Comparing the new region merge strategy (with mapping table) with the old one (without mapping table)

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Last updated: 2025-07-15

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Introduction

Region Merging Strategy Update

• Old strategy (“without mapping table”):

When defining boundary genes for region merging, we only considered the molecular trait level. If a molecular trait exceeded a certain threshold, we evaluated whether region merging was needed based on that trait alone.

• New strategy (“with mapping table”):

We now assess merging based on gene-level PIP. Since multiple molecular traits can span different regions but still map to the same gene, we incorporate this mapping information when defining boundary genes. This can result in more genes and regions being merged compared to the old approach.

In the figures below:

Without mapping table refers to the old strategy.

With mapping table refers to the new, gene-centric strategy.

Unique genes are those identified by one strategy but not the other.

library(ggplot2)
library(gridExtra)

trait_nopsy <- c("LDL-ukb-d-30780_irnt","IBD-ebi-a-GCST004131","aFib-ebi-a-GCST006414","SBP-ukb-a-360",
                 "T1D-GCST90014023","T2D-panukb","ATH_gtexukb","BMI-panukb","HB-panukb",
                 "Height-panukb","HTN-panukb","PLT-panukb","RA-panukb","RBC-panukb",
                 "WBC-ieu-b-30"
                 )
trait_psy <- c("SCZ-ieu-b-5102","ASD-ieu-a-1185","BIP-ieu-b-5110","MDD-ieu-b-102","PD-ieu-b-7",
               "ADHD-ieu-a-1183","NS-ukb-a-230")
traits <- c(trait_nopsy,trait_psy)

plot_overlap_barplot_boundarygene <- function(selected_boundary_genes_old,
                                 selected_boundary_genes_new, trait) {
 
  # Extract gene names
  old_genes <- selected_boundary_genes_old$id
  new_genes <- selected_boundary_genes_new$id
  
  # Compute overlap
  overlap_genes <- intersect(old_genes, new_genes)
  new_genes_unique <- setdiff(new_genes, overlap_genes)
  n_overlap <- length(overlap_genes)
  n_old <- length(old_genes)
  n_new<- length(new_genes)
  
  # Construct data frame for plotting
  df <- data.frame(
    group = rep(c("without \nmapping table","with \nmapping table"), each = 2),
    part = rep(c("Overlap", "Unique"), 2),
    count = c(n_overlap, n_old - n_overlap, n_overlap, n_new- n_overlap)
  )
  
  # Ensure proper stacking order
  df$part <- factor(df$part, levels = c("Unique", "Overlap"))
  
  # Plot
  p <- ggplot(df, aes(x = group, y = count, fill = part)) +
    geom_bar(stat = "identity", width = 0.6) +
    geom_text(aes(label = count), position = position_stack(vjust = 0.5), color = "white", size = 5) +
    scale_fill_manual(values = c("Overlap" = "#1f77b4", "Unique" = "#ff7f0e")) +
    labs(
      x = "",
      y = "Number of selected boundary genes",
      title = trait,
      fill = ""
    ) +
    theme_minimal(base_size = 14)+ 
    theme(axis.text.x = element_text(angle = 45, hjust = 1))
  
  return(p)
}


plot_overlap_barplot_region <- function(region_id_new,
                                 region_id_old, trait) {
  
  # Compute overlap
  overlap_genes <- intersect(region_id_new, region_id_old)
  new_genes_unique <- setdiff(region_id_new, region_id_old)
  n_overlap <- length(overlap_genes)
  n_old <- length(region_id_old)
  n_new<- length(region_id_new)
  
  # Construct data frame for plotting
  df <- data.frame(
    group = rep(c("without \nmapping table","with \nmapping table"), each = 2),
    part = rep(c("Overlap", "Unique"), 2),
    count = c(n_overlap, n_old - n_overlap, n_overlap, n_new- n_overlap)
  )
  
  # Ensure proper stacking order
  df$part <- factor(df$part, levels = c("Unique", "Overlap"))
  
  # Plot
  p <- ggplot(df, aes(x = group, y = count, fill = part)) +
    geom_bar(stat = "identity", width = 0.6) +
    geom_text(aes(label = count), position = position_stack(vjust = 0.5), color = "white", size = 5) +
    scale_fill_manual(values = c("Overlap" = "#1f77b4", "Unique" = "#ff7f0e")) +
    labs(
      x = "",
      y = "Number of merged regions",
      title = trait,
      fill = ""
    ) +
    theme_minimal(base_size = 14)+ 
    theme(axis.text.x = element_text(angle = 45, hjust = 1))
  
  return(p)
}


plot_overlap_barplot_siggene <- function(combined_pip_by_group_multi_old,
                                 combined_pip_by_group_multi_new, trait) {
 
  # Extract gene names
  old_genes <- combined_pip_by_group_multi_old$gene_name[combined_pip_by_group_multi_old$combined_pip > 0.8]
  new_genes <- combined_pip_by_group_multi_new$gene_name[combined_pip_by_group_multi_new$combined_pip > 0.8]
  
  # Compute overlap
  overlap_genes <- intersect(old_genes, new_genes)
  new_genes_unique <- setdiff(new_genes, overlap_genes)
  n_overlap <- length(overlap_genes)
  n_old <- length(old_genes)
  n_new<- length(new_genes)
  
  # Construct data frame for plotting
  df <- data.frame(
    group = rep(c("without \nmapping table","with \nmapping table"), each = 2),
    part = rep(c("Overlap", "Unique"), 2),
    count = c(n_overlap, n_old - n_overlap, n_overlap, n_new- n_overlap)
  )
  
  # Ensure proper stacking order
  df$part <- factor(df$part, levels = c("Unique", "Overlap"))
  
  # Plot
  p <- ggplot(df, aes(x = group, y = count, fill = part)) +
    geom_bar(stat = "identity", width = 0.6) +
    geom_text(aes(label = count), position = position_stack(vjust = 0.5), color = "white", size = 5) +
    scale_fill_manual(values = c("Overlap" = "#1f77b4", "Unique" = "#ff7f0e")) +
    labs(
      x = "",
      y = "Number of gene with PIP > 0.8",
      title = trait,
      fill = ""
    ) +
    theme_minimal(base_size = 14)+ 
    theme(axis.text.x = element_text(angle = 45, hjust = 1))
  
  return(p)
}

folder_results_old <- "/project/xinhe/xsun/multi_group_ctwas/23.multi_group_0515/snakemake_outputs/"

for (trait in traits) {
  # trait <- "LDL-ukb-d-30780_irnt"

  file_old_gene <- paste0(folder_results_old,"/",trait,"/",trait,".3qtls.thin1.shared_all.L5.regionmerge_selected_boundary_genes.RDS")
  if(file.exists(file_old_gene)){
    selected_boundary_genes_old <- readRDS(file_old_gene)
    region_id_old <- unique(selected_boundary_genes_old$region_id)
  }else{
    selected_boundary_genes_old <- NULL
    region_id_old <- NULL
  }
  combined_pip_by_group_multi_old <- readRDS(paste0(folder_results_old,trait,"/",trait,".3qtls.combined_pip_bytype_final.RDS"))
  
  
  file_new_genes <- paste0(folder_results_old,"/",trait,"/",trait,".3qtls.thin1.shared_all.L5.regionmerge_with_mappingtable_selected_boundary_genes.RDS")
  if(file.exists(file_new_genes)){
    selected_boundary_genes_new <- readRDS(file_new_genes)
    region_id_new <- unique(selected_boundary_genes_new$region_id)
  }else{
    selected_boundary_genes_new <- NULL
    region_id_new <- NULL
  }
  combined_pip_by_group_multi_new <- readRDS(paste0(folder_results_old,trait,"/",trait,".3qtls.combined_pip_rmmapping_bytype_final.RDS"))
  
  p1 <- plot_overlap_barplot_boundarygene(selected_boundary_genes_old = selected_boundary_genes_old, selected_boundary_genes_new = selected_boundary_genes_new, trait = trait)
  
  p2 <- plot_overlap_barplot_region(region_id_new = region_id_new,region_id_old = region_id_old, trait = trait)
  
  p3 <- plot_overlap_barplot_siggene(combined_pip_by_group_multi_old = combined_pip_by_group_multi_old, combined_pip_by_group_multi_new = combined_pip_by_group_multi_new,trait = trait)
  grid.arrange(p1, p2, p3, ncol = 3)
  
  combined_pip_by_group_multi_sig_new <- combined_pip_by_group_multi_new$gene_name[combined_pip_by_group_multi_new$combined_pip > 0.8]
  combined_pip_by_group_multi_sig_old <- combined_pip_by_group_multi_old$gene_name[combined_pip_by_group_multi_old$combined_pip > 0.8]
  unique_old <- combined_pip_by_group_multi_sig_old[!combined_pip_by_group_multi_sig_old %in% combined_pip_by_group_multi_sig_new]
  
  unique_new <- combined_pip_by_group_multi_sig_new[!combined_pip_by_group_multi_sig_new %in% combined_pip_by_group_multi_sig_old]
  
  print(sprintf("Unique genes (with mapping table): %s",paste0(unique_new,collapse = ", ")))
  print(sprintf("Unique genes (without mapping table): %s",paste0(unique_old,collapse = ", ")))
  
}

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6921b53	XSun	2025-06-30

[1] "Unique genes (with mapping table): "
[1] "Unique genes (without mapping table): "

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6921b53	XSun	2025-06-30

[1] "Unique genes (with mapping table): "
[1] "Unique genes (without mapping table): HLA-DQB1"

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6921b53	XSun	2025-06-30

[1] "Unique genes (with mapping table): "
[1] "Unique genes (without mapping table): GPR85"

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[1] "Unique genes (without mapping table): CAMK1D"

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[1] "Unique genes (with mapping table): "
[1] "Unique genes (without mapping table): CYP21A2"

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[1] "Unique genes (with mapping table): "
[1] "Unique genes (without mapping table): SLC22A3, MCTP2, MYPN, PAMR1, VMAC"

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[1] "Unique genes (with mapping table): "
[1] "Unique genes (without mapping table): ZNF385A, MAMDC2, MORC2, BMF"

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[1] "Unique genes (with mapping table): "
[1] "Unique genes (without mapping table): VEGFA, ABHD12, TAF8, ITSN1, ZNF106, VRK2, CCND3, XAF1"

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[1] "Unique genes (with mapping table): ACAP1, DMXL1, LIPA, C2CD2, ERAL1, KDM6B, PTEN"
[1] "Unique genes (without mapping table): ATP13A1, LSM4, FYCO1, SUSD1, ANKRD44"

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[1] "Unique genes (without mapping table): "

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] C

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

other attached packages:
[1] gridExtra_2.3 ggplot2_3.5.1

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.12      highr_0.9        pillar_1.9.0     compiler_4.2.0  
 [5] bslib_0.3.1      later_1.3.0      jquerylib_0.1.4  git2r_0.30.1    
 [9] workflowr_1.7.0  tools_4.2.0      digest_0.6.29    jsonlite_1.8.0  
[13] evaluate_0.15    lifecycle_1.0.4  tibble_3.2.1     gtable_0.3.0    
[17] pkgconfig_2.0.3  rlang_1.1.2      cli_3.6.1        rstudioapi_0.13 
[21] yaml_2.3.5       xfun_0.41        fastmap_1.1.0    withr_2.5.0     
[25] dplyr_1.1.4      stringr_1.5.1    knitr_1.39       generics_0.1.2  
[29] fs_1.5.2         vctrs_0.6.5      sass_0.4.1       tidyselect_1.2.0
[33] rprojroot_2.0.3  grid_4.2.0       glue_1.6.2       R6_2.5.1        
[37] fansi_1.0.3      rmarkdown_2.25   farver_2.1.0     magrittr_2.0.3  
[41] whisker_0.4      scales_1.3.0     promises_1.2.0.1 htmltools_0.5.2 
[45] colorspace_2.0-3 httpuv_1.6.5     labeling_0.4.2   utf8_1.2.2      
[49] stringi_1.7.6    munsell_0.5.0