LDL_newtissue_example

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Last updated: 2024-12-20

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Knit directory: multigroup_ctwas_analysis/

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Introduction

Tissues are: “Liver”,“Spleen”,“Esophagus_Gastroesophageal_Junction”,“Esophagus_Muscularis”,“Esophagus_Mucosa”

library(ctwas)
library(EnsDb.Hsapiens.v86)
library(ggplot2)
library(gridExtra)
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(  "#1f77b4", "#ff7f0e", "#2ca02c", "#d62728",  "#9467bd", "#8c564b", "#e377c2", "#7f7f7f",  "#bcbd22",  "#17becf",  "#f7b6d2",  "#c5b0d5",  "#9edae5", "#ffbb78",  "#98df8a",  "#ff9896" )

plot_piechart <- function(ctwas_parameters, colors, by) {
  # 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("Percent of Heritability")  # Title
  
  return(pie)
}

LDL-ukb-d-30780_irnt

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

results_dir_origin <- paste0("/project/xinhe/xsun/multi_group_ctwas/11.multi_group_1008/results-newtissues/",trait,"/")
ctwas_res_origin <- readRDS(paste0(results_dir_origin,trait,".ctwas.res.RDS"))

finemap_res_origin <- ctwas_res_origin$finemap_res

Parameters

gwas_n <- samplesize[trait]
tissue <- c("Liver","Spleen","Esophagus_Gastroesophageal_Junction","Esophagus_Muscularis","Esophagus_Mucosa")

param_origin <- ctwas_res_origin$param
make_convergence_plots(param_origin, gwas_n, colors = colors)

ctwas_parameters_origin <- summarize_param(param_origin, gwas_n)
pve_pie_by_type_origin <- plot_piechart(ctwas_parameters = ctwas_parameters_origin, colors = colors, by = "type")
pve_pie_by_context_origin <- plot_piechart(ctwas_parameters = ctwas_parameters_origin, colors = colors, by = "context")

gridExtra::grid.arrange(pve_pie_by_type_origin,pve_pie_by_context_origin, ncol = 2)

Region merge

load(paste0("/project/xinhe/xsun/multi_group_ctwas/11.multi_group_1008/post_process_rm_ld-newtissues/rm_",trait,".rdata"))

finemap_res_rm <- res_regionmerge$finemap_res
finemap_res_rm_boundary_genes <- finemap_res_rm[finemap_res_rm$id %in%selected_boundary_genes$id,]
finemap_res_rm_boundary_genes_pip <- finemap_res_rm_boundary_genes[,c("id","susie_pip","cs")]


finemap_res_origin_boundary_genes <- finemap_res_origin[finemap_res_origin$id %in%selected_boundary_genes$id,]
finemap_res_origin_boundary_genes_pip <- finemap_res_origin_boundary_genes[,c("id","susie_pip","cs")]

finemap_res_compare_regionmerge <- merge(finemap_res_origin_boundary_genes_pip,finemap_res_rm_boundary_genes_pip, by = "id")
colnames(finemap_res_compare_regionmerge) <- c("id","susie_pip_origin","cs_origin","susie_pip_reginmerge","cs_reginmerge")

DT::datatable(finemap_res_compare_regionmerge,caption = htmltools::tags$caption( style = 'caption-side: left; text-align: left; color:black;  font-size:150% ;','Selected boundary genes (susie_pip > 0.5)'),options = list(pageLength = 10) )

LD-mismatch

Diagnosis

file_pipthreshold02 <- paste0("/project/xinhe/xsun/multi_group_ctwas/11.multi_group_1008/post_process_rm_ld-newtissues/ldmismatch_diagnosis_pipthres02_nozfilter_", trait, ".rdata")

if (file.exists(file_pipthreshold02)) {
  load(file_pipthreshold02)
  pip_02 <- data.frame(
  "PIP Threshold" = "0.2",
  "Number of Selected Regions" = length(selected_region_ids),
  "Number of Problematic Genes" = length(problematic_genes),
  "Number of Problematic Regions" = length(problematic_region_ids),
  "Number of Problematic SNPs" = length(res_ldmismatch$problematic_snps),
  "Number of Flipped SNPs" = length(res_ldmismatch$flipped_snps)
)
}else{

  load(paste0("/project/xinhe/xsun/multi_group_ctwas/11.multi_group_1008/post_process_rm_ld-newtissues/ldmismatch_diagnosis_pipthres02_", trait, ".rdata"))
  pip_02 <- data.frame(
  "PIP Threshold" = "0.2",
  "Number of Selected Regions Number of Selected Regions" = length(selected_region_ids),
  "Number of Problematic Genes" = 0,
  "Number of Problematic Regions" = 0,
  "Number of Problematic SNPs" = length(res_ldmismatch$problematic_snps),
  "Number of Flipped SNPs" = length(res_ldmismatch$flipped_snps)
)

}

file_pipthreshold05 <- paste0("/project/xinhe/xsun/multi_group_ctwas/11.multi_group_1008/post_process_rm_ld-newtissues/ldmismatch_diagnosis_pipthres05_nozfilter_", trait, ".rdata")

if (file.exists(file_pipthreshold05)) {
  load(file_pipthreshold05)
  pip_05 <- data.frame(
  "PIP Threshold" = "0.5",
  "Number of Selected Regions" = length(selected_region_ids),
  "Number of Problematic Genes" = length(problematic_genes),
  "Number of Problematic Regions" = length(problematic_region_ids),
  "Number of Problematic SNPs" = length(res_ldmismatch$problematic_snps),
  "Number of Flipped SNPs" = length(res_ldmismatch$flipped_snps)
)
}else{

  load(paste0("/project/xinhe/xsun/multi_group_ctwas/11.multi_group_1008/post_process_rm_ld-newtissues/ldmismatch_diagnosis_pipthres05_", trait, ".rdata"))
  pip_05 <- data.frame(
  "PIP Threshold" = "0.5",
  "Number of Selected Regions" = length(selected_region_ids),
  "Number of Problematic Genes" = 0,
  "Number of Problematic Regions" = 0,
  "Number of Problematic SNPs" = length(res_ldmismatch$problematic_snps),
  "Number of Flipped SNPs" = length(res_ldmismatch$flipped_snps)
)

}


results_table <- rbind(pip_02, pip_05)

DT::datatable(results_table,caption = htmltools::tags$caption( style = 'caption-side: left; text-align: left; color:black;  font-size:150% ;','LD mismatch diagnosis table for different gene cutoff'),options = list(pageLength = 10) )

Comparing 2 LD mismatch fixing methods

file_ldmismatch_results <- paste0("/project/xinhe/xsun/multi_group_ctwas/11.multi_group_1008/post_process_rm_ld-newtissues/ldmismatch_pipthres02_nold_nozfilter_",trait,".rdata")

if(file.exists(file_ldmismatch_results)) {

  load(file_pipthreshold02)

  load(file_ldmismatch_results)
  finemap_res_ldmm_nold <- res_ldmm_nold$finemap_res
  load(paste0("/project/xinhe/xsun/multi_group_ctwas/11.multi_group_1008/post_process_rm_ld-newtissues/ldmismatch_pipthres02_removesnp_nozfilter_",trait,".rdata"))
  finemap_res_ldmm_removesnp <- res_ldmm_removesnp$finemap_res

  finemap_res_ldmm_nold_problematic_gene <- finemap_res_ldmm_nold[finemap_res_ldmm_nold$region_id %in% problematic_region_ids & finemap_res_ldmm_nold$type != "SNP",]
  finemap_res_ldmm_removesnp_problematic_gene <- finemap_res_ldmm_removesnp[finemap_res_ldmm_removesnp$region_id %in% problematic_region_ids & finemap_res_ldmm_removesnp$type != "SNP",]

  merge_2method <- merge(finemap_res_ldmm_nold_problematic_gene,finemap_res_ldmm_removesnp_problematic_gene, by ="id",all.x=T)
  merge_2method$highlight <- ifelse(merge_2method$id %in% problematic_genes, "problematic genes", "good genes")
  merge_2method$susie_pip.y[is.na(merge_2method$susie_pip.y)] <- 1.5
    p1 <- ggplot(data = merge_2method, aes(x = susie_pip.x, y = susie_pip.y, color = highlight, alpha = highlight)) + 
    geom_point() +
    scale_color_manual(values = c("problematic genes" = "red", "good genes" = "black")) +  
    scale_alpha_manual(values = c("problematic genes" = 1, "good genes" = 0.1)) +
    labs(x = "PIP_noLD", y = "PIP_removesnp") + 
    geom_abline(slope = 1, intercept = 0, col = "red") + 
    ggtitle("Problematic regions only, genes only") +
    theme_minimal()

  finemap_res_rm_problematic_gene <- finemap_res_rm[finemap_res_rm$region_id %in% problematic_region_ids & finemap_res_rm$type != "SNP",]

  merge_rm_ldmm_nold <-  merge(finemap_res_rm_problematic_gene,finemap_res_ldmm_nold_problematic_gene, by ="id",all.x=T)
  merge_rm_ldmm_nold$highlight <- ifelse(merge_rm_ldmm_nold$id %in% problematic_genes, "problematic genes", "good genes")
  merge_rm_ldmm_nold$susie_pip.y[is.na(merge_rm_ldmm_nold$susie_pip.y)] <- 1.5
  p2 <- ggplot(data = merge_rm_ldmm_nold, aes(x= susie_pip.x, y= susie_pip.y, color = highlight, alpha = highlight)) + 
    geom_point() +
    labs(x="PIP_after_regionmerge", y="PIP_noLD") + 
    scale_color_manual(values = c("problematic genes" = "red", "good genes" = "black")) +  
    scale_alpha_manual(values = c("problematic genes" = 1, "good genes" = 0.1)) +
    geom_abline(slope = 1, intercept = 0, col ="red") + 
    ggtitle("problematic regions only, genes only") +
    theme_minimal()

  merge_rm_ldmm_removesnp <-  merge(finemap_res_rm_problematic_gene,finemap_res_ldmm_removesnp_problematic_gene, by ="id",all.x =T)
  merge_rm_ldmm_removesnp$highlight <- ifelse(merge_rm_ldmm_removesnp$id %in% problematic_genes, "problematic genes", "good genes")
  merge_rm_ldmm_removesnp$susie_pip.y[is.na(merge_rm_ldmm_removesnp$susie_pip.y)] <- 1.5
  p3 <- ggplot(data = merge_rm_ldmm_removesnp, aes(x= susie_pip.x, y= susie_pip.y, color = highlight, alpha = highlight)) + 
    geom_point() +
    labs(x="PIP_after_regionmerge", y="PIP_removesnp") + 
    scale_alpha_manual(values = c("problematic genes" = 1, "good genes" = 0.1)) +
    scale_color_manual(values = c("problematic genes" = "red", "good genes" = "black")) +
    geom_abline(slope = 1, intercept = 0, col ="red") + 
    ggtitle("problematic regions only, genes only") +
    theme_minimal()
  
  print(sprintf("Total number of molecular traits in problematic regions = %s",nrow(merge_rm_ldmm_removesnp)))
  print(sprintf("Number of molecular traits disappeared after removing prblematic SNPs = %s", sum(merge_rm_ldmm_removesnp$susie_pip.y == 1.5)))
  print("The dots showing PIP =1.5 means: these genes were removed since the only QTLs of them are problematic")
  print("Notes: 2 intron overlapped")
  grid.arrange(p1,p2,p3, ncol = 3)



}else{

  print("There's no problematic genes, no need to compare")

}

[1] "Total number of molecular traits in problematic regions = 1630"
[1] "Number of molecular traits disappeared after removing prblematic SNPs = 375"
[1] "The dots showing PIP =1.5 means: these genes were removed since the only QTLs of them are problematic"
[1] "Notes: 2 intron overlapped"

Comparing z-scores and susie_pip

if(file.exists(file_ldmismatch_results)){

  finemap_res_origin <- ctwas_res_origin$finemap_res
  finemap_res_origin_gene <- finemap_res_origin[finemap_res_origin$type != "SNP",]
  finemap_res_origin_gene$highlight <- ifelse(finemap_res_origin_gene$id %in% problematic_genes, "problematic genes", "good genes")

  p1 <- ggplot(data = finemap_res_origin_gene, aes(x= abs(z), y= susie_pip, color = highlight)) +
    geom_point() +
    scale_color_manual(values = c("problematic genes" = "red", "good genes" = "black")) +
    ggtitle("Original ctwas results") +
    theme_minimal()


  finemap_res_rm_gene <- finemap_res_rm[finemap_res_rm$type != "SNP",]
  finemap_res_rm_gene$highlight <- ifelse(finemap_res_rm_gene$id %in% problematic_genes, "problematic genes", "good genes")

  p2 <- ggplot(data = finemap_res_rm_gene, aes(x= abs(z), y= susie_pip, color = highlight)) +
    geom_point() +
    scale_color_manual(values = c("problematic genes" = "red", "good genes" = "black")) +
    ggtitle("After region merge") +
    theme_minimal()


  finemap_res_ldmm_nold_gene <- finemap_res_ldmm_nold[finemap_res_ldmm_nold$type !="SNP",]
  finemap_res_ldmm_nold_gene$highlight <- ifelse(finemap_res_ldmm_nold_gene$id %in% problematic_genes, "problematic genes", "good genes")

  p3 <- ggplot(data = finemap_res_ldmm_nold_gene, aes(x= abs(z), y= susie_pip, color = highlight)) +
    geom_point() +
    scale_color_manual(values = c("problematic genes" = "red", "good genes" = "black")) +
    ggtitle("After LD mismatch fixed -- noLD") +
    theme_minimal()

  finemap_res_ldmm_removesnp_gene <- finemap_res_ldmm_removesnp[finemap_res_ldmm_removesnp$type !="SNP",]
  finemap_res_ldmm_removesnp_gene$highlight <- ifelse(finemap_res_ldmm_removesnp_gene$id %in% problematic_genes, "problematic genes", "good genes")

  p4 <- ggplot(data = finemap_res_ldmm_removesnp_gene, aes(x= abs(z), y= susie_pip, color = highlight)) +
    geom_point() +
    scale_color_manual(values = c("problematic genes" = "red", "good genes" = "black")) +
    ggtitle("After LD mismatch fixed -- SNP removed") +
    theme_minimal()


  grid.arrange(p1,p2,p3,p4, ncol = 4)

  print("L - estimated in region merge step")
  print(updated_data_res_regionmerge$updated_region_L[problematic_region_ids])
  load(paste0("/project/xinhe/xsun/multi_group_ctwas/11.multi_group_1008/post_process_rm_ld-newtissues/ldmismatch_pipthres02_removesnp_rescreenregion_nozfilter_",trait,".rdata"))
  print("L - re-estimated after updating z_scores, region data")
  print(screen_res$screened_region_L)
}else{

  print("There's no problematic genes")

  finemap_res_origin <- ctwas_res_origin$finemap_res
  finemap_res_origin_gene <- finemap_res_origin[finemap_res_origin$type != "SNP",]

  p1 <- ggplot(data = finemap_res_origin_gene, aes(x= abs(z), y= susie_pip)) +
    geom_point() +
    ggtitle("Original ctwas results") +
    theme_minimal()


  finemap_res_rm_gene <- finemap_res_rm[finemap_res_rm$type != "SNP",]

  p2 <- ggplot(data = finemap_res_rm_gene, aes(x= abs(z), y= susie_pip)) +
    geom_point() +
    ggtitle("After region merge") +
    theme_minimal()

  grid.arrange(p1,p2, ncol = 2)
}

[1] "L - estimated in region merge step"
19_9127717_13360313 
                  5 
[1] "L - re-estimated after updating z_scores, region data"
19_9127717_13360313 
                  5 

Examples for LD-mismatch fixing

weights_origin <- readRDS(paste0("/project/xinhe/xsun/multi_group_ctwas/11.multi_group_1008/results/",trait,"/",trait,".preprocessed.weights.RDS"))

load(paste0("/project/xinhe/xsun/multi_group_ctwas/11.multi_group_1008/post_process_rm_ld-newtissues/ldmismatch_pipthres02_removesnp_weights_updated_nozfilter_",trait,".rdata"))

region_id <- "19_9127717_13360313"

finemap_res_rm <- anno_finemap_res(finemap_res_rm,
                                          snp_map = updated_data_res_regionmerge[["updated_snp_map"]],
                                          mapping_table = mapping_two,
                                          add_gene_annot = TRUE,
                                          map_by = "molecular_id",
                                          drop_unmapped = TRUE,
                                          add_position = TRUE,
                                          use_gene_pos = "mid")

2024-12-20 17:43:11 INFO::Annotating fine-mapping result ...
2024-12-20 17:43:11 INFO::Map molecular traits to genes
2024-12-20 17:43:12 INFO::Split PIPs for molecular traits mapped to multiple genes
2024-12-20 17:43:19 INFO::Add gene positions
2024-12-20 17:43:22 INFO::Add SNP positions

finemap_res_ldmm_nold <- anno_finemap_res(finemap_res_ldmm_nold,
                                          snp_map = updated_data_res_regionmerge[["updated_snp_map"]],
                                          mapping_table = mapping_two,
                                          add_gene_annot = TRUE,
                                          map_by = "molecular_id",
                                          drop_unmapped = TRUE,
                                          add_position = TRUE,
                                          use_gene_pos = "mid")

2024-12-20 17:43:40 INFO::Annotating fine-mapping result ...
2024-12-20 17:43:40 INFO::Map molecular traits to genes
2024-12-20 17:43:41 INFO::Split PIPs for molecular traits mapped to multiple genes
2024-12-20 17:43:48 INFO::Add gene positions
2024-12-20 17:43:48 INFO::Add SNP positions

finemap_res_ldmm_removesnp <- anno_finemap_res(finemap_res_ldmm_removesnp,
                                   snp_map = updated_data_res_regionmerge[["updated_snp_map"]],
                                   mapping_table = mapping_two,
                                   add_gene_annot = TRUE,
                                   map_by = "molecular_id",
                                   drop_unmapped = TRUE,
                                   add_position = TRUE,
                                   use_gene_pos = "mid")

2024-12-20 17:43:53 INFO::Annotating fine-mapping result ...
2024-12-20 17:43:53 INFO::Map molecular traits to genes
2024-12-20 17:43:53 INFO::Split PIPs for molecular traits mapped to multiple genes
2024-12-20 17:44:00 INFO::Add gene positions
2024-12-20 17:44:00 INFO::Add SNP positions

finemap_res_rm_gene <- finemap_res_rm[finemap_res_rm$type != "SNP",]
finemap_res_ldmm_removesnp_gene <- finemap_res_ldmm_removesnp[finemap_res_ldmm_removesnp$type !="SNP",]



print("locus plot -- after region merge")

[1] "locus plot -- after region merge"

make_locusplot(finemap_res_rm,
               region_id = region_id,
               ens_db = ens_db,
               weights = weights_origin,
               highlight_pip = 0.8,
               filter_protein_coding_genes = TRUE,
               filter_cs = TRUE,
               color_pval_by = "cs",
               color_pip_by = "cs",panel.heights = c(4,4,1,1))

2024-12-20 17:44:09 INFO::Limit to protein coding genes
2024-12-20 17:44:09 INFO::focal id: intron_19_11120522_11123174|Liver_sQTL
2024-12-20 17:44:09 INFO::focal molecular trait: LDLR Liver sQTL
2024-12-20 17:44:09 INFO::Range of locus: chr19:9127860-13930432
2024-12-20 17:44:09 INFO::focal molecular trait QTL positions: 11120205,11120527
2024-12-20 17:44:09 INFO::Limit PIPs to credible sets

print("locus plot -- LD mismatch: no LD")

[1] "locus plot -- LD mismatch: no LD"

make_locusplot(finemap_res_ldmm_nold,
               region_id = region_id,
               ens_db = ens_db,
               weights = weights_origin,
               highlight_pip = 0.8,
               filter_protein_coding_genes = TRUE,
               filter_cs = TRUE,
               color_pval_by = "cs",
               color_pip_by = "cs",panel.heights = c(4,4,1,1))

2024-12-20 17:44:14 INFO::Limit to protein coding genes
2024-12-20 17:44:14 INFO::focal id: ENSG00000197256.10|Liver_eQTL
2024-12-20 17:44:14 INFO::focal molecular trait: KANK2 Liver eQTL
2024-12-20 17:44:14 INFO::Range of locus: chr19:9127860-13930432
2024-12-20 17:44:14 INFO::focal molecular trait QTL positions: 11197621
2024-12-20 17:44:14 INFO::Limit PIPs to credible sets

print("locus plot -- LD mismatch: snp removed")

[1] "locus plot -- LD mismatch: snp removed"

make_locusplot(finemap_res_ldmm_removesnp,
               region_id = region_id,
               ens_db = ens_db,
               weights = weights_updated,
               highlight_pip = 0.8,
               filter_protein_coding_genes = TRUE,
               filter_cs = TRUE,
               color_pval_by = "cs",
               color_pip_by = "cs",panel.heights = c(4,4,1,1))

2024-12-20 17:44:19 INFO::Limit to protein coding genes
2024-12-20 17:44:19 INFO::focal id: intron_19_11116212_11120092|Liver_sQTL
2024-12-20 17:44:19 INFO::focal molecular trait: LDLR Liver sQTL
2024-12-20 17:44:19 INFO::Range of locus: chr19:9127860-13930432
2024-12-20 17:44:19 INFO::focal molecular trait QTL positions: 11116394
2024-12-20 17:44:19 INFO::Limit PIPs to credible sets

finemap_res_rm_gene_region <- finemap_res_rm_gene[finemap_res_rm_gene$region_id == region_id,]
finemap_res_ldmm_removesnp_gene_region <- finemap_res_ldmm_removesnp_gene[finemap_res_ldmm_removesnp_gene$region_id == region_id,]
merged_region_gene <- merge(finemap_res_rm_gene_region,finemap_res_ldmm_removesnp_gene_region,by = "id",all.x=T)
merged_region_gene <- merged_region_gene[,c("id","gene_name.x","z.x","susie_pip.x","cs.x","z.y","susie_pip.y","cs.y")]
colnames(merged_region_gene) <- c("id","gene_name","z_regionmerge","susie_pip_regionmerge","cs_regionmerge","z_ldmismatch","susie_pip_ldmismatch","cs_ldmismatch")

merged_region_gene$highlight <- ifelse(merged_region_gene$id %in% problematic_genes, "problematic genes", "good genes")

merged_region_gene$z_ldmismatch[is.na(merged_region_gene$z_ldmismatch)] <- 10
print("The dots showing z_ldmismatch =10 means: these genes were removed since the only QTLs of them are problematic")

[1] "The dots showing z_ldmismatch =10 means: these genes were removed since the only QTLs of them are problematic"

ggplot(data = merged_region_gene, aes(x= z_regionmerge, y= z_ldmismatch, color = highlight, alpha = highlight)) +
  geom_point() +
  scale_color_manual(values = c("problematic genes" = "red", "good genes" = "black")) +
  scale_alpha_manual(values = c("problematic genes" = 1, "good genes" = 0.3)) +
  ggtitle("Comparing z-scores before/after removing the problematic SNPs") +
  theme_minimal()

DT::datatable(merged_region_gene[merged_region_gene$z_ldmismatch != merged_region_gene$z_regionmerge,],caption = htmltools::tags$caption( style = 'caption-side: left; text-align: left; color:black;  font-size:150% ;','Genes with different z before / after removing the problematic SNPs'),options = list(pageLength = 10) )

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

other attached packages:
 [1] dplyr_1.1.4               gridExtra_2.3            
 [3] ggplot2_3.5.1             EnsDb.Hsapiens.v86_2.99.0
 [5] ensembldb_2.20.2          AnnotationFilter_1.20.0  
 [7] GenomicFeatures_1.48.3    AnnotationDbi_1.58.0     
 [9] Biobase_2.56.0            GenomicRanges_1.48.0     
[11] GenomeInfoDb_1.39.9       IRanges_2.30.0           
[13] S4Vectors_0.34.0          BiocGenerics_0.42.0      
[15] ctwas_0.4.20.9001        

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            logging_0.10-108           
 [17] cachem_1.0.6                knitr_1.39                 
 [19] jsonlite_1.8.0              workflowr_1.7.0            
 [21] Rsamtools_2.12.0            dbplyr_2.1.1               
 [23] png_0.1-7                   readr_2.1.2                
 [25] compiler_4.2.0              httr_1.4.3                 
 [27] assertthat_0.2.1            Matrix_1.5-3               
 [29] fastmap_1.1.0               lazyeval_0.2.2             
 [31] cli_3.6.1                   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 LDlinkR_1.2.3              
 [63] yaml_2.3.5                  curl_4.3.2                 
 [65] memoise_2.0.1               sass_0.4.1                 
 [67] biomaRt_2.54.1              stringi_1.7.6              
 [69] RSQLite_2.3.1               highr_0.9                  
 [71] BiocIO_1.6.0                filelock_1.0.2             
 [73] BiocParallel_1.30.3         rlang_1.1.2                
 [75] pkgconfig_2.0.3             matrixStats_0.62.0         
 [77] bitops_1.0-7                evaluate_0.15              
 [79] lattice_0.20-45             purrr_1.0.2                
 [81] labeling_0.4.2              GenomicAlignments_1.32.0   
 [83] htmlwidgets_1.5.4           cowplot_1.1.1              
 [85] bit_4.0.4                   tidyselect_1.2.0           
 [87] magrittr_2.0.3              R6_2.5.1                   
 [89] generics_0.1.2              DelayedArray_0.22.0        
 [91] DBI_1.2.2                   withr_2.5.0                
 [93] pgenlibr_0.3.3              pillar_1.9.0               
 [95] KEGGREST_1.36.3             RCurl_1.98-1.7             
 [97] mixsqp_0.3-43               tibble_3.2.1               
 [99] crayon_1.5.1                utf8_1.2.2                 
[101] BiocFileCache_2.4.0         plotly_4.10.0              
[103] tzdb_0.4.0                  rmarkdown_2.25             
[105] progress_1.2.2              grid_4.2.0                 
[107] data.table_1.14.2           blob_1.2.3                 
[109] git2r_0.30.1                digest_0.6.29              
[111] tidyr_1.3.0                 httpuv_1.6.5               
[113] munsell_0.5.0               viridisLite_0.4.0          
[115] bslib_0.3.1