 INFO::focal molecular trait:
2025-04-25 09:38:20 INFO::Range of locus: chr1:151566589-153207547
2025-04-25 09:38:20 INFO::focal molecular trait QTL positions: 151865616

T2D-panukb

trait <- "T2D-panukb"
st <- "with_susieST"

gwas_n <- samplesize[trait]

%h2g for setting: shared_all, thin = 1

thin <- 1
var_struc <- "shared_all"
param_susieST <- readRDS(paste0(folder_results_susieST,"/",trait,"/",trait,".",st,".thin",thin,".",var_struc,".param.RDS"))


ctwas_parameters_susieST <- summarize_param(param_susieST, gwas_n)
total_nonSNPpve_susieST <- 1- ctwas_parameters_susieST$prop_heritability["SNP"]
pve_pie_by_type_susieST <- plot_piechart_topn(ctwas_parameters = ctwas_parameters_susieST, colors = colors, by = "type", title = "top-apa, multi-group")


param_apaonly <- readRDS(paste0(folder_results_apaonly,"/",trait,"/",trait,".",st,".thin",thin,".",var_struc,".param.RDS"))

ctwas_parameters_apaonly <- summarize_param(param_apaonly,gwas_n)
total_nonSNPpve_apaonly <- 1- ctwas_parameters_apaonly$prop_heritability["SNP"]
pve_pie_by_type_apaonly <- plot_piechart_topn(ctwas_parameters = ctwas_parameters_apaonly, colors = colors, by = "type", title = "top-apa, multi tissue apa")


top_tissue <- top_tissues[trait]
file_param_single <- paste0(folder_results_single, trait, "/", trait, "_", top_tissue, ".thin", thin, ".", var_struc, ".param.RDS")

param_single <- readRDS(file_param_single)
ctwas_parameters_single <- summarize_param(param_single, gwas_n)
total_nonSNPpve_single <- 1- ctwas_parameters_single$prop_heritability["SNP"]

pve_pie_by_type_single <- plot_piechart_topn(ctwas_parameters = ctwas_parameters_single, colors = colors, by = "context", title = "top-apa, single tissue apa")


plot_multi(pve_pie_by_type_single,pve_pie_by_type_apaonly,pve_pie_by_type_susieST, title=trait)

Version Author Date
278bbd9 XSun 2025-04-17 
TableGrob (2 x 3) "arrange": 4 grobs
  z     cells    name                 grob
1 1 (2-2,1-1) arrange       gtable[layout]
2 2 (2-2,2-2) arrange       gtable[layout]
3 3 (2-2,3-3) arrange       gtable[layout]
4 4 (1-1,1-3) arrange text[GRID.text.2032]
param_susieST <- readRDS(paste0(folder_results_susieST_susie,"/",trait,"/",trait,".",st,".thin",thin,".",var_struc,".param.RDS"))


ctwas_parameters_susieST <- summarize_param(param_susieST, gwas_n)
total_nonSNPpve_susieST <- 1- ctwas_parameters_susieST$prop_heritability["SNP"]
pve_pie_by_type_susieST <- plot_piechart_topn(ctwas_parameters = ctwas_parameters_susieST, colors = colors, by = "type", title = "multi-group")


param_apaonly <- readRDS(paste0(folder_results_apaonly_susie,"/",trait,"/",trait,".",st,".thin",thin,".",var_struc,".param.RDS"))

ctwas_parameters_apaonly <- summarize_param(param_apaonly,gwas_n)
total_nonSNPpve_apaonly <- 1- ctwas_parameters_apaonly$prop_heritability["SNP"]
pve_pie_by_type_apaonly <- plot_piechart_topn(ctwas_parameters = ctwas_parameters_apaonly, colors = colors, by = "type", title = "multi tissue susie st")


top_tissue <- top_tissues[trait]
file_param_single <- paste0(folder_results_single_susie, trait, "/", trait, "_", top_tissue, ".thin", thin, ".", var_struc, ".param.RDS")

param_single <- readRDS(file_param_single)
ctwas_parameters_single <- summarize_param(param_single, gwas_n)
total_nonSNPpve_single <- 1- ctwas_parameters_single$prop_heritability["SNP"]

pve_pie_by_type_single <- plot_piechart_topn(ctwas_parameters = ctwas_parameters_single, colors = colors, by = "context", title = "single tissue susie st")


plot_multi(pve_pie_by_type_single,pve_pie_by_type_apaonly,pve_pie_by_type_susieST, title=trait)

Version Author Date
bda6e43 XSun 2025-04-09 
TableGrob (2 x 3) "arrange": 4 grobs
  z     cells    name                 grob
1 1 (2-2,1-1) arrange       gtable[layout]
2 2 (2-2,2-2) arrange       gtable[layout]
3 3 (2-2,3-3) arrange       gtable[layout]
4 4 (1-1,1-3) arrange text[GRID.text.2171]

BMI-panukb

trait <- "BMI-panukb"
st <- "with_susieST"

gwas_n <- samplesize[trait]

%h2g for setting: shared_all, thin = 1

thin <- 1
var_struc <- "shared_all"
param_susieST <- readRDS(paste0(folder_results_susieST,"/",trait,"/",trait,".",st,".thin",thin,".",var_struc,".param.RDS"))


ctwas_parameters_susieST <- summarize_param(param_susieST, gwas_n)
total_nonSNPpve_susieST <- 1- ctwas_parameters_susieST$prop_heritability["SNP"]
pve_pie_by_type_susieST <- plot_piechart_topn(ctwas_parameters = ctwas_parameters_susieST, colors = colors, by = "type", title = "top-apa, multi-group")


param_apaonly <- readRDS(paste0(folder_results_apaonly,"/",trait,"/",trait,".",st,".thin",thin,".",var_struc,".param.RDS"))

ctwas_parameters_apaonly <- summarize_param(param_apaonly,gwas_n)
total_nonSNPpve_apaonly <- 1- ctwas_parameters_apaonly$prop_heritability["SNP"]
pve_pie_by_type_apaonly <- plot_piechart_topn(ctwas_parameters = ctwas_parameters_apaonly, colors = colors, by = "type", title = "top-apa, multi tissue apa")


top_tissue <- top_tissues[trait]
file_param_single <- paste0(folder_results_single, trait, "/", trait, "_", top_tissue, ".thin", thin, ".", var_struc, ".param.RDS")

param_single <- readRDS(file_param_single)
ctwas_parameters_single <- summarize_param(param_single, gwas_n)
total_nonSNPpve_single <- 1- ctwas_parameters_single$prop_heritability["SNP"]

pve_pie_by_type_single <- plot_piechart_topn(ctwas_parameters = ctwas_parameters_single, colors = colors, by = "context", title = "top-apa, single tissue apa")


plot_multi(pve_pie_by_type_single,pve_pie_by_type_apaonly,pve_pie_by_type_susieST, title=trait)

TableGrob (2 x 3) "arrange": 4 grobs
  z     cells    name                 grob
1 1 (2-2,1-1) arrange       gtable[layout]
2 2 (2-2,2-2) arrange       gtable[layout]
3 3 (2-2,3-3) arrange       gtable[layout]
4 4 (1-1,1-3) arrange text[GRID.text.2310]
param_susieST <- readRDS(paste0(folder_results_susieST_susie,"/",trait,"/",trait,".",st,".thin",thin,".",var_struc,".param.RDS"))


ctwas_parameters_susieST <- summarize_param(param_susieST, gwas_n)
total_nonSNPpve_susieST <- 1- ctwas_parameters_susieST$prop_heritability["SNP"]
pve_pie_by_type_susieST <- plot_piechart_topn(ctwas_parameters = ctwas_parameters_susieST, colors = colors, by = "type", title = "multi-group")


param_apaonly <- readRDS(paste0(folder_results_apaonly_susie,"/",trait,"/",trait,".",st,".thin",thin,".",var_struc,".param.RDS"))

ctwas_parameters_apaonly <- summarize_param(param_apaonly,gwas_n)
total_nonSNPpve_apaonly <- 1- ctwas_parameters_apaonly$prop_heritability["SNP"]
pve_pie_by_type_apaonly <- plot_piechart_topn(ctwas_parameters = ctwas_parameters_apaonly, colors = colors, by = "type", title = "multi tissue susie st")


top_tissue <- top_tissues[trait]
file_param_single <- paste0(folder_results_single_susie, trait, "/", trait, "_", top_tissue, ".thin", thin, ".", var_struc, ".param.RDS")

param_single <- readRDS(file_param_single)
ctwas_parameters_single <- summarize_param(param_single, gwas_n)
total_nonSNPpve_single <- 1- ctwas_parameters_single$prop_heritability["SNP"]

pve_pie_by_type_single <- plot_piechart_topn(ctwas_parameters = ctwas_parameters_single, colors = colors, by = "context", title = "single tissue susie st")


plot_multi(pve_pie_by_type_single,pve_pie_by_type_apaonly,pve_pie_by_type_susieST, title=trait)

Version Author Date
6e49457 XSun 2025-04-17
bda6e43 XSun 2025-04-09 
TableGrob (2 x 3) "arrange": 4 grobs
  z     cells    name                 grob
1 1 (2-2,1-1) arrange       gtable[layout]
2 2 (2-2,2-2) arrange       gtable[layout]
3 3 (2-2,3-3) arrange       gtable[layout]
4 4 (1-1,1-3) arrange text[GRID.text.2449]

RBC-panukb

trait <- "RBC-panukb"
st <- "with_susieST"

gwas_n <- samplesize[trait]

%h2g for setting: shared_all, thin = 1

thin <- 1
var_struc <- "shared_all"
param_susieST <- readRDS(paste0(folder_results_susieST,"/",trait,"/",trait,".",st,".thin",thin,".",var_struc,".param.RDS"))


ctwas_parameters_susieST <- summarize_param(param_susieST, gwas_n)
total_nonSNPpve_susieST <- 1- ctwas_parameters_susieST$prop_heritability["SNP"]
pve_pie_by_type_susieST <- plot_piechart_topn(ctwas_parameters = ctwas_parameters_susieST, colors = colors, by = "type", title = "top-apa, multi-group")


param_apaonly <- readRDS(paste0(folder_results_apaonly,"/",trait,"/",trait,".",st,".thin",thin,".",var_struc,".param.RDS"))

ctwas_parameters_apaonly <- summarize_param(param_apaonly,gwas_n)
total_nonSNPpve_apaonly <- 1- ctwas_parameters_apaonly$prop_heritability["SNP"]
pve_pie_by_type_apaonly <- plot_piechart_topn(ctwas_parameters = ctwas_parameters_apaonly, colors = colors, by = "type", title = "top-apa, multi tissue apa")


top_tissue <- top_tissues[trait]
file_param_single <- paste0(folder_results_single, trait, "/", trait, "_", top_tissue, ".thin", thin, ".", var_struc, ".param.RDS")

param_single <- readRDS(file_param_single)
ctwas_parameters_single <- summarize_param(param_single, gwas_n)
total_nonSNPpve_single <- 1- ctwas_parameters_single$prop_heritability["SNP"]

pve_pie_by_type_single <- plot_piechart_topn(ctwas_parameters = ctwas_parameters_single, colors = colors, by = "context", title = "top-apa, single tissue apa")


plot_multi(pve_pie_by_type_single,pve_pie_by_type_apaonly,pve_pie_by_type_susieST, title=trait)

TableGrob (2 x 3) "arrange": 4 grobs
  z     cells    name                 grob
1 1 (2-2,1-1) arrange       gtable[layout]
2 2 (2-2,2-2) arrange       gtable[layout]
3 3 (2-2,3-3) arrange       gtable[layout]
4 4 (1-1,1-3) arrange text[GRID.text.2588]
param_susieST <- readRDS(paste0(folder_results_susieST_susie,"/",trait,"/",trait,".",st,".thin",thin,".",var_struc,".param.RDS"))


ctwas_parameters_susieST <- summarize_param(param_susieST, gwas_n)
total_nonSNPpve_susieST <- 1- ctwas_parameters_susieST$prop_heritability["SNP"]
pve_pie_by_type_susieST <- plot_piechart_topn(ctwas_parameters = ctwas_parameters_susieST, colors = colors, by = "type", title = "multi-group")


param_apaonly <- readRDS(paste0(folder_results_apaonly_susie,"/",trait,"/",trait,".",st,".thin",thin,".",var_struc,".param.RDS"))

ctwas_parameters_apaonly <- summarize_param(param_apaonly,gwas_n)
total_nonSNPpve_apaonly <- 1- ctwas_parameters_apaonly$prop_heritability["SNP"]
pve_pie_by_type_apaonly <- plot_piechart_topn(ctwas_parameters = ctwas_parameters_apaonly, colors = colors, by = "type", title = "multi tissue susie st")


top_tissue <- top_tissues[trait]
file_param_single <- paste0(folder_results_single_susie, trait, "/", trait, "_", top_tissue, ".thin", thin, ".", var_struc, ".param.RDS")

param_single <- readRDS(file_param_single)
ctwas_parameters_single <- summarize_param(param_single, gwas_n)
total_nonSNPpve_single <- 1- ctwas_parameters_single$prop_heritability["SNP"]

pve_pie_by_type_single <- plot_piechart_topn(ctwas_parameters = ctwas_parameters_single, colors = colors, by = "context", title = "single tissue susie st")


plot_multi(pve_pie_by_type_single,pve_pie_by_type_apaonly,pve_pie_by_type_susieST, title=trait)

Version Author Date
bda6e43 XSun 2025-04-09 
TableGrob (2 x 3) "arrange": 4 grobs
  z     cells    name                 grob
1 1 (2-2,1-1) arrange       gtable[layout]
2 2 (2-2,2-2) arrange       gtable[layout]
3 3 (2-2,3-3) arrange       gtable[layout]
4 4 (1-1,1-3) arrange text[GRID.text.2727]

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] grid      stats4    stats     graphics  grDevices utils     datasets 
[8] methods   base     

other attached packages:
 [1] egg_0.4.5                 gridExtra_2.3            
 [3] ggrepel_0.9.1             pheatmap_1.0.12          
 [5] EnsDb.Hsapiens.v86_2.99.0 ensembldb_2.20.2         
 [7] AnnotationFilter_1.20.0   GenomicFeatures_1.48.3   
 [9] AnnotationDbi_1.58.0      Biobase_2.56.0           
[11] GenomicRanges_1.48.0      GenomeInfoDb_1.39.9      
[13] IRanges_2.30.0            S4Vectors_0.34.0         
[15] BiocGenerics_0.42.0       forcats_0.5.1            
[17] stringr_1.5.1             dplyr_1.1.4              
[19] purrr_1.0.2               readr_2.1.2              
[21] tidyr_1.3.0               tibble_3.2.1             
[23] tidyverse_1.3.1           ggplot2_3.5.1            
[25] ctwas_0.5.4.9000         

loaded via a namespace (and not attached):
  [1] readxl_1.4.0                backports_1.4.1            
  [3] workflowr_1.7.0             BiocFileCache_2.4.0        
  [5] repr_1.1.4                  lazyeval_0.2.2             
  [7] crosstalk_1.2.0             BiocParallel_1.30.3        
  [9] LDlinkR_1.2.3               digest_0.6.29              
 [11] htmltools_0.5.2             fansi_1.0.3                
 [13] magrittr_2.0.3              memoise_2.0.1              
 [15] tzdb_0.4.0                  Biostrings_2.64.0          
 [17] modelr_0.1.8                AMR_2.1.1                  
 [19] matrixStats_0.62.0          locuszoomr_0.2.1           
 [21] prettyunits_1.1.1           colorspace_2.0-3           
 [23] skimr_2.1.4                 blob_1.2.3                 
 [25] rvest_1.0.2                 rappdirs_0.3.3             
 [27] haven_2.5.0                 xfun_0.41                  
 [29] crayon_1.5.1                RCurl_1.98-1.7             
 [31] jsonlite_1.8.0              zoo_1.8-10                 
 [33] glue_1.6.2                  gtable_0.3.0               
 [35] zlibbioc_1.42.0             XVector_0.36.0             
 [37] DelayedArray_0.22.0         scales_1.3.0               
 [39] DBI_1.2.2                   Rcpp_1.0.12                
 [41] viridisLite_0.4.0           progress_1.2.2             
 [43] bit_4.0.4                   DT_0.22                    
 [45] htmlwidgets_1.5.4           httr_1.4.3                 
 [47] RColorBrewer_1.1-3          ellipsis_0.3.2             
 [49] farver_2.1.0                pkgconfig_2.0.3            
 [51] XML_3.99-0.14               sass_0.4.1                 
 [53] dbplyr_2.1.1                utf8_1.2.2                 
 [55] labeling_0.4.2              tidyselect_1.2.0           
 [57] rlang_1.1.2                 later_1.3.0                
 [59] munsell_0.5.0               pgenlibr_0.3.3             
 [61] cellranger_1.1.0            tools_4.2.0                
 [63] cachem_1.0.6                cli_3.6.1                  
 [65] generics_0.1.2              RSQLite_2.3.1              
 [67] broom_0.8.0                 evaluate_0.15              
 [69] fastmap_1.1.0               yaml_2.3.5                 
 [71] knitr_1.39                  bit64_4.0.5                
 [73] fs_1.5.2                    KEGGREST_1.36.3            
 [75] whisker_0.4                 xml2_1.3.3                 
 [77] biomaRt_2.54.1              compiler_4.2.0             
 [79] rstudioapi_0.13             plotly_4.10.0              
 [81] filelock_1.0.2              curl_4.3.2                 
 [83] png_0.1-7                   reprex_2.0.1               
 [85] bslib_0.3.1                 stringi_1.7.6              
 [87] highr_0.9                   lattice_0.20-45            
 [89] ProtGenerics_1.28.0         Matrix_1.5-3               
 [91] vctrs_0.6.5                 pillar_1.9.0               
 [93] lifecycle_1.0.4             jquerylib_0.1.4            
 [95] data.table_1.14.2           cowplot_1.1.1              
 [97] bitops_1.0-7                irlba_2.3.5                
 [99] httpuv_1.6.5                rtracklayer_1.56.0         
[101] R6_2.5.1                    BiocIO_1.6.0               
[103] promises_1.2.0.1            codetools_0.2-18           
[105] assertthat_0.2.1            SummarizedExperiment_1.26.1
[107] rprojroot_2.0.3             rjson_0.2.21               
[109] withr_2.5.0                 GenomicAlignments_1.32.0   
[111] Rsamtools_2.12.0            GenomeInfoDbData_1.2.8     
[113] parallel_4.2.0              hms_1.1.1                  
[115] gggrid_0.2-0                rmarkdown_2.25             
[117] MatrixGenerics_1.8.0        logging_0.10-108           
[119] git2r_0.30.1                mixsqp_0.3-43              
[121] lubridate_1.8.0             base64enc_0.1-3            
[123] restfulr_0.0.14