Comparing results from predictdb eQTL / Munro eQTL / predictdb eQTL + sQTL / predictdb eQTL + sQTL + Munro rsQTL + apaQTL / all 8 weights

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Last updated: 2024-10-07

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Knit directory: multigroup_ctwas_analysis/

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---

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")

plot_venn <- function(npred,nmunro,noverlap) {
  
  venn.plot <- draw.pairwise.venn(
    area1 = npred,          # Size of Group A
    area2 = nmunro,          # Size of Group B
    cross.area = noverlap,     # Overlap between Group A and Group B
    category = c("Predictdb", "Munro"),  # Labels for the groups
    fill = c("red", "blue"),             # Colors for the groups
    lty = "blank",                       # Line type for the circles
    cex = 2,                             # Font size for the numbers
    cat.cex = 2                          # Font size for the labels
  )
  
  return(venn.plot)
  
}

plot_3venn <- function(es, esra, all8) {
  
  venn.plot <- draw.triple.venn(
      area1 = length(es),
      area2 = length(esra),
      area3 = length(all8),
      n12 = length(intersect(es, esra)),
      n23 = length(intersect(esra, all8)),
      n13 = length(intersect(es, all8)),
      n123 = length(Reduce(intersect, list(es, esra, all8))),
      category = c("e+s", "e+s+rs+apa", "8weights"),
      fill = c("red", "green", "blue"),
      lty = "dashed",
      cex = 2,
      cat.cex = 1.5,
      cat.col = c("red", "green", "blue"),
      scaled = T
      )
  
  return(venn.plot)
  
}

plot_scatter <- function(overlap) {
  
  overlap$pip_diff <- abs(overlap$susie_pip_predictdb - overlap$susie_pip_munro)
  
  overlap$pip_diff_category <- cut(overlap$pip_diff, breaks = c(-Inf, 0.3, 0.5, Inf), 
                                 labels = c("0~0.3", "0.3~0.5", "0.5+"))
  
  p1 <- ggplot(data = overlap, aes(x = susie_pip_predictdb, y = susie_pip_munro, 
                                 color = pip_diff_category)) +
        geom_point() +
        scale_color_manual(values = c("0~0.3" = scales::alpha("blue", 0.3), 
                                      "0.3~0.5" = "orange", 
                                      "0.5+" = "red")) +
        ggtitle("Comparing susie PIPs") + 
        xlab("susie pip -- predictdb") +
        ylab("susie pip -- munro") +
        theme_minimal() +
        labs(color = "PIP diff") +
        guides(color = guide_legend(override.aes = list(size = 3)))

# Modify p2 to color points based on pip_diff categories with alpha for blue
  p2 <- ggplot(data = overlap, aes(x = z_predictdb, y = z_munro, 
                                 color = pip_diff_category)) +
        geom_point() +
        scale_color_manual(values = c("0~0.3" = scales::alpha("blue", 0.3), 
                                     "0.3~0.5" = "orange", 
                                     "0.5+" = "red")) +
        ggtitle("Comparing zscores") + 
        xlab("zscores -- predictdb") +
        ylab("zscores -- munro") +
        theme_minimal() +
        labs(color = "PIP diff") +
        guides(color = guide_legend(override.aes = list(size = 3)))
  
        combined_plot <- grid.arrange(p1, p2, ncol = 2)
  
        return(combined_plot)
}

plot_heatmap <- function(heatmap_data, main) {
  
  rownames(heatmap_data) <- heatmap_data$gene_name
  heatmap_data <- heatmap_data %>% dplyr::select(-gene_name, -combined_pip)
  
  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_piechart <- function(ctwas_parameters, colors) {
  data <- data.frame(
    category = names(ctwas_parameters$attributable_pve),
    percentage = ctwas_parameters$attributable_pve
  )
  
  # 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)
}

process_data_rsapa <- function(finemap_res) {
  
  finemap_res_gene <- finemap_res[finemap_res$type!="SNP",]
  finemap_res_gene_apa <- finemap_res_gene[finemap_res_gene$type == "apaQTL",]
  gene_apa <- unique(finemap_res_gene_apa$gene_name)
  
  finemap_res_gene_rs <- finemap_res_gene[finemap_res_gene$type == "rsQTL",]
  gene_rs <- unique(finemap_res_gene_rs$gene_name)
  
  gene_overlap_rsapa <- intersect(gene_apa,gene_rs)
  
  finemap_res_gene_apa_overlap <- finemap_res_gene_apa %>%
    dplyr::filter(gene_name %in% gene_overlap_rsapa) %>%
    group_by(gene_name) %>%
    dplyr::filter(abs(z) == max(abs(z))) %>%
    ungroup() %>%
    dplyr::select(gene_name, z)
  
  finemap_res_gene_rs_overlap <- finemap_res_gene_rs %>%
    dplyr::filter(gene_name %in% gene_overlap_rsapa) %>%
    group_by(gene_name) %>%
    dplyr::filter(abs(z) == max(abs(z))) %>%
    ungroup() %>%
    dplyr::select(gene_name, z)
  
  rs_apa_overlap_merged <- merge(finemap_res_gene_apa_overlap,finemap_res_gene_rs_overlap, by="gene_name")
  colnames(rs_apa_overlap_merged) <- c("gene_name","z_apa","z_rs")
  
  return(rs_apa_overlap_merged)
  
}

plot_scatter_rsapa <- function(rs_apa_overlap_merged) {
  
  ggplot(data = rs_apa_overlap_merged, aes(x=abs(z_apa), y=abs(z_rs))) +
    geom_point() + 
    ggtitle("Scatter plot for largest abs(z) for the overlapping genes") +
    xlab("largest abs(z) for apa genes") + 
    ylab("largest abs(z) for rs genes") + 
    theme_minimal()
  
}

plot_venn_apars <- function(gene_apa, gene_rs, gene_overlap) {
  
  venn.plot <- draw.pairwise.venn(
    area1 = length(gene_apa),          # Size of Group A
    area2 = length(gene_rs),          # Size of Group B
    cross.area = length(gene_overlap_rsapa),     # Overlap between Group A and Group B
    category = c("#of apa genes", "#of rs genes"),  # Labels for the groups
    fill = c("red", "blue"),             # Colors for the groups
    lty = "blank",                       # Line type for the circles
    cex = 2,                             # Font size for the numbers
    cat.cex = 2,scaled = T,                       # Font size for the labels
    cat.pos = c(0.5, 0.5),                 # Adjust the label position, set to 0 for central alignment
    cat.dist = c(0, 0),                  
  )

  grid.text("number of genes of rsQTL and apaQTL", x = 0.5, y = 0.95, gp = gpar(fontsize = 20, fontface = "bold"))
  

}
  


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
  
  # Rename the columns based on specific conditions
  for (col in colnames(heatmap_data)[3:ncol(heatmap_data)]) {
    if (grepl("rsQTL|apaQTL|isoQTL|eQTL|tssQTL|sQTL", col) && !grepl("eQTL_pred|sQTL_pred", col)) {
      new_column_names[new_column_names == col] <- paste0(col, "_munro")
    }
  }
  
  # Assign the new column names to heatmap_data
  colnames(heatmap_data) <- new_column_names
  
  return(heatmap_data)
}

Settings

6 modalities from Munro

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

LDL-ukb-d-30780_irnt

Comparing predictdb eQTL VS munro eQTL

trait <- "LDL-ukb-d-30780_irnt"
tissue <- "Liver"
### predictdb
results_dir_epred <- paste0("/project/xinhe/xsun/multi_group_ctwas/9.deciding_weights_4traits/results/",trait,"/epred/")

ctwas_res_epred <- readRDS(paste0(results_dir_epred,trait,".ctwas.res.RDS"))
snp_map_epred  <- readRDS(paste0(results_dir_epred,trait,".snp_map.RDS"))
finemap_res_epred <- ctwas_res_epred$finemap_res
finemap_res_epred$molecular_id <- get_molecular_ids(finemap_res_epred)

finemap_res_epred <- anno_finemap_res(finemap_res_epred,
                              snp_map = snp_map_epred,
                              mapping_table = mapping_predictdb,
                              add_gene_annot = TRUE,
                              map_by = "molecular_id",
                              drop_unmapped = TRUE,
                              add_position = TRUE,
                              use_gene_pos = "mid")

2024-10-07 15:17:54 INFO::Annotating fine-mapping result ...
2024-10-07 15:18:00 INFO::Map molecular traits to genes
2024-10-07 15:18:21 INFO::Add gene positions
2024-10-07 15:18:21 INFO::Add SNP positions

finemap_res_gene_epred <- finemap_res_epred[finemap_res_epred$type !="SNP",]

### munro
results_dir_emunro <- paste0("/project/xinhe/xsun/multi_group_ctwas/9.deciding_weights_4traits/results/",trait,"/emunro/")

ctwas_res_emunro <- readRDS(paste0(results_dir_emunro,trait,".ctwas.res.RDS"))
snp_map_emunro  <- readRDS(paste0(results_dir_emunro,trait,".snp_map.RDS"))
finemap_res_emunro <- ctwas_res_emunro$finemap_res
finemap_res_emunro$molecular_id <- get_molecular_ids(finemap_res_emunro)

finemap_res_emunro <- anno_finemap_res(finemap_res_emunro,
                              snp_map = snp_map_emunro,
                              mapping_table = mapping_munro,
                              add_gene_annot = TRUE,
                              map_by = "molecular_id",
                              drop_unmapped = TRUE,
                              add_position = TRUE,
                              use_gene_pos = "mid")

2024-10-07 15:19:38 INFO::Annotating fine-mapping result ...
2024-10-07 15:19:38 INFO::Map molecular traits to genes
2024-10-07 15:19:51 INFO::Add gene positions
2024-10-07 15:19:51 INFO::Add SNP positions

finemap_res_gene_emunro <- finemap_res_emunro[finemap_res_emunro$type !="SNP",]

overlap <- merge(finemap_res_gene_epred, finemap_res_gene_emunro, by = "gene_name")
overlap <- overlap[,c("gene_name","type.x","context.x","region_id.x","z.x","susie_pip.x","z.y","susie_pip.y")]
colnames(overlap) <- c("gene_name","type","context","region_id","z_predictdb","susie_pip_predictdb","z_munro","susie_pip_munro")

DT::datatable(overlap,caption = htmltools::tags$caption( style = 'caption-side: topleft; text-align = left; color:black;','overlapping genes'),options = list(pageLength = 10) )

venn <- plot_venn(npred = nrow(finemap_res_gene_epred), nmunro = nrow(finemap_res_gene_emunro), noverlap = nrow(overlap))

Past versions of unnamed-chunk-2-2.png

Version	Author	Date
b00c776	XSun	2024-10-03

print("For the overlapping genes")

[1] "For the overlapping genes"

scatter_plot <- plot_scatter(overlap = overlap)

Past versions of unnamed-chunk-3-1.png

Version	Author	Date
11796cd	XSun	2024-10-03
b00c776	XSun	2024-10-03

Comparing predictdb e+s VS predictdb e+s + Munro rs+apa VS all 8 weights

gwas_n <- samplesize[trait]

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")

2024-10-07 15:21:03 INFO::Annotating fine-mapping result ...
2024-10-07 15:21:04 INFO::Map molecular traits to genes
2024-10-07 15:21:05 INFO::Split PIPs for molecular traits mapped to multiple genes
2024-10-07 15:21:12 INFO::Add gene positions
2024-10-07 15:21:12 INFO::Add SNP positions

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

2024-10-07 15:21:27 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")

2024-10-07 15:22:14 INFO::Annotating fine-mapping result ...
2024-10-07 15:22:14 INFO::Map molecular traits to genes
2024-10-07 15:22:15 INFO::Split PIPs for molecular traits mapped to multiple genes
2024-10-07 15:22:23 INFO::Add gene positions
2024-10-07 15:22:23 INFO::Add SNP positions

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

2024-10-07 15:22:38 INFO::Limit gene results to credible sets

results_dir_8W <- paste0("/project/xinhe/xsun/multi_group_ctwas/9.deciding_weights_4traits/results/",trait,"/8W/")

snp_map_8W <- readRDS(paste0(results_dir_8W,trait,".snp_map.RDS"))
ctwas_res_8W <- readRDS(paste0(results_dir_8W,trait,".ctwas.res.RDS"))

param_8W <- ctwas_res_8W$param
finemap_res_8W <- ctwas_res_8W$finemap_res

p_conv_8W <- make_convergence_plots(param_8W, gwas_n, ncol = 1, colors = colors)

ctwas_parameters_8W <- summarize_param(param_8W, gwas_n)
pve_pie_8W <- plot_piechart(ctwas_parameters = ctwas_parameters_8W, colors = colors)

finemap_res_8W$molecular_id <- get_molecular_ids(finemap_res_8W)
finemap_res_8W <- anno_finemap_res(finemap_res_8W,
                              snp_map = snp_map_8W,
                              mapping_table = mapping_two,
                              add_gene_annot = TRUE,
                              map_by = "molecular_id",
                              drop_unmapped = TRUE,
                              add_position = TRUE,
                              use_gene_pos = "mid")

2024-10-07 15:23:25 INFO::Annotating fine-mapping result ...
2024-10-07 15:23:26 INFO::Map molecular traits to genes
2024-10-07 15:23:27 INFO::Split PIPs for molecular traits mapped to multiple genes
2024-10-07 15:23:35 INFO::Add gene positions
2024-10-07 15:23:35 INFO::Add SNP positions

finemap_res_8W$type <- ifelse(grepl("_pred$", finemap_res_8W$context),
                                         paste0(finemap_res_8W$type, "_pred"),
                                         finemap_res_8W$type)

combined_pip_by_type_8W <- combine_gene_pips(finemap_res =finemap_res_8W,
                                  group_by = "gene_name",
                                  by = "type", 
                                  method = "combine_cs",
                                  filter_cs = T )

2024-10-07 15:23:52 INFO::Limit gene results to credible sets

Parameters

print("each row represents one setting: predictdb e+s, predictdb e+s + Munro apa+rs, all 8 weights accordingly")

[1] "each row represents one setting: predictdb e+s, predictdb e+s + Munro apa+rs, all 8 weights accordingly"

grid.arrange(p_conv_espred,p_conv_4W,p_conv_8W, ncol = 3)

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

Version	Author	Date
b00c776	XSun	2024-10-03

######pve
group_pve_espred <- ctwas_parameters_espred$group_pve
group_pve_espred <- group_pve_espred[-length(group_pve_espred)]
group_pve_espred <- c(group_pve_espred, rep(NA,6))
group_pve_espred <- c(group_pve_espred, ctwas_parameters_espred$total_pve)
names(group_pve_espred) <- c("eQTL_pred","sQTL_pred","apaQTL_munro","rsQTL_munro","isoQTL_munro","tssQTL_munro","eQTL_munro","sQTL_munro","TOTAL")

group_pve_4W <- ctwas_parameters_4W$group_pve
group_pve_4W <- group_pve_4W[-length(group_pve_4W)]
group_pve_4W <- c(group_pve_4W, rep(NA,4))
group_pve_4W <- c(group_pve_4W, ctwas_parameters_4W$total_pve)
names(group_pve_4W) <- c("eQTL_pred","sQTL_pred","apaQTL_munro","rsQTL_munro","isoQTL_munro","tssQTL_munro","eQTL_munro","sQTL_munro","TOTAL")

group_pve_8W <- ctwas_parameters_8W$group_pve
group_pve_8W <- group_pve_8W[-length(group_pve_8W)]
group_pve_8W <- group_pve_8W[c(paste0(tissue,"_pred|eQTL"),paste0(tissue,"_pred|sQTL"),paste0(tissue,"|apaQTL"),paste0(tissue,"|rsQTL"),paste0(tissue,"|isoQTL"),paste0(tissue,"|tssQTL"),paste0(tissue,"|eQTL"),paste0(tissue,"|sQTL"))]
group_pve_8W <- c(group_pve_8W, ctwas_parameters_8W$total_pve)
names(group_pve_8W) <- c("eQTL_pred","sQTL_pred","apaQTL_munro","rsQTL_munro","isoQTL_munro","tssQTL_munro","eQTL_munro","sQTL_munro","TOTAL")

grouppve <- cbind(group_pve_espred,group_pve_4W,group_pve_8W)
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,6))
names(group_size_espred) <- c("eQTL_pred","sQTL_pred","apaQTL_munro","rsQTL_munro","isoQTL_munro","tssQTL_munro","eQTL_munro","sQTL_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,4))
names(group_size_4W) <- c("eQTL_pred","sQTL_pred","apaQTL_munro","rsQTL_munro","isoQTL_munro","tssQTL_munro","eQTL_munro","sQTL_munro")

group_size_8W <- ctwas_parameters_8W$group_size
group_size_8W <- group_size_8W[-length(group_size_8W)]
group_size_8W <- group_size_8W[c(paste0(tissue,"_pred|eQTL"),paste0(tissue,"_pred|sQTL"),paste0(tissue,"|apaQTL"),paste0(tissue,"|rsQTL"),paste0(tissue,"|isoQTL"),paste0(tissue,"|tssQTL"),paste0(tissue,"|eQTL"),paste0(tissue,"|sQTL"))]
names(group_size_8W) <- c("eQTL_pred","sQTL_pred","apaQTL_munro","rsQTL_munro","isoQTL_munro","tssQTL_munro","eQTL_munro","sQTL_munro")

groupsize <- cbind(group_size_espred,group_size_4W,group_size_8W)

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, all 8 weights accordingly")

[1] "each pie chart represents one setting: predictdb e+s, predictdb e+s + Munro apa+rs, all 8 weights accordingly"

grid.arrange(pve_pie_espred,pve_pie_4W,pve_pie_8W, ncol =3)

Comparing apaQTL and rsQTL

gene_apa <- unique(finemap_res_4W[finemap_res_4W$type == "apaQTL",]$gene_name)
gene_rs <- unique(finemap_res_4W[finemap_res_4W$type == "rsQTL",]$gene_name)
gene_overlap_rsapa <- intersect(gene_apa,gene_rs)
plot_venn_apars(gene_apa = gene_apa,gene_rs = gene_rs,gene_overlap = gene_overlap_rsapa)

Past versions of unnamed-chunk-8-1.png

Version	Author	Date
b00c776	XSun	2024-10-03

rs_apa_overlap_merged <- process_data_rsapa(finemap_res = finemap_res_4W)
plot_scatter_rsapa(rs_apa_overlap_merged = rs_apa_overlap_merged)

Past versions of unnamed-chunk-8-2.png

Version	Author	Date
b00c776	XSun	2024-10-03

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_8W <- combined_pip_by_type_8W[combined_pip_by_type_8W$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 -- all 8 weights", nrow(combined_sig_8W))

[1] "# of genes with PIP > 0.8 = 75 -- all 8 weights"

# DT::datatable(combined_sig_espred,caption = htmltools::tags$caption( style = 'caption-side: topleft; text-align = left; color:black;','High PIP genes -- predictdb e+s'),options = list(pageLength = 10) )
# DT::datatable(combined_sig_4W,caption = htmltools::tags$caption( style = 'caption-side: topleft; text-align = left; color:black;','High PIP genes -- predictdb e+s +  Munro apa + rs'),options = list(pageLength = 10) )
# DT::datatable(combined_sig_8W,caption = htmltools::tags$caption( style = 'caption-side: topleft; text-align = left; color:black;','High PIP genes -- all 8 weights'),options = list(pageLength = 10) )

venn.plot <- plot_3venn(es = combined_sig_espred$gene_name,esra = combined_sig_4W$gene_name,all8 = combined_sig_8W$gene_name)

Past versions of unnamed-chunk-9-1.png

Version	Author	Date
b00c776	XSun	2024-10-03

###1
heatmap_data <- combined_sig_8W[!combined_sig_8W$gene_name %in%combined_sig_espred$gene_name, ]
column_order <- c("gene_name","combined_pip",
                  "eQTL_pred_pip", "sQTL_pred_pip", "rsQTL_pip","apaQTL_pip",
                  "isoQTL_pip", "eQTL_pip","tssQTL_pip","sQTL_pip")
heatmap_data <- heatmap_data[,column_order]
colnames(heatmap_data) <- c("gene_name","combined_pip",
                  "eQTL_pred_pip", "sQTL_pred_pip", "rsQTL_munro_pip","apaQTL_munro_pip",
                  "isoQTL_munro_pip", "eQTL_munro_pip","tssQTL_munro_pip","sQTL_munro_pip")
p1 <- plot_heatmap(heatmap_data = heatmap_data,main = "PIP partition for the genes reported by 8weight setting but not by e+s setting")

###2
heatmap_data <- combined_sig_4W[!combined_sig_4W$gene_name %in%combined_sig_espred$gene_name, ]
column_order <- c("gene_name","combined_pip",
                  "eQTL_pip", "sQTL_pip", "rsQTL_pip","apaQTL_pip")
heatmap_data <- heatmap_data[,column_order]
colnames(heatmap_data) <- c("gene_name","combined_pip",
                  "eQTL_pred_pip", "sQTL_pred_pip", "rsQTL_munro_pip","apaQTL_munro_pip")
p2 <- plot_heatmap(heatmap_data = heatmap_data,main = "PIP partition for the genes reported by 4weight setting but not by e+s setting")

###3
heatmap_data <- combined_sig_8W[!combined_sig_8W$gene_name %in%combined_sig_4W$gene_name, ]
column_order <- c("gene_name","combined_pip",
                  "eQTL_pred_pip", "sQTL_pred_pip", "rsQTL_pip","apaQTL_pip",
                  "isoQTL_pip", "eQTL_pip","tssQTL_pip","sQTL_pip")
heatmap_data <- heatmap_data[,column_order]
colnames(heatmap_data) <- c("gene_name","combined_pip",
                  "eQTL_pred_pip", "sQTL_pred_pip", "rsQTL_munro_pip","apaQTL_munro_pip",
                  "isoQTL_munro_pip", "eQTL_munro_pip","tssQTL_munro_pip","sQTL_munro_pip")
p3 <- plot_heatmap(heatmap_data = heatmap_data,main = "PIP partition for the genes reported by 8weight setting but not by 4weight setting")

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

IBD-ebi-a-GCST004131

Comparing predictdb eQTL VS munro eQTL

trait <- "IBD-ebi-a-GCST004131"
tissue <- "Colon_Transverse"
### predictdb
results_dir_epred <- paste0("/project/xinhe/xsun/multi_group_ctwas/9.deciding_weights_4traits/results/",trait,"/epred/")

ctwas_res_epred <- readRDS(paste0(results_dir_epred,trait,".ctwas.res.RDS"))
snp_map_epred  <- readRDS(paste0(results_dir_epred,trait,".snp_map.RDS"))
finemap_res_epred <- ctwas_res_epred$finemap_res
finemap_res_epred$molecular_id <- get_molecular_ids(finemap_res_epred)

finemap_res_epred <- anno_finemap_res(finemap_res_epred,
                              snp_map = snp_map_epred,
                              mapping_table = mapping_predictdb,
                              add_gene_annot = TRUE,
                              map_by = "molecular_id",
                              drop_unmapped = TRUE,
                              add_position = TRUE,
                              use_gene_pos = "mid")

2024-10-07 15:24:44 INFO::Annotating fine-mapping result ...
2024-10-07 15:24:44 INFO::Map molecular traits to genes
2024-10-07 15:24:49 INFO::Add gene positions
2024-10-07 15:24:49 INFO::Add SNP positions

finemap_res_gene_epred <- finemap_res_epred[finemap_res_epred$type !="SNP",]

### munro
results_dir_emunro <- paste0("/project/xinhe/xsun/multi_group_ctwas/9.deciding_weights_4traits/results/",trait,"/emunro/")

ctwas_res_emunro <- readRDS(paste0(results_dir_emunro,trait,".ctwas.res.RDS"))
snp_map_emunro  <- readRDS(paste0(results_dir_emunro,trait,".snp_map.RDS"))
finemap_res_emunro <- ctwas_res_emunro$finemap_res
finemap_res_emunro$molecular_id <- get_molecular_ids(finemap_res_emunro)

finemap_res_emunro <- anno_finemap_res(finemap_res_emunro,
                              snp_map = snp_map_emunro,
                              mapping_table = mapping_munro,
                              add_gene_annot = TRUE,
                              map_by = "molecular_id",
                              drop_unmapped = TRUE,
                              add_position = TRUE,
                              use_gene_pos = "mid")

2024-10-07 15:25:49 INFO::Annotating fine-mapping result ...
2024-10-07 15:25:49 INFO::Map molecular traits to genes
2024-10-07 15:25:54 INFO::Add gene positions
2024-10-07 15:25:54 INFO::Add SNP positions

finemap_res_gene_emunro <- finemap_res_emunro[finemap_res_emunro$type !="SNP",]

overlap <- merge(finemap_res_gene_epred, finemap_res_gene_emunro, by = "gene_name")
overlap <- overlap[,c("gene_name","type.x","context.x","region_id.x","z.x","susie_pip.x","z.y","susie_pip.y")]
colnames(overlap) <- c("gene_name","type","context","region_id","z_predictdb","susie_pip_predictdb","z_munro","susie_pip_munro")

DT::datatable(overlap,caption = htmltools::tags$caption( style = 'caption-side: topleft; text-align = left; color:black;','overlapping genes'),options = list(pageLength = 10) )

venn <- plot_venn(npred = nrow(finemap_res_gene_epred), nmunro = nrow(finemap_res_gene_emunro), noverlap = nrow(overlap))

print("For the overlapping genes")

[1] "For the overlapping genes"

scatter_plot <- plot_scatter(overlap = overlap)

Comparing predictdb e+s VS predictdb e+s + Munro rs+apa VS all 8 weights

gwas_n <- samplesize[trait]

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")

2024-10-07 15:26:57 INFO::Annotating fine-mapping result ...
2024-10-07 15:26:57 INFO::Map molecular traits to genes
2024-10-07 15:26:58 INFO::Split PIPs for molecular traits mapped to multiple genes
2024-10-07 15:27:03 INFO::Add gene positions
2024-10-07 15:27:04 INFO::Add SNP positions

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

2024-10-07 15:27:13 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")

2024-10-07 15:27:59 INFO::Annotating fine-mapping result ...
2024-10-07 15:27:59 INFO::Map molecular traits to genes
2024-10-07 15:28:00 INFO::Split PIPs for molecular traits mapped to multiple genes
2024-10-07 15:28:06 INFO::Add gene positions
2024-10-07 15:28:06 INFO::Add SNP positions

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

2024-10-07 15:28:15 INFO::Limit gene results to credible sets

results_dir_8W <- paste0("/project/xinhe/xsun/multi_group_ctwas/9.deciding_weights_4traits/results/",trait,"/8W/")

snp_map_8W <- readRDS(paste0(results_dir_8W,trait,".snp_map.RDS"))
ctwas_res_8W <- readRDS(paste0(results_dir_8W,trait,".ctwas.res.RDS"))

param_8W <- ctwas_res_8W$param
finemap_res_8W <- ctwas_res_8W$finemap_res

p_conv_8W <- make_convergence_plots(param_8W, gwas_n, ncol = 1, colors = colors)

ctwas_parameters_8W <- summarize_param(param_8W, gwas_n)
pve_pie_8W <- plot_piechart(ctwas_parameters = ctwas_parameters_8W, colors = colors)

finemap_res_8W$molecular_id <- get_molecular_ids(finemap_res_8W)
finemap_res_8W <- anno_finemap_res(finemap_res_8W,
                              snp_map = snp_map_8W,
                              mapping_table = mapping_two,
                              add_gene_annot = TRUE,
                              map_by = "molecular_id",
                              drop_unmapped = TRUE,
                              add_position = TRUE,
                              use_gene_pos = "mid")

2024-10-07 15:28:58 INFO::Annotating fine-mapping result ...
2024-10-07 15:28:58 INFO::Map molecular traits to genes
2024-10-07 15:28:59 INFO::Split PIPs for molecular traits mapped to multiple genes
2024-10-07 15:29:05 INFO::Add gene positions
2024-10-07 15:29:05 INFO::Add SNP positions

finemap_res_8W$type <- ifelse(grepl("_pred$", finemap_res_8W$context),
                                         paste0(finemap_res_8W$type, "_pred"),
                                         finemap_res_8W$type)

combined_pip_by_type_8W <- combine_gene_pips(finemap_res =finemap_res_8W,
                                  group_by = "gene_name",
                                  by = "type",
                                  method = "combine_cs",
                                  filter_cs = T )

2024-10-07 15:29:21 INFO::Limit gene results to credible sets

Parameters

print("each row represents one setting: predictdb e+s, predictdb e+s + Munro apa+rs, all 8 weights accordingly")

[1] "each row represents one setting: predictdb e+s, predictdb e+s + Munro apa+rs, all 8 weights accordingly"

grid.arrange(p_conv_espred,p_conv_4W,p_conv_8W, ncol = 3)

Past versions of unnamed-chunk-16-1.png

Version	Author	Date
b00c776	XSun	2024-10-03

######pve
group_pve_espred <- ctwas_parameters_espred$group_pve
group_pve_espred <- group_pve_espred[-length(group_pve_espred)]
group_pve_espred <- c(group_pve_espred, rep(NA,6))
group_pve_espred <- c(group_pve_espred, ctwas_parameters_espred$total_pve)
names(group_pve_espred) <- c("eQTL_pred","sQTL_pred","apaQTL_munro","rsQTL_munro","isoQTL_munro","tssQTL_munro","eQTL_munro","sQTL_munro","TOTAL")

group_pve_4W <- ctwas_parameters_4W$group_pve
group_pve_4W <- group_pve_4W[-length(group_pve_4W)]
group_pve_4W <- c(group_pve_4W, rep(NA,4))
group_pve_4W <- c(group_pve_4W, ctwas_parameters_4W$total_pve)
names(group_pve_4W) <- c("eQTL_pred","sQTL_pred","apaQTL_munro","rsQTL_munro","isoQTL_munro","tssQTL_munro","eQTL_munro","sQTL_munro","TOTAL")

group_pve_8W <- ctwas_parameters_8W$group_pve
group_pve_8W <- group_pve_8W[-length(group_pve_8W)]
group_pve_8W <- group_pve_8W[c(paste0(tissue,"_pred|eQTL"),paste0(tissue,"_pred|sQTL"),paste0(tissue,"|apaQTL"),paste0(tissue,"|rsQTL"),paste0(tissue,"|isoQTL"),paste0(tissue,"|tssQTL"),paste0(tissue,"|eQTL"),paste0(tissue,"|sQTL"))]


group_pve_8W <- c(group_pve_8W, ctwas_parameters_8W$total_pve)
names(group_pve_8W) <- c("eQTL_pred","sQTL_pred","apaQTL_munro","rsQTL_munro","isoQTL_munro","tssQTL_munro","eQTL_munro","sQTL_munro","TOTAL")

grouppve <- cbind(group_pve_espred,group_pve_4W,group_pve_8W)
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,6))
names(group_size_espred) <- c("eQTL_pred","sQTL_pred","apaQTL_munro","rsQTL_munro","isoQTL_munro","tssQTL_munro","eQTL_munro","sQTL_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,4))
names(group_size_4W) <- c("eQTL_pred","sQTL_pred","apaQTL_munro","rsQTL_munro","isoQTL_munro","tssQTL_munro","eQTL_munro","sQTL_munro")

group_size_8W <- ctwas_parameters_8W$group_size
group_size_8W <- group_size_8W[-length(group_size_8W)]
group_size_8W <- group_size_8W[c(paste0(tissue,"_pred|eQTL"),paste0(tissue,"_pred|sQTL"),paste0(tissue,"|apaQTL"),paste0(tissue,"|rsQTL"),paste0(tissue,"|isoQTL"),paste0(tissue,"|tssQTL"),paste0(tissue,"|eQTL"),paste0(tissue,"|sQTL"))]
names(group_size_8W) <- c("eQTL_pred","sQTL_pred","apaQTL_munro","rsQTL_munro","isoQTL_munro","tssQTL_munro","eQTL_munro","sQTL_munro")

groupsize <- cbind(group_size_espred,group_size_4W,group_size_8W)

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, all 8 weights accordingly")

[1] "each pie chart represents one setting: predictdb e+s, predictdb e+s + Munro apa+rs, all 8 weights accordingly"

grid.arrange(pve_pie_espred,pve_pie_4W,pve_pie_8W, ncol =3)

Comparing apaQTL and rsQTL

gene_apa <- unique(finemap_res_4W[finemap_res_4W$type == "apaQTL",]$gene_name)
gene_rs <- unique(finemap_res_4W[finemap_res_4W$type == "rsQTL",]$gene_name)
gene_overlap_rsapa <- intersect(gene_apa,gene_rs)
plot_venn_apars(gene_apa = gene_apa,gene_rs = gene_rs,gene_overlap = gene_overlap_rsapa)

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

Version	Author	Date
b00c776	XSun	2024-10-03

rs_apa_overlap_merged <- process_data_rsapa(finemap_res = finemap_res_4W)
plot_scatter_rsapa(rs_apa_overlap_merged = rs_apa_overlap_merged)

### 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_8W <- combined_pip_by_type_8W[combined_pip_by_type_8W$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 -- all 8 weights", nrow(combined_sig_8W))

[1] "# of genes with PIP > 0.8 = 43 -- all 8 weights"

# DT::datatable(combined_sig_espred,caption = htmltools::tags$caption( style = 'caption-side: topleft; text-align = left; color:black;','High PIP genes -- predictdb e+s'),options = list(pageLength = 10) )
# DT::datatable(combined_sig_4W,caption = htmltools::tags$caption( style = 'caption-side: topleft; text-align = left; color:black;','High PIP genes -- predictdb e+s +  Munro apa + rs'),options = list(pageLength = 10) )
# DT::datatable(combined_sig_8W,caption = htmltools::tags$caption( style = 'caption-side: topleft; text-align = left; color:black;','High PIP genes -- all 8 weights'),options = list(pageLength = 10) )

venn.plot <- plot_3venn(es = combined_sig_espred$gene_name,esra = combined_sig_4W$gene_name,all8 = combined_sig_8W$gene_name)

###1
heatmap_data <- combined_sig_8W[!combined_sig_8W$gene_name %in%combined_sig_espred$gene_name, ]
column_order <- c("gene_name","combined_pip",
                  "eQTL_pred_pip", "sQTL_pred_pip", "rsQTL_pip","apaQTL_pip",
                  "isoQTL_pip", "eQTL_pip","tssQTL_pip","sQTL_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 8weight setting but not by e+s setting")

###2
heatmap_data <- combined_sig_4W[!combined_sig_4W$gene_name %in%combined_sig_espred$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 4weight setting but not by e+s setting")

###3
heatmap_data <- combined_sig_8W[!combined_sig_8W$gene_name %in%combined_sig_4W$gene_name, ]
column_order <- c("gene_name","combined_pip",
                  "eQTL_pred_pip", "sQTL_pred_pip", "rsQTL_pip","apaQTL_pip",
                  "isoQTL_pip", "eQTL_pip","tssQTL_pip","sQTL_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 8weight setting but not by 4weight 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
11796cd	XSun	2024-10-03
b00c776	XSun	2024-10-03

SBP-ukb-a-360

Comparing predictdb eQTL VS munro eQTL

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

### predictdb
results_dir_epred <- paste0("/project/xinhe/xsun/multi_group_ctwas/9.deciding_weights_4traits/results/",trait,"/epred/")

ctwas_res_epred <- readRDS(paste0(results_dir_epred,trait,".ctwas.res.RDS"))
snp_map_epred  <- readRDS(paste0(results_dir_epred,trait,".snp_map.RDS"))
finemap_res_epred <- ctwas_res_epred$finemap_res
finemap_res_epred$molecular_id <- get_molecular_ids(finemap_res_epred)

finemap_res_epred <- anno_finemap_res(finemap_res_epred,
                              snp_map = snp_map_epred,
                              mapping_table = mapping_predictdb,
                              add_gene_annot = TRUE,
                              map_by = "molecular_id",
                              drop_unmapped = TRUE,
                              add_position = TRUE,
                              use_gene_pos = "mid")

2024-10-07 15:30:28 INFO::Annotating fine-mapping result ...
2024-10-07 15:30:28 INFO::Map molecular traits to genes
2024-10-07 15:30:37 INFO::Add gene positions
2024-10-07 15:30:37 INFO::Add SNP positions

finemap_res_gene_epred <- finemap_res_epred[finemap_res_epred$type !="SNP",]

### munro
results_dir_emunro <- paste0("/project/xinhe/xsun/multi_group_ctwas/9.deciding_weights_4traits/results/",trait,"/emunro/")

ctwas_res_emunro <- readRDS(paste0(results_dir_emunro,trait,".ctwas.res.RDS"))
snp_map_emunro  <- readRDS(paste0(results_dir_emunro,trait,".snp_map.RDS"))
finemap_res_emunro <- ctwas_res_emunro$finemap_res
finemap_res_emunro$molecular_id <- get_molecular_ids(finemap_res_emunro)

finemap_res_emunro <- anno_finemap_res(finemap_res_emunro,
                              snp_map = snp_map_emunro,
                              mapping_table = mapping_munro,
                              add_gene_annot = TRUE,
                              map_by = "molecular_id",
                              drop_unmapped = TRUE,
                              add_position = TRUE,
                              use_gene_pos = "mid")

2024-10-07 15:31:33 INFO::Annotating fine-mapping result ...
2024-10-07 15:31:36 INFO::Map molecular traits to genes
2024-10-07 15:31:44 INFO::Add gene positions
2024-10-07 15:31:44 INFO::Add SNP positions

finemap_res_gene_emunro <- finemap_res_emunro[finemap_res_emunro$type !="SNP",]

overlap <- merge(finemap_res_gene_epred, finemap_res_gene_emunro, by = "gene_name")
overlap <- overlap[,c("gene_name","type.x","context.x","region_id.x","z.x","susie_pip.x","z.y","susie_pip.y")]
colnames(overlap) <- c("gene_name","type","context","region_id","z_predictdb","susie_pip_predictdb","z_munro","susie_pip_munro")

DT::datatable(overlap,caption = htmltools::tags$caption( style = 'caption-side: topleft; text-align = left; color:black;','overlapping genes'),options = list(pageLength = 10) )

venn <- plot_venn(npred = nrow(finemap_res_gene_epred), nmunro = nrow(finemap_res_gene_emunro), noverlap = nrow(overlap))

print("For the overlapping genes")

[1] "For the overlapping genes"

scatter_plot <- plot_scatter(overlap = overlap)

Comparing predictdb e+s VS predictdb e+s + Munro rs+apa VS all 8 weights

gwas_n <- samplesize[trait]

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")

2024-10-07 15:32:46 INFO::Annotating fine-mapping result ...
2024-10-07 15:32:46 INFO::Map molecular traits to genes
2024-10-07 15:32:48 INFO::Split PIPs for molecular traits mapped to multiple genes
2024-10-07 15:32:57 INFO::Add gene positions
2024-10-07 15:32:57 INFO::Add SNP positions

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

2024-10-07 15:33:17 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")

2024-10-07 15:34:03 INFO::Annotating fine-mapping result ...
2024-10-07 15:34:03 INFO::Map molecular traits to genes
2024-10-07 15:34:04 INFO::Split PIPs for molecular traits mapped to multiple genes
2024-10-07 15:34:14 INFO::Add gene positions
2024-10-07 15:34:15 INFO::Add SNP positions

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

2024-10-07 15:34:43 INFO::Limit gene results to credible sets

results_dir_8W <- paste0("/project/xinhe/xsun/multi_group_ctwas/9.deciding_weights_4traits/results/",trait,"/8W/")

snp_map_8W <- readRDS(paste0(results_dir_8W,trait,".snp_map.RDS"))
ctwas_res_8W <- readRDS(paste0(results_dir_8W,trait,".ctwas.res.RDS"))

param_8W <- ctwas_res_8W$param
finemap_res_8W <- ctwas_res_8W$finemap_res

p_conv_8W <- make_convergence_plots(param_8W, gwas_n, ncol = 1, colors = colors)

ctwas_parameters_8W <- summarize_param(param_8W, gwas_n)
pve_pie_8W <- plot_piechart(ctwas_parameters = ctwas_parameters_8W, colors = colors)

finemap_res_8W$molecular_id <- get_molecular_ids(finemap_res_8W)
finemap_res_8W <- anno_finemap_res(finemap_res_8W,
                              snp_map = snp_map_8W,
                              mapping_table = mapping_two,
                              add_gene_annot = TRUE,
                              map_by = "molecular_id",
                              drop_unmapped = TRUE,
                              add_position = TRUE,
                              use_gene_pos = "mid")

2024-10-07 15:35:29 INFO::Annotating fine-mapping result ...
2024-10-07 15:35:29 INFO::Map molecular traits to genes
2024-10-07 15:35:30 INFO::Split PIPs for molecular traits mapped to multiple genes
2024-10-07 15:35:42 INFO::Add gene positions
2024-10-07 15:35:46 INFO::Add SNP positions

finemap_res_8W$type <- ifelse(grepl("_pred$", finemap_res_8W$context),
                                         paste0(finemap_res_8W$type, "_pred"),
                                         finemap_res_8W$type)

combined_pip_by_type_8W <- combine_gene_pips(finemap_res =finemap_res_8W,
                                  group_by = "gene_name",
                                  by = "type",
                                  method = "combine_cs",
                                  filter_cs = T )

2024-10-07 15:36:15 INFO::Limit gene results to credible sets

Parameters

print("each row represents one setting: predictdb e+s, predictdb e+s + Munro apa+rs, all 8 weights accordingly")

[1] "each row represents one setting: predictdb e+s, predictdb e+s + Munro apa+rs, all 8 weights accordingly"

grid.arrange(p_conv_espred,p_conv_4W,p_conv_8W, ncol = 3)

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

Version	Author	Date
b00c776	XSun	2024-10-03

######pve
group_pve_espred <- ctwas_parameters_espred$group_pve
group_pve_espred <- group_pve_espred[-length(group_pve_espred)]
group_pve_espred <- c(group_pve_espred, rep(NA,6))
group_pve_espred <- c(group_pve_espred, ctwas_parameters_espred$total_pve)
names(group_pve_espred) <- c("eQTL_pred","sQTL_pred","apaQTL_munro","rsQTL_munro","isoQTL_munro","tssQTL_munro","eQTL_munro","sQTL_munro","TOTAL")

group_pve_4W <- ctwas_parameters_4W$group_pve
group_pve_4W <- group_pve_4W[-length(group_pve_4W)]
group_pve_4W <- c(group_pve_4W, rep(NA,4))
group_pve_4W <- c(group_pve_4W, ctwas_parameters_4W$total_pve)
names(group_pve_4W) <- c("eQTL_pred","sQTL_pred","apaQTL_munro","rsQTL_munro","isoQTL_munro","tssQTL_munro","eQTL_munro","sQTL_munro","TOTAL")

group_pve_8W <- ctwas_parameters_8W$group_pve
group_pve_8W <- group_pve_8W[-length(group_pve_8W)]
group_pve_8W <- group_pve_8W[c(paste0(tissue,"_pred|eQTL"),paste0(tissue,"_pred|sQTL"),paste0(tissue,"|apaQTL"),paste0(tissue,"|rsQTL"),paste0(tissue,"|isoQTL"),paste0(tissue,"|tssQTL"),paste0(tissue,"|eQTL"),paste0(tissue,"|sQTL"))]
group_pve_8W <- c(group_pve_8W, ctwas_parameters_8W$total_pve)
names(group_pve_8W) <- c("eQTL_pred","sQTL_pred","apaQTL_munro","rsQTL_munro","isoQTL_munro","tssQTL_munro","eQTL_munro","sQTL_munro","TOTAL")

grouppve <- cbind(group_pve_espred,group_pve_4W,group_pve_8W)
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,6))
names(group_size_espred) <- c("eQTL_pred","sQTL_pred","apaQTL_munro","rsQTL_munro","isoQTL_munro","tssQTL_munro","eQTL_munro","sQTL_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,4))
names(group_size_4W) <- c("eQTL_pred","sQTL_pred","apaQTL_munro","rsQTL_munro","isoQTL_munro","tssQTL_munro","eQTL_munro","sQTL_munro")

group_size_8W <- ctwas_parameters_8W$group_size
group_size_8W <- group_size_8W[-length(group_size_8W)]
group_size_8W <- group_size_8W[c(paste0(tissue,"_pred|eQTL"),paste0(tissue,"_pred|sQTL"),paste0(tissue,"|apaQTL"),paste0(tissue,"|rsQTL"),paste0(tissue,"|isoQTL"),paste0(tissue,"|tssQTL"),paste0(tissue,"|eQTL"),paste0(tissue,"|sQTL"))]
names(group_size_8W) <- c("eQTL_pred","sQTL_pred","apaQTL_munro","rsQTL_munro","isoQTL_munro","tssQTL_munro","eQTL_munro","sQTL_munro")

groupsize <- cbind(group_size_espred,group_size_4W,group_size_8W)

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, all 8 weights accordingly")

[1] "each pie chart represents one setting: predictdb e+s, predictdb e+s + Munro apa+rs, all 8 weights accordingly"

grid.arrange(pve_pie_espred,pve_pie_4W,pve_pie_8W, ncol =3)

Comparing apaQTL and rsQTL

gene_apa <- unique(finemap_res_4W[finemap_res_4W$type == "apaQTL",]$gene_name)
gene_rs <- unique(finemap_res_4W[finemap_res_4W$type == "rsQTL",]$gene_name)
gene_overlap_rsapa <- intersect(gene_apa,gene_rs)
plot_venn_apars(gene_apa = gene_apa,gene_rs = gene_rs,gene_overlap = gene_overlap_rsapa)

rs_apa_overlap_merged <- process_data_rsapa(finemap_res = finemap_res_4W)
plot_scatter_rsapa(rs_apa_overlap_merged = rs_apa_overlap_merged)

Past versions of unnamed-chunk-26-2.png

Version	Author	Date
b00c776	XSun	2024-10-03

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_8W <- combined_pip_by_type_8W[combined_pip_by_type_8W$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 -- all 8 weights", nrow(combined_sig_8W))

[1] "# of genes with PIP > 0.8 = 70 -- all 8 weights"

# DT::datatable(combined_sig_espred,caption = htmltools::tags$caption( style = 'caption-side: topleft; text-align = left; color:black;','High PIP genes -- predictdb e+s'),options = list(pageLength = 10) )
# DT::datatable(combined_sig_4W,caption = htmltools::tags$caption( style = 'caption-side: topleft; text-align = left; color:black;','High PIP genes -- predictdb e+s +  Munro apa + rs'),options = list(pageLength = 10) )
# DT::datatable(combined_sig_8W,caption = htmltools::tags$caption( style = 'caption-side: topleft; text-align = left; color:black;','High PIP genes -- all 8 weights'),options = list(pageLength = 10) )

venn.plot <- plot_3venn(es = combined_sig_espred$gene_name,esra = combined_sig_4W$gene_name,all8 = combined_sig_8W$gene_name)

Past versions of unnamed-chunk-27-1.png

Version	Author	Date
11796cd	XSun	2024-10-03
b00c776	XSun	2024-10-03

###1
heatmap_data <- combined_sig_8W[!combined_sig_8W$gene_name %in%combined_sig_espred$gene_name, ]
column_order <- c("gene_name","combined_pip",
                  "eQTL_pred_pip", "sQTL_pred_pip", "rsQTL_pip","apaQTL_pip",
                  "isoQTL_pip", "eQTL_pip","tssQTL_pip","sQTL_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 8weight setting but not by e+s setting")

###2
heatmap_data <- combined_sig_4W[!combined_sig_4W$gene_name %in%combined_sig_espred$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 4weight setting but not by e+s setting")

###3
heatmap_data <- combined_sig_8W[!combined_sig_8W$gene_name %in%combined_sig_4W$gene_name, ]
column_order <- c("gene_name","combined_pip",
                  "eQTL_pred_pip", "sQTL_pred_pip", "rsQTL_pip","apaQTL_pip",
                  "isoQTL_pip", "eQTL_pip","tssQTL_pip","sQTL_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 8weight setting but not by 4weight setting")

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

WBC-ieu-b-30

Comparing predictdb eQTL VS munro eQTL

trait <- "WBC-ieu-b-30"
tissue <- "Whole_Blood"
### predictdb
results_dir_epred <- paste0("/project/xinhe/xsun/multi_group_ctwas/9.deciding_weights_4traits/results/",trait,"/epred/")

ctwas_res_epred <- readRDS(paste0(results_dir_epred,trait,".ctwas.res.RDS"))
snp_map_epred  <- readRDS(paste0(results_dir_epred,trait,".snp_map.RDS"))
finemap_res_epred <- ctwas_res_epred$finemap_res
finemap_res_epred$molecular_id <- get_molecular_ids(finemap_res_epred)

finemap_res_epred <- anno_finemap_res(finemap_res_epred,
                              snp_map = snp_map_epred,
                              mapping_table = mapping_predictdb,
                              add_gene_annot = TRUE,
                              map_by = "molecular_id",
                              drop_unmapped = TRUE,
                              add_position = TRUE,
                              use_gene_pos = "mid")

2024-10-07 15:37:29 INFO::Annotating fine-mapping result ...
2024-10-07 15:37:29 INFO::Map molecular traits to genes
2024-10-07 15:37:46 INFO::Add gene positions
2024-10-07 15:37:46 INFO::Add SNP positions

finemap_res_gene_epred <- finemap_res_epred[finemap_res_epred$type !="SNP",]

### munro
results_dir_emunro <- paste0("/project/xinhe/xsun/multi_group_ctwas/9.deciding_weights_4traits/results/",trait,"/emunro/")

ctwas_res_emunro <- readRDS(paste0(results_dir_emunro,trait,".ctwas.res.RDS"))
snp_map_emunro  <- readRDS(paste0(results_dir_emunro,trait,".snp_map.RDS"))
finemap_res_emunro <- ctwas_res_emunro$finemap_res
finemap_res_emunro$molecular_id <- get_molecular_ids(finemap_res_emunro)

finemap_res_emunro <- anno_finemap_res(finemap_res_emunro,
                              snp_map = snp_map_emunro,
                              mapping_table = mapping_munro,
                              add_gene_annot = TRUE,
                              map_by = "molecular_id",
                              drop_unmapped = TRUE,
                              add_position = TRUE,
                              use_gene_pos = "mid")

2024-10-07 15:39:18 INFO::Annotating fine-mapping result ...
2024-10-07 15:39:18 INFO::Map molecular traits to genes
2024-10-07 15:39:35 INFO::Add gene positions
2024-10-07 15:39:35 INFO::Add SNP positions

finemap_res_gene_emunro <- finemap_res_emunro[finemap_res_emunro$type !="SNP",]

overlap <- merge(finemap_res_gene_epred, finemap_res_gene_emunro, by = "gene_name")
overlap <- overlap[,c("gene_name","type.x","context.x","region_id.x","z.x","susie_pip.x","z.y","susie_pip.y")]
colnames(overlap) <- c("gene_name","type","context","region_id","z_predictdb","susie_pip_predictdb","z_munro","susie_pip_munro")

DT::datatable(overlap,caption = htmltools::tags$caption( style = 'caption-side: topleft; text-align = left; color:black;','overlapping genes'),options = list(pageLength = 10) )

venn <- plot_venn(npred = nrow(finemap_res_gene_epred), nmunro = nrow(finemap_res_gene_emunro), noverlap = nrow(overlap))

print("For the overlapping genes")

[1] "For the overlapping genes"

scatter_plot <- plot_scatter(overlap = overlap)

Comparing predictdb e+s VS predictdb e+s + Munro rs+apa VS all 8 weights

gwas_n <- samplesize[trait]

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")

2024-10-07 15:41:05 INFO::Annotating fine-mapping result ...
2024-10-07 15:41:05 INFO::Map molecular traits to genes
2024-10-07 15:41:07 INFO::Split PIPs for molecular traits mapped to multiple genes
2024-10-07 15:41:26 INFO::Add gene positions
2024-10-07 15:41:27 INFO::Add SNP positions

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

2024-10-07 15:41:49 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")

2024-10-07 15:42:59 INFO::Annotating fine-mapping result ...
2024-10-07 15:42:59 INFO::Map molecular traits to genes
2024-10-07 15:43:00 INFO::Split PIPs for molecular traits mapped to multiple genes
2024-10-07 15:43:17 INFO::Add gene positions
2024-10-07 15:43:18 INFO::Add SNP positions

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

2024-10-07 15:43:38 INFO::Limit gene results to credible sets

results_dir_8W <- paste0("/project/xinhe/xsun/multi_group_ctwas/9.deciding_weights_4traits/results/",trait,"/8W/")

snp_map_8W <- readRDS(paste0(results_dir_8W,trait,".snp_map.RDS"))
ctwas_res_8W <- readRDS(paste0(results_dir_8W,trait,".ctwas.res.RDS"))

param_8W <- ctwas_res_8W$param
finemap_res_8W <- ctwas_res_8W$finemap_res

p_conv_8W <- make_convergence_plots(param_8W, gwas_n, ncol = 1, colors = colors)

ctwas_parameters_8W <- summarize_param(param_8W, gwas_n)
pve_pie_8W <- plot_piechart(ctwas_parameters = ctwas_parameters_8W, colors = colors)

finemap_res_8W$molecular_id <- get_molecular_ids(finemap_res_8W)
finemap_res_8W <- anno_finemap_res(finemap_res_8W,
                              snp_map = snp_map_8W,
                              mapping_table = mapping_two,
                              add_gene_annot = TRUE,
                              map_by = "molecular_id",
                              drop_unmapped = TRUE,
                              add_position = TRUE,
                              use_gene_pos = "mid")

2024-10-07 15:44:37 INFO::Annotating fine-mapping result ...
2024-10-07 15:44:37 INFO::Map molecular traits to genes
2024-10-07 15:44:38 INFO::Split PIPs for molecular traits mapped to multiple genes
2024-10-07 15:45:03 INFO::Add gene positions
2024-10-07 15:45:04 INFO::Add SNP positions

finemap_res_8W$type <- ifelse(grepl("_pred$", finemap_res_8W$context),
                                         paste0(finemap_res_8W$type, "_pred"),
                                         finemap_res_8W$type)

combined_pip_by_type_8W <- combine_gene_pips(finemap_res =finemap_res_8W,
                                  group_by = "gene_name",
                                  by = "type",
                                  method = "combine_cs",
                                  filter_cs = T )

2024-10-07 15:45:30 INFO::Limit gene results to credible sets

Parameters

print("each row represents one setting: predictdb e+s, predictdb e+s + Munro apa+rs, all 8 weights accordingly")

[1] "each row represents one setting: predictdb e+s, predictdb e+s + Munro apa+rs, all 8 weights accordingly"

grid.arrange(p_conv_espred,p_conv_4W,p_conv_8W, ncol = 3)

######pve
group_pve_espred <- ctwas_parameters_espred$group_pve
group_pve_espred <- group_pve_espred[-length(group_pve_espred)]
group_pve_espred <- c(group_pve_espred, rep(NA,6))
group_pve_espred <- c(group_pve_espred, ctwas_parameters_espred$total_pve)
names(group_pve_espred) <- c("eQTL_pred","sQTL_pred","apaQTL_munro","rsQTL_munro","isoQTL_munro","tssQTL_munro","eQTL_munro","sQTL_munro","TOTAL")

group_pve_4W <- ctwas_parameters_4W$group_pve
group_pve_4W <- group_pve_4W[-length(group_pve_4W)]
group_pve_4W <- c(group_pve_4W, rep(NA,4))
group_pve_4W <- c(group_pve_4W, ctwas_parameters_4W$total_pve)
names(group_pve_4W) <- c("eQTL_pred","sQTL_pred","apaQTL_munro","rsQTL_munro","isoQTL_munro","tssQTL_munro","eQTL_munro","sQTL_munro","TOTAL")

group_pve_8W <- ctwas_parameters_8W$group_pve
group_pve_8W <- group_pve_8W[-length(group_pve_8W)]
group_pve_8W <- group_pve_8W[c(paste0(tissue,"_pred|eQTL"),paste0(tissue,"_pred|sQTL"),paste0(tissue,"|apaQTL"),paste0(tissue,"|rsQTL"),paste0(tissue,"|isoQTL"),paste0(tissue,"|tssQTL"),paste0(tissue,"|eQTL"),paste0(tissue,"|sQTL"))]
group_pve_8W <- c(group_pve_8W, ctwas_parameters_8W$total_pve)
names(group_pve_8W) <- c("eQTL_pred","sQTL_pred","apaQTL_munro","rsQTL_munro","isoQTL_munro","tssQTL_munro","eQTL_munro","sQTL_munro","TOTAL")

grouppve <- cbind(group_pve_espred,group_pve_4W,group_pve_8W)
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,6))
names(group_size_espred) <- c("eQTL_pred","sQTL_pred","apaQTL_munro","rsQTL_munro","isoQTL_munro","tssQTL_munro","eQTL_munro","sQTL_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,4))
names(group_size_4W) <- c("eQTL_pred","sQTL_pred","apaQTL_munro","rsQTL_munro","isoQTL_munro","tssQTL_munro","eQTL_munro","sQTL_munro")

group_size_8W <- ctwas_parameters_8W$group_size
group_size_8W <- group_size_8W[-length(group_size_8W)]
group_size_8W <- group_size_8W[c(paste0(tissue,"_pred|eQTL"),paste0(tissue,"_pred|sQTL"),paste0(tissue,"|apaQTL"),paste0(tissue,"|rsQTL"),paste0(tissue,"|isoQTL"),paste0(tissue,"|tssQTL"),paste0(tissue,"|eQTL"),paste0(tissue,"|sQTL"))]
names(group_size_8W) <- c("eQTL_pred","sQTL_pred","apaQTL_munro","rsQTL_munro","isoQTL_munro","tssQTL_munro","eQTL_munro","sQTL_munro")

groupsize <- cbind(group_size_espred,group_size_4W,group_size_8W)

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, all 8 weights accordingly")

[1] "each pie chart represents one setting: predictdb e+s, predictdb e+s + Munro apa+rs, all 8 weights accordingly"

grid.arrange(pve_pie_espred,pve_pie_4W,pve_pie_8W, ncol =3)

Comparing apaQTL and rsQTL

gene_apa <- unique(finemap_res_4W[finemap_res_4W$type == "apaQTL",]$gene_name)
gene_rs <- unique(finemap_res_4W[finemap_res_4W$type == "rsQTL",]$gene_name)
gene_overlap_rsapa <- intersect(gene_apa,gene_rs)
plot_venn_apars(gene_apa = gene_apa,gene_rs = gene_rs,gene_overlap = gene_overlap_rsapa)

rs_apa_overlap_merged <- process_data_rsapa(finemap_res = finemap_res_4W)
plot_scatter_rsapa(rs_apa_overlap_merged = rs_apa_overlap_merged)

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_8W <- combined_pip_by_type_8W[combined_pip_by_type_8W$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 -- all 8 weights", nrow(combined_sig_8W))

[1] "# of genes with PIP > 0.8 = 232 -- all 8 weights"

# DT::datatable(combined_sig_espred,caption = htmltools::tags$caption( style = 'caption-side: topleft; text-align = left; color:black;','High PIP genes -- predictdb e+s'),options = list(pageLength = 10) )
# DT::datatable(combined_sig_4W,caption = htmltools::tags$caption( style = 'caption-side: topleft; text-align = left; color:black;','High PIP genes -- predictdb e+s +  Munro apa + rs'),options = list(pageLength = 10) )
# DT::datatable(combined_sig_8W,caption = htmltools::tags$caption( style = 'caption-side: topleft; text-align = left; color:black;','High PIP genes -- all 8 weights'),options = list(pageLength = 10) )

venn.plot <- plot_3venn(es = combined_sig_espred$gene_name,esra = combined_sig_4W$gene_name,all8 = combined_sig_8W$gene_name)

###1
heatmap_data <- combined_sig_8W[!combined_sig_8W$gene_name %in%combined_sig_espred$gene_name, ]
column_order <- c("gene_name","combined_pip",
                  "eQTL_pred_pip", "sQTL_pred_pip", "rsQTL_pip","apaQTL_pip",
                  "isoQTL_pip", "eQTL_pip","tssQTL_pip","sQTL_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 8weight setting but not by e+s setting")

###2
heatmap_data <- combined_sig_4W[!combined_sig_4W$gene_name %in%combined_sig_espred$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 4weight setting but not by e+s setting")

###3
heatmap_data <- combined_sig_8W[!combined_sig_8W$gene_name %in%combined_sig_4W$gene_name, ]
column_order <- c("gene_name","combined_pip",
                  "eQTL_pred_pip", "sQTL_pred_pip", "rsQTL_pip","apaQTL_pip",
                  "isoQTL_pip", "eQTL_pip","tssQTL_pip","sQTL_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 8weight setting but not by 4weight setting")

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

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] dplyr_1.1.4               pheatmap_1.0.12          
 [3] gridExtra_2.3             ggplot2_3.5.1            
 [5] VennDiagram_1.7.3         futile.logger_1.4.3      
 [7] EnsDb.Hsapiens.v86_2.99.0 ensembldb_2.20.2         
 [9] AnnotationFilter_1.20.0   GenomicFeatures_1.48.3   
[11] AnnotationDbi_1.58.0      Biobase_2.56.0           
[13] GenomicRanges_1.48.0      GenomeInfoDb_1.39.9      
[15] IRanges_2.30.0            S4Vectors_0.34.0         
[17] BiocGenerics_0.42.0       ctwas_0.4.14             

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