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Last updated: 2025-04-29

Checks: 6 1

Knit directory: multigroup_ctwas_analysis/

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---

library(ctwas)
library(dplyr)
library(ggplot2)
library(ggrepel)
library(egg)
library(gridExtra)
library(grid)

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

thin <- 1
vgs <- "shared_all"
L <-5

fix_panel_size <- function(plot, width = 2.1, height = 2) {
  set_panel_size(plot, 
                 width = unit(width, "in"), 
                 height = unit(height, "in"))
}

folder_results_multiqtl <- "/project/xinhe/xsun/multi_group_ctwas/19.diff_qtls/snakemake_outputs/"

plot_piechart_topn <- function(ctwas_parameters, colors, by, title, n_tissue=10) {
  # Define fixed colors for QTL types
  qtl_colors <- c(
    eQTL = "#ff7f0e",
    sQTL = "#2ca02c",
    stQTL = "#d62728",
    edQTL = "#9467bd"
  )
  
  # 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)
  } else if (by == "context") {
    data <- data %>%
      group_by(context) %>%
      summarize(percentage = sum(percentage)) %>%
      mutate(category = context)
  } else {
    stop("Invalid 'by' parameter. Use 'type' or 'context'.")
  }
  
  # Calculate percentage labels
  data$percentage_label <- paste0(round(data$percentage * 100, 2), "%")
  
  if(nrow(data) > (n_tissue +1)){
    data <- data %>%
      filter(context != "SNP") %>%
      arrange(desc(percentage)) %>%
      mutate(rank = row_number()) %>%
      mutate(context = ifelse(rank <= n_tissue, context, "Other_Tissues")) %>%
      group_by(context) %>%
      summarise(percentage = sum(percentage), .groups = "drop") %>%
      bind_rows(data %>% filter(context == "SNP") %>% select(context, percentage)) %>%
      mutate(
        category = context,
        percentage_label = paste0(sprintf("%.2f", percentage * 100), "%")
      ) %>%
      arrange(desc(percentage))
    
    sorted_levels <- data %>%
      mutate(sort_key = case_when(
        category == "Other_Tissues" ~ 1,
        category == "SNP" ~ 2,
        TRUE ~ 0
      )) %>%
      arrange(sort_key, desc(percentage)) %>%
      pull(category)
  } else {
    sorted_levels <- data %>%
      arrange((category == "SNP"), desc(percentage)) %>%
      pull(category)
  }
  
  data$category <- factor(data$category, levels = sorted_levels)
  
  # Order data for positioning
  data <- data %>% 
    arrange(category) %>%
    mutate(
      cumulative = cumsum(percentage),
      midpoint = cumulative - percentage / 2
    )
  
  # Prepare colors
  categories <- levels(data$category)
  has_snp <- "SNP" %in% categories
  other_cats <- setdiff(categories, "SNP")
  
  color_vec <- c()
  if (has_snp) color_vec["SNP"] <- "#1f77b4"
  
  # Split categories into QTL and non-QTL
  qtl_cats <- other_cats[other_cats %in% names(qtl_colors)]
  non_qtl_cats <- other_cats[!other_cats %in% names(qtl_colors)]
  
  # Assign QTL colors
  for (cat in qtl_cats) color_vec[cat] <- qtl_colors[cat]
  
  # Assign non-QTL colors
  if (length(non_qtl_cats) > 0) {
    if (is.null(names(colors))) {
      colors_non_qtl <- rep(colors, length.out = length(non_qtl_cats))
      color_vec <- c(color_vec, setNames(colors_non_qtl, non_qtl_cats))
    } else {
      for (cat in non_qtl_cats) {
        color_vec[cat] <- ifelse(cat %in% names(colors), colors[cat], "#808080")
      }
    }
  }
  
  # Calculate label positions
  data <- data %>%
    mutate(
      y_pos = midpoint,
      angle = 0,
      hjust = 0.5
    )
  
  # Create pie chart
  pie <- ggplot(data, aes(x = "", y = percentage, fill = category)) +
    geom_bar(stat = "identity", width = 1) +
    coord_polar("y", start = 0) +
    theme_void() +
    geom_text_repel(
      aes(
        y = 1 - y_pos,
        label = percentage_label,
        angle = angle
      ),
      size = 3,
      nudge_x = 0.8,
      segment.size = 0.3,
      segment.color = "gray40",
      box.padding = 0.2,
      min.segment.length = 0.1,
      hjust = 0.5,
      vjust = 0.5
    ) +
    scale_fill_manual(values = color_vec) +
    labs(fill = "") +
    ggtitle(title)
  
  return(pie)
}

LDL-ukb-d-30780_irnt, Liver

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

gwas_n <- samplesize[trait]

Comparing eQTL, sQTL and eQTL + sQTL

qtl <- "eonly"

file_param <- paste0(folder_results_multiqtl, trait, "/", trait, ".", qtl, ".thin", thin, ".", vgs, ".param.RDS")
param <- readRDS(file_param)
ctwas_parameters <- summarize_param(param, gwas_n)

p1 <- plot_piechart_topn(ctwas_parameters = ctwas_parameters,colors = colors,by = "type",title = NULL)

qtl <- "sonly"

file_param <- paste0(folder_results_multiqtl, trait, "/", trait, ".", qtl, ".thin", thin, ".", vgs, ".param.RDS")
param <- readRDS(file_param)
ctwas_parameters <- summarize_param(param, gwas_n)

p2 <- plot_piechart_topn(ctwas_parameters = ctwas_parameters,colors = colors,by = "type",title = NULL)

qtl <- "es"

file_param <- paste0(folder_results_multiqtl, trait, "/", trait, ".", qtl, ".thin", thin, ".", vgs, ".param.RDS")
param <- readRDS(file_param)
ctwas_parameters <- summarize_param(param, gwas_n)

p3 <- plot_piechart_topn(ctwas_parameters = ctwas_parameters,colors = colors,by = "type",title = NULL)


# Convert plots to gtables with fixed panel sizes
pie1 <- fix_panel_size(p1)
pie2 <- fix_panel_size(p2)
pie3 <- fix_panel_size(p3)

# Calculate widths of each gtable (plot + legend)
widths <- unit.c(grobWidth(pie1), grobWidth(pie2), grobWidth(pie3))

# Arrange plots with their natural widths
p <- grid.arrange(pie1, pie2, pie3, 
                  ncol = 3, 
                  widths = widths,
                  top = paste0(trait)
)

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

Version	Author	Date
3b4edbe	XSun	2025-04-28

Comparing eQTL and eQTL + stQTL + edQTL

qtl <- "eonly"

file_param <- paste0(folder_results_multiqtl, trait, "/", trait, ".", qtl, ".thin", thin, ".", vgs, ".param.RDS")
param <- readRDS(file_param)
ctwas_parameters <- summarize_param(param, gwas_n)

p1 <- plot_piechart_topn(ctwas_parameters = ctwas_parameters,colors = colors,by = "type",title = NULL)

qtl <- "ested"

file_param <- paste0(folder_results_multiqtl, trait, "/", trait, ".", qtl, ".thin", thin, ".", vgs, ".param.RDS")
param <- readRDS(file_param)
ctwas_parameters <- summarize_param(param, gwas_n)

p2 <- plot_piechart_topn(ctwas_parameters = ctwas_parameters,colors = colors,by = "type",title = NULL)


pie1 <- fix_panel_size(p1)
pie2 <- fix_panel_size(p2)

# Calculate widths of each gtable (plot + legend)
widths <- unit.c(grobWidth(pie1), grobWidth(pie2))

# Arrange plots with their natural widths
p <- grid.arrange(pie1, pie2,  
                  ncol = 2, 
                  widths = widths,
                  top = paste0(trait)
)

Past versions of unnamed-chunk-4-1.png

Version	Author	Date
3b4edbe	XSun	2025-04-28

Comparing stQTL, edQTL and eQTL + stQTL + edQTL

qtl <- "stonly"

file_param <- paste0(folder_results_multiqtl, trait, "/", trait, ".", qtl, ".thin", thin, ".", vgs, ".param.RDS")
param <- readRDS(file_param)
ctwas_parameters <- summarize_param(param, gwas_n)

p1 <- plot_piechart_topn(ctwas_parameters = ctwas_parameters,colors = colors,by = "type",title = NULL)

qtl <- "edonly"

file_param <- paste0(folder_results_multiqtl, trait, "/", trait, ".", qtl, ".thin", thin, ".", vgs, ".param.RDS")
param <- readRDS(file_param)
ctwas_parameters <- summarize_param(param, gwas_n)

p2 <- plot_piechart_topn(ctwas_parameters = ctwas_parameters,colors = colors,by = "type",title = NULL)

qtl <- "ested"

file_param <- paste0(folder_results_multiqtl, trait, "/", trait, ".", qtl, ".thin", thin, ".", vgs, ".param.RDS")
param <- readRDS(file_param)
ctwas_parameters <- summarize_param(param, gwas_n)

p3 <- plot_piechart_topn(ctwas_parameters = ctwas_parameters,colors = colors,by = "type",title = NULL)


# Convert plots to gtables with fixed panel sizes
pie1 <- fix_panel_size(p1)
pie2 <- fix_panel_size(p2)
pie3 <- fix_panel_size(p3)

# Calculate widths of each gtable (plot + legend)
widths <- unit.c(grobWidth(pie1), grobWidth(pie2), grobWidth(pie3))

# Arrange plots with their natural widths
p <- grid.arrange(pie1, pie2, pie3, 
                  ncol = 3, 
                  widths = widths,
                  top = paste0(trait)
)

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

Version	Author	Date
3b4edbe	XSun	2025-04-28

Comparing stQTL and stQTL + sQTL

qtl <- "stonly"

file_param <- paste0(folder_results_multiqtl, trait, "/", trait, ".", qtl, ".thin", thin, ".", vgs, ".param.RDS")
param <- readRDS(file_param)
ctwas_parameters <- summarize_param(param, gwas_n)

p1 <- plot_piechart_topn(ctwas_parameters = ctwas_parameters,colors = colors,by = "type",title = NULL)

qtl <- "sts"

file_param <- paste0(folder_results_multiqtl, trait, "/", trait, ".", qtl, ".thin", thin, ".", vgs, ".param.RDS")
param <- readRDS(file_param)
ctwas_parameters <- summarize_param(param, gwas_n)

p2 <- plot_piechart_topn(ctwas_parameters = ctwas_parameters,colors = colors,by = "type",title = NULL)


pie1 <- fix_panel_size(p1)
pie2 <- fix_panel_size(p2)

# Calculate widths of each gtable (plot + legend)
widths <- unit.c(grobWidth(pie1), grobWidth(pie2))

# Arrange plots with their natural widths
p <- grid.arrange(pie1, pie2,  
                  ncol = 2, 
                  widths = widths,
                  top = paste0(trait)
)

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

Version	Author	Date
3b4edbe	XSun	2025-04-28

IBD-ebi-a-GCST004131, Whole_Blood & Colon_Transverse

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

gwas_n <- samplesize[trait]

Comparing eQTL, sQTL and eQTL + sQTL

qtl <- "eonly"

file_param <- paste0(folder_results_multiqtl, trait, "/", trait, ".", qtl, ".thin", thin, ".", vgs, ".param.RDS")
param <- readRDS(file_param)
ctwas_parameters <- summarize_param(param, gwas_n)

p1 <- plot_piechart_topn(ctwas_parameters = ctwas_parameters,colors = colors,by = "type",title = NULL)

qtl <- "sonly"

file_param <- paste0(folder_results_multiqtl, trait, "/", trait, ".", qtl, ".thin", thin, ".", vgs, ".param.RDS")
param <- readRDS(file_param)
ctwas_parameters <- summarize_param(param, gwas_n)

p2 <- plot_piechart_topn(ctwas_parameters = ctwas_parameters,colors = colors,by = "type",title = NULL)

qtl <- "es"

file_param <- paste0(folder_results_multiqtl, trait, "/", trait, ".", qtl, ".thin", thin, ".", vgs, ".param.RDS")
param <- readRDS(file_param)
ctwas_parameters <- summarize_param(param, gwas_n)

p3 <- plot_piechart_topn(ctwas_parameters = ctwas_parameters,colors = colors,by = "type",title = NULL)


# Convert plots to gtables with fixed panel sizes
pie1 <- fix_panel_size(p1)
pie2 <- fix_panel_size(p2)
pie3 <- fix_panel_size(p3)

# Calculate widths of each gtable (plot + legend)
widths <- unit.c(grobWidth(pie1), grobWidth(pie2), grobWidth(pie3))

# Arrange plots with their natural widths
p <- grid.arrange(pie1, pie2, pie3, 
                  ncol = 3, 
                  widths = widths,
                  top = paste0(trait)
)

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

Version	Author	Date
3b4edbe	XSun	2025-04-28

Comparing eQTL and eQTL + stQTL + edQTL

qtl <- "eonly"

file_param <- paste0(folder_results_multiqtl, trait, "/", trait, ".", qtl, ".thin", thin, ".", vgs, ".param.RDS")
param <- readRDS(file_param)
ctwas_parameters <- summarize_param(param, gwas_n)

p1 <- plot_piechart_topn(ctwas_parameters = ctwas_parameters,colors = colors,by = "type",title = NULL)

qtl <- "ested"

file_param <- paste0(folder_results_multiqtl, trait, "/", trait, ".", qtl, ".thin", thin, ".", vgs, ".param.RDS")
param <- readRDS(file_param)
ctwas_parameters <- summarize_param(param, gwas_n)

p2 <- plot_piechart_topn(ctwas_parameters = ctwas_parameters,colors = colors,by = "type",title = NULL)


pie1 <- fix_panel_size(p1)
pie2 <- fix_panel_size(p2)

# Calculate widths of each gtable (plot + legend)
widths <- unit.c(grobWidth(pie1), grobWidth(pie2))

# Arrange plots with their natural widths
p <- grid.arrange(pie1, pie2,  
                  ncol = 2, 
                  widths = widths,
                  top = paste0(trait)
)

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

Version	Author	Date
3b4edbe	XSun	2025-04-28

Comparing stQTL, edQTL and eQTL + stQTL + edQTL

qtl <- "stonly"

file_param <- paste0(folder_results_multiqtl, trait, "/", trait, ".", qtl, ".thin", thin, ".", vgs, ".param.RDS")
param <- readRDS(file_param)
ctwas_parameters <- summarize_param(param, gwas_n)

p1 <- plot_piechart_topn(ctwas_parameters = ctwas_parameters,colors = colors,by = "type",title = NULL)

qtl <- "edonly"

file_param <- paste0(folder_results_multiqtl, trait, "/", trait, ".", qtl, ".thin", thin, ".", vgs, ".param.RDS")
param <- readRDS(file_param)
ctwas_parameters <- summarize_param(param, gwas_n)

p2 <- plot_piechart_topn(ctwas_parameters = ctwas_parameters,colors = colors,by = "type",title = NULL)

qtl <- "ested"

file_param <- paste0(folder_results_multiqtl, trait, "/", trait, ".", qtl, ".thin", thin, ".", vgs, ".param.RDS")
param <- readRDS(file_param)
ctwas_parameters <- summarize_param(param, gwas_n)

p3 <- plot_piechart_topn(ctwas_parameters = ctwas_parameters,colors = colors,by = "type",title = NULL)


# Convert plots to gtables with fixed panel sizes
pie1 <- fix_panel_size(p1)
pie2 <- fix_panel_size(p2)
pie3 <- fix_panel_size(p3)

# Calculate widths of each gtable (plot + legend)
widths <- unit.c(grobWidth(pie1), grobWidth(pie2), grobWidth(pie3))

# Arrange plots with their natural widths
p <- grid.arrange(pie1, pie2, pie3, 
                  ncol = 3, 
                  widths = widths,
                  top = paste0(trait)
)

Past versions of unnamed-chunk-10-1.png

Version	Author	Date
3b4edbe	XSun	2025-04-28

Comparing stQTL and stQTL + sQTL

qtl <- "stonly"

file_param <- paste0(folder_results_multiqtl, trait, "/", trait, ".", qtl, ".thin", thin, ".", vgs, ".param.RDS")
param <- readRDS(file_param)
ctwas_parameters <- summarize_param(param, gwas_n)

p1 <- plot_piechart_topn(ctwas_parameters = ctwas_parameters,colors = colors,by = "type",title = NULL)

qtl <- "sts"

file_param <- paste0(folder_results_multiqtl, trait, "/", trait, ".", qtl, ".thin", thin, ".", vgs, ".param.RDS")
param <- readRDS(file_param)
ctwas_parameters <- summarize_param(param, gwas_n)

p2 <- plot_piechart_topn(ctwas_parameters = ctwas_parameters,colors = colors,by = "type",title = NULL)


pie1 <- fix_panel_size(p1)
pie2 <- fix_panel_size(p2)

# Calculate widths of each gtable (plot + legend)
widths <- unit.c(grobWidth(pie1), grobWidth(pie2))

# Arrange plots with their natural widths
p <- grid.arrange(pie1, pie2,  
                  ncol = 2, 
                  widths = widths,
                  top = paste0(trait)
)

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

Version	Author	Date
3b4edbe	XSun	2025-04-28

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

other attached packages:
[1] egg_0.4.5        gridExtra_2.3    ggrepel_0.9.1    ggplot2_3.5.1   
[5] dplyr_1.1.4      ctwas_0.5.4.9000

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] GenomicRanges_1.48.0        base64enc_0.1-3            
  [9] fs_1.5.2                    rstudioapi_0.13            
 [11] farver_2.1.0                bit64_4.0.5                
 [13] AnnotationDbi_1.58.0        fansi_1.0.3                
 [15] xml2_1.3.3                  codetools_0.2-18           
 [17] logging_0.10-108            cachem_1.0.6               
 [19] knitr_1.39                  jsonlite_1.8.0             
 [21] workflowr_1.7.0             Rsamtools_2.12.0           
 [23] dbplyr_2.1.1                png_0.1-7                  
 [25] readr_2.1.2                 compiler_4.2.0             
 [27] httr_1.4.3                  assertthat_0.2.1           
 [29] Matrix_1.5-3                fastmap_1.1.0              
 [31] lazyeval_0.2.2              cli_3.6.1                  
 [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                 Biobase_2.56.0             
 [43] jquerylib_0.1.4             vctrs_0.6.5                
 [45] Biostrings_2.64.0           rtracklayer_1.56.0         
 [47] xfun_0.41                   stringr_1.5.1              
 [49] irlba_2.3.5                 lifecycle_1.0.4            
 [51] restfulr_0.0.14             ensembldb_2.20.2           
 [53] XML_3.99-0.14               zlibbioc_1.42.0            
 [55] zoo_1.8-10                  scales_1.3.0               
 [57] gggrid_0.2-0                hms_1.1.1                  
 [59] promises_1.2.0.1            MatrixGenerics_1.8.0       
 [61] ProtGenerics_1.28.0         parallel_4.2.0             
 [63] SummarizedExperiment_1.26.1 AnnotationFilter_1.20.0    
 [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                   S4Vectors_0.34.0           
 [75] BiocIO_1.6.0                GenomicFeatures_1.48.3     
 [77] BiocGenerics_0.42.0         filelock_1.0.2             
 [79] BiocParallel_1.30.3         repr_1.1.4                 
 [81] GenomeInfoDb_1.39.9         rlang_1.1.2                
 [83] pkgconfig_2.0.3             matrixStats_0.62.0         
 [85] bitops_1.0-7                evaluate_0.15              
 [87] lattice_0.20-45             purrr_1.0.2                
 [89] labeling_0.4.2              GenomicAlignments_1.32.0   
 [91] htmlwidgets_1.5.4           cowplot_1.1.1              
 [93] bit_4.0.4                   tidyselect_1.2.0           
 [95] magrittr_2.0.3              AMR_2.1.1                  
 [97] R6_2.5.1                    IRanges_2.30.0             
 [99] generics_0.1.2              DelayedArray_0.22.0        
[101] DBI_1.2.2                   withr_2.5.0                
[103] pgenlibr_0.3.3              pillar_1.9.0               
[105] whisker_0.4                 mixsqp_0.3-43              
[107] KEGGREST_1.36.3             RCurl_1.98-1.7             
[109] tibble_3.2.1                crayon_1.5.1               
[111] utf8_1.2.2                  BiocFileCache_2.4.0        
[113] plotly_4.10.0               tzdb_0.4.0                 
[115] rmarkdown_2.25              progress_1.2.2             
[117] data.table_1.14.2           blob_1.2.3                 
[119] git2r_0.30.1                digest_0.6.29              
[121] tidyr_1.3.0                 httpuv_1.6.5               
[123] stats4_4.2.0                munsell_0.5.0              
[125] viridisLite_0.4.0           skimr_2.1.4                
[127] bslib_0.3.1