Last updated: 2025-05-26

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Introduction

This is a summary for tissues selected here: https://sq-96.github.io/multigroup_ctwas_analysis/realdata_final_tissueselection_mingene0_splicing_exclude_brainprocessed.html

library(ctwas)

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

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

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


folder_results_single <- "/project/xinhe/xsun/multi_group_ctwas/22.singlegroup_0515/ctwas_output/expression/"
folder_results_multi <- "/project/xinhe/xsun/multi_group_ctwas/23.multi_group_0515/snakemake_outputs/"


generate_piecharts_for_trait <- function(title_top = NULL, colors = NULL) {

  if(is.null(colors)) {
    colors <- c("#ff7f0e", "#2ca02c", "#d62728",  "#9467bd", "#8c564b", "#e377c2", "#7f7f7f",  "#bcbd22",  "#17becf",  "#f7b6d2",  "#c5b0d5",  "#9edae5", "#ffbb78",  "#98df8a",  "#ff9896" )
  }
  
  pie_eqtl_single <- plot_piechart_single(ctwas_parameters_single, colors, by = "type", title = NULL)
  
  pie_eqtl_multi_type <- plot_piechart_topn(ctwas_parameters_multi, colors, by = "type", title = NULL)
  pie_eqtl_multi_context <- plot_piechart_topn(ctwas_parameters_multi, colors, by = "context", title = NULL, n_tissue = 10)

  # Function to fix panel size
  fix_panel_size <- function(plot, width = 2.1, height = 2) {
    set_panel_size(plot, width = unit(width, "in"), height = unit(height, "in"))
  }

  # Apply fixed panel size
  pie1 <- fix_panel_size(pie_eqtl_single)
  pie2 <- fix_panel_size(pie_eqtl_multi_type)
  pie3 <- fix_panel_size(pie_eqtl_multi_context)

  # Compute natural widths
  widths <- unit.c(grobWidth(pie1), grobWidth(pie2), grobWidth(pie3))

  # Arrange
  p <- grid.arrange(pie1, pie2, pie3,
                    ncol = 3,
                    widths = widths,
                    top = title_top)

  return(p)
}

# plot_overlap_barplot <- function(combined_pip_by_group_multi,
#                                  combined_pip_by_group_single,
#                                  PIP_cutoff = 0.8, tissue,
#                                  trait) {
#   # Filter genes by PIP cutoff
#   combined_pip_by_group_sig_multi <- combined_pip_by_group_multi[combined_pip_by_group_multi$combined_pip > PIP_cutoff, ]
#   combined_pip_by_group_sig_single <- combined_pip_by_group_single[combined_pip_by_group_single$combined_pip > PIP_cutoff, ]
#   
#   # Extract gene names
#   multi_genes <- combined_pip_by_group_sig_multi$gene_name
#   single_genes <- combined_pip_by_group_sig_single$gene_name
#   
#   # Compute overlap
#   overlap_genes <- intersect(multi_genes, single_genes)
#   single_genes_unique <- setdiff(single_genes,overlap_genes)
#   n_overlap <- length(overlap_genes)
#   n_multi <- length(multi_genes)
#   n_single <- length(single_genes)
#   
#   # Construct data frame for plotting
#   df <- data.frame(
#     group = rep(c("Multi-group", paste0("Single-eQTL - \n", tissue)), each = 2),
#     part = rep(c("Overlap", "Unique"), 2),
#     count = c(n_overlap, n_multi - n_overlap, n_overlap, n_single - n_overlap)
#   )
#   
#   # Ensure proper stacking order
#   df$part <- factor(df$part, levels = c("Unique", "Overlap"))
#   
#   # Plot
#   p <- ggplot(df, aes(x = group, y = count, fill = part)) +
#     geom_bar(stat = "identity", width = 0.6) +
#     geom_text(aes(label = count), position = position_stack(vjust = 0.5), color = "white", size = 5) +
#     scale_fill_manual(values = c("Overlap" = "#1f77b4", "Unique" = "#ff7f0e")) +
#     labs(
#       x = "",
#       y = paste0("Number of Genes at PIP > ", PIP_cutoff),
#       title = trait,
#       fill = ""
#     ) +
#     theme_minimal(base_size = 14)
#   
#   return(p,single_genes_unique)
# }

plot_overlap_barplot <- function(combined_pip_by_group_multi,
                                 combined_pip_by_group_single,
                                 PIP_cutoff = 0.8,
                                 tissue,
                                 trait,
                                 return_unique_genes = FALSE) {
  # Filter genes by PIP cutoff
  combined_pip_by_group_sig_multi <- combined_pip_by_group_multi[combined_pip_by_group_multi$combined_pip > PIP_cutoff, ]
  combined_pip_by_group_sig_single <- combined_pip_by_group_single[combined_pip_by_group_single$combined_pip > PIP_cutoff, ]
  
  # Extract gene names
  multi_genes <- combined_pip_by_group_sig_multi$gene_name
  single_genes <- combined_pip_by_group_sig_single$gene_name
  
  # Compute overlap
  overlap_genes <- intersect(multi_genes, single_genes)
  single_genes_unique <- setdiff(single_genes, overlap_genes)
  n_overlap <- length(overlap_genes)
  n_multi <- length(multi_genes)
  n_single <- length(single_genes)
  
  # Construct data frame for plotting
  df <- data.frame(
    group = rep(c("Multi-group", paste0("Single-eQTL - \n", tissue)), each = 2),
    part = rep(c("Overlap", "Unique"), 2),
    count = c(n_overlap, n_multi - n_overlap, n_overlap, n_single - n_overlap)
  )
  
  # Ensure proper stacking order
  df$part <- factor(df$part, levels = c("Unique", "Overlap"))
  
  # Plot
  p <- ggplot(df, aes(x = group, y = count, fill = part)) +
    geom_bar(stat = "identity", width = 0.6) +
    geom_text(aes(label = count), position = position_stack(vjust = 0.5), color = "white", size = 5) +
    scale_fill_manual(values = c("Overlap" = "#1f77b4", "Unique" = "#ff7f0e")) +
    labs(
      x = "",
      y = paste0("Number of Genes at PIP > ", PIP_cutoff),
      title = trait,
      fill = ""
    ) +
    theme_minimal(base_size = 14)
  
  if (return_unique_genes) {
    return(list(plot = p, single_genes_unique = single_genes_unique))
  } else {
    return(p)
  }
}


get_top_tissue <- function(group_pve) {
  # Remove QTL type to extract tissue names
  group_pve <- group_pve[-which(names(group_pve) =="SNP")]
  
  tissue_names <- sub("\\|.*", "", names(group_pve))
  
  # Sum PVE values across tissues
  tissue_pve <- tapply(group_pve, tissue_names, sum)
  
  # Return the tissue name with the highest total PVE
  top_tissue <- names(which.max(tissue_pve))
  return(top_tissue)
}
for (trait in traits){
  
  cat("\n")
  cat(trait)
  cat("\n")
  
  ## parameters
  gwas_n <- samplesize[trait]
  
  param_multi <- readRDS(paste0(folder_results_multi,trait,"/",trait,".4qtls.thin1.shared_all.param.RDS"))
  ctwas_parameters_multi <- summarize_param(param = param_multi,gwas_n = gwas_n)
  
  top_tissue <- get_top_tissue(ctwas_parameters_multi$group_pve)
  
  param_single <- readRDS(paste0(folder_results_single, trait, "/", trait, "_", top_tissue, ".thin1.shared_all.param.RDS"))
  ctwas_parameters_single <- summarize_param(param_single, gwas_n)
  
  title <- paste0(trait, ", top tissue: ", top_tissue)
  grid.newpage()
  print(generate_piecharts_for_trait(title_top = title))
  
  ## Overlap 
  
  PIP_cutoff <- 0.8
  
  combined_pip_by_group_single <- readRDS(paste0(folder_results_single, trait, "/", trait, "_", top_tissue, ".combined_pip_bygroup_final.RDS"))
  combined_pip_by_group_multi <- readRDS(paste0(folder_results_multi,trait,"/",trait,".4qtls.combined_pip_bygroup_final.RDS"))
  
  grid.newpage()
  print(plot_overlap_barplot(combined_pip_by_group_multi = combined_pip_by_group_multi, combined_pip_by_group_single = combined_pip_by_group_single,PIP_cutoff = PIP_cutoff,tissue = top_tissue,trait = trait,return_unique_genes = T))
  
  ## heatmaps
  
  combined_pip_by_group_multi_sig <- combined_pip_by_group_multi[combined_pip_by_group_multi$combined_pip > PIP_cutoff,]
  combined_pip_by_group_single_sig <- combined_pip_by_group_single[combined_pip_by_group_single$combined_pip > PIP_cutoff,]
  
  combined_pip_by_group_multi_unique <- combined_pip_by_group_multi_sig[!combined_pip_by_group_multi_sig$gene_name %in% combined_pip_by_group_single_sig$gene_name, ]
  
  grid.newpage()
  if(nrow(combined_pip_by_group_multi_unique) > 0) {
    print(plot_heatmap(heatmap_data = combined_pip_by_group_multi_unique, main = paste0("New genes identified by multigroup analysis, PIP>",PIP_cutoff),showPIP = T))
  }
  
  print(paste0("Unique genes from multigroup: ", paste0(combined_pip_by_group_multi_unique$gene_name, collapse = ", ")))
  

}

LDL-ukb-d-30780_irnt

TableGrob (2 x 3) "arrange": 4 grobs
  z     cells    name                grob
1 1 (2-2,1-1) arrange      gtable[layout]
2 2 (2-2,2-2) arrange      gtable[layout]
3 3 (2-2,3-3) arrange      gtable[layout]
4 4 (1-1,1-3) arrange text[GRID.text.161]
$plot


$single_genes_unique
[1] "PARP9"   "DDX56"   "ASAP3"   "MZF1"    "CLDN23"  "FUT2"    "VIL1"   
[8] "KLHDC7A" "WBP1L"  

[1] "Unique genes from multigroup: LDLR, PCSK9, CETP, CCNJ, PKN3, APOC1, ABCA8, FCGRT, DNAJC13, LRCH4, HMGCR, ASGR1, APOB, FLT3, ADH1B, ZDHHC18, USP39, TIMD4, ZFYVE1, NPC1L1, ERGIC3, ACP6, MITF, PSRC1, SNX17, GABBR1, KIF13B, R3HDM2, SIPA1, FAM117B, PGS1, MYPOP, USP3, ADRB1, WASHC4, PHC1, TPD52, THOP1, ZFP28, PDE4C, XPNPEP3, DMTN, SEPT2"

IBD-ebi-a-GCST004131

TableGrob (2 x 3) "arrange": 4 grobs
  z     cells    name                grob
1 1 (2-2,1-1) arrange      gtable[layout]
2 2 (2-2,2-2) arrange      gtable[layout]
3 3 (2-2,3-3) arrange      gtable[layout]
4 4 (1-1,1-3) arrange text[GRID.text.381]
$plot


$single_genes_unique
character(0)

[1] "Unique genes from multigroup: HLA-DQB1, BRD7, SBNO2, PTPN2, FOSL2, ERI3, CD244, TNFRSF6B, ADAM15, PAX8, FCGR2A, GPR35, SMAD3, AMZ1, MAST2, ACBD3, NDUFS1, STAT3, CASC3, RGS14, RORC, TCF3, ERAP2, ATG16L1, AUH"

aFib-ebi-a-GCST006414

TableGrob (2 x 3) "arrange": 4 grobs
  z     cells    name                grob
1 1 (2-2,1-1) arrange      gtable[layout]
2 2 (2-2,2-2) arrange      gtable[layout]
3 3 (2-2,3-3) arrange      gtable[layout]
4 4 (1-1,1-3) arrange text[GRID.text.592]
$plot


$single_genes_unique
[1] "DLEU1"   "DEK"     "MTSS1"   "C5orf47"

[1] "Unique genes from multigroup: RAB1A, PRRX1, PCM1, PLEC, PALMD, GMCL1, SCMH1, NACA, RBMS1, NCOR2, KCNH2, DNAJC12, LRRC10, PHLDB2, RUFY3, RBM20, MYO18B, SSPN, XPO7, CAMK2D, CCNB1IP1, CCT2, MICAL3, FLNC, AHSA2, CFL2, C21orf2, VPS13A, DDX17"

SBP-ukb-a-360

TableGrob (2 x 3) "arrange": 4 grobs
  z     cells    name                grob
1 1 (2-2,1-1) arrange      gtable[layout]
2 2 (2-2,2-2) arrange      gtable[layout]
3 3 (2-2,3-3) arrange      gtable[layout]
4 4 (1-1,1-3) arrange text[GRID.text.798]
$plot


$single_genes_unique
[1] "ZNF467" "HFE"    "SHISA8" "SHBG"   "PLK2"  

[1] "Unique genes from multigroup: BAG6, PKN2, PKP4, ENPEP, FES, RERE, NUDT5, RAB34, SLC9A3R2, TMBIM1, HOXA11, DMWD, TMEM175, MTMR9, ADH1B, PPP3R1, SP140L, NAA60, PECAM1, FHOD3, SGSM3, NPR1, PAQR5, CLCN6, THAP3, CAMK1D, TBX2, FBXO38, SWAP70, REXO1, EIF2AK4, CCDC163"

T1D-GCST90014023

TableGrob (2 x 3) "arrange": 4 grobs
  z     cells    name                 grob
1 1 (2-2,1-1) arrange       gtable[layout]
2 2 (2-2,2-2) arrange       gtable[layout]
3 3 (2-2,3-3) arrange       gtable[layout]
4 4 (1-1,1-3) arrange text[GRID.text.1007]
$plot


$single_genes_unique
[1] "ELMO3"

[1] "Unique genes from multigroup: C1QTNF6, CAMK4, PLEKHM1, EED, TAPBP, CCDC88B, PGM1, ZMYND8, CTSB, SLC11A1, PRCC, RASGRP1, BATF3"

T2D-panukb

TableGrob (2 x 3) "arrange": 4 grobs
  z     cells    name                 grob
1 1 (2-2,1-1) arrange       gtable[layout]
2 2 (2-2,2-2) arrange       gtable[layout]
3 3 (2-2,3-3) arrange       gtable[layout]
4 4 (1-1,1-3) arrange text[GRID.text.1221]
$plot


$single_genes_unique
[1] "CDKN1C" "JAZF1" 

[1] "Unique genes from multigroup: RREB1, PRKRIP1, ENHO, HLA-DRB5, CEP68, EMC1, CAMK1D"

ATH_gtexukb

TableGrob (2 x 3) "arrange": 4 grobs
  z     cells    name                 grob
1 1 (2-2,1-1) arrange       gtable[layout]
2 2 (2-2,2-2) arrange       gtable[layout]
3 3 (2-2,3-3) arrange       gtable[layout]
4 4 (1-1,1-3) arrange text[GRID.text.1436]
$plot


$single_genes_unique
[1] "ZDHHC24" "CEP95"  

[1] "Unique genes from multigroup: GSDMB, IL21R, RNF219, NPNT, TRAPPC2L, AHI1, MRVI1, IL4R, SERPINB7, DCAF1, RAD50, INPP5B, SEPT8, ELP2"

BMI-panukb

TableGrob (2 x 3) "arrange": 4 grobs
  z     cells    name                 grob
1 1 (2-2,1-1) arrange       gtable[layout]
2 2 (2-2,2-2) arrange       gtable[layout]
3 3 (2-2,3-3) arrange       gtable[layout]
4 4 (1-1,1-3) arrange text[GRID.text.1647]
$plot


$single_genes_unique
[1] "PSORS1C1" "MAPK11"   "ENHO"     "C1QTNF4"  "FAM231B"  "DLG4"    

[1] "Unique genes from multigroup: MFSD13A, AGAP3, NEGR1, PDIA2, PTOV1, NASP, ADGRB2, GALNT4, ENTPD6, CTBP2, NPY5R, REXO1, RNF187, EEF1A2, PRPF6, SNRNP70, FLT3, CDHR3, EI24, ACP7, TP53, GPR61, ADH1B, PRMT2, MEST, KHSRP, ECE2, KCNC4, DENND1A, ANAPC4, RSPO3, PIK3C3, SLIT1, RALY, PATJ, EPHA4, ATP13A2, DICER1, R3HDM1, PPM1A"

HB-panukb

TableGrob (2 x 3) "arrange": 4 grobs
  z     cells    name                 grob
1 1 (2-2,1-1) arrange       gtable[layout]
2 2 (2-2,2-2) arrange       gtable[layout]
3 3 (2-2,3-3) arrange       gtable[layout]
4 4 (1-1,1-3) arrange text[GRID.text.1861]
$plot


$single_genes_unique
[1] "MKRN2OS" "ENTPD6"  "KIFC2"   "PPP5C"   "CCND2"   "SH3GL1"  "PLA2G6" 

[1] "Unique genes from multigroup: FCGRT, CD36, ACVRL1, EMC10, FAM35A, FCGR2A, MKRN2, CAT, PARP6, ABHD12, MAST2, REEP3, ZMIZ2, RGS14, NOSIP, LTBP4, SRSF4, ZNF589, GMPR, CYP21A2, RAB34, FEM1B, TBK1, RAB8A, MYO1C, TMEM176A, DAZAP1, TNPO1, FAM193B, H6PD, ANKRD9, SUMF1, SLC9A3R2, ASPSCR1, EMP2, TTC13, PSMB9, ATP5S, HLF, TNFAIP8L3, HGFAC, GSTM4, TNK2, TOR1B, EEF1A1, TMPRSS6, CSF1, EXOC3L2, NT5DC1, LIPA, ZNF384, PPP1R36, POLI, PAFAH1B3, C11orf84, FBF1, SLC16A9, NOTCH1, HOXA7, APOL3, GPRC5A, HDDC2, BET1L, HBS1L, BAHCC1, UGT8, OPLAH, ZBTB38, NNT, ANKRD10, CCDC92, PC, RIPK3, CDK7, EFHC1, ACVR1B, NECAP2, PFKM, VEGFB, WDR41, MISP3, SMG9"

Height-panukb

TableGrob (2 x 3) "arrange": 4 grobs
  z     cells    name                 grob
1 1 (2-2,1-1) arrange       gtable[layout]
2 2 (2-2,2-2) arrange       gtable[layout]
3 3 (2-2,3-3) arrange       gtable[layout]
4 4 (1-1,1-3) arrange text[GRID.text.2068]
$plot


$single_genes_unique
[1] "RGP1"       "ACHE"       "SRSF12"     "DVL3"       "SPHK2"     
[6] "PSMB9"      "TOPORS-AS1" "UTP11"      "MYLK4"     

[1] "Unique genes from multigroup: CD79B, PTH1R, HOMEZ, TRIM41, GNA12, DCAKD, HTR1F, FBLN5, RINT1, CDO1, STAT6, RAB34, CEP192, ACP1, BCS1L, SUPT3H, BRICD5, GALNT5, DBNL, FHOD3, TMEM8B, SCMH1, P2RX4, CBX2, CHCHD10, GGPS1, UVSSA, PLAG1, TLE1, LRRC29, TRIM6, CNIH4, SSBP4, LMF1, PIGC, C2orf40, IL11RA, NOS3, GLT8D2, ASPH, CNDP2, HECTD1, SMAD3, NUP37, KIAA1614, GDPD3, UBE2Q1, SPIN1, PPP2R5C, MLF2, ABCC8, SF3A2, ODF2L, PCSK5, ANKS3, USP47, CRELD1, ZCCHC3, SLC9A1, DKK3, DNAJC5G, SLC22A3, ELN, BAMBI, CDK11A, HMGN1, ZC3H13, ZNF565, YAP1, SRCAP, ACTR1B, RFT1, HYOU1, FBXW8, ITPK1, EDEM3, ZZEF1, ZBTB38, MAP2K2, FAM114A1, RCN1, FAN1, SPSB1, ZNF438, AKR1C2, MYPN, DCUN1D5, TNS2, SMCHD1, MYO7A, CHD1L, MCTP2, ABHD15, PITRM1, SEL1L, FGFR3, MSRB3, EXOSC9, SLC25A30, MXD4, TTLL6, RALGAPA2, PARP12, TPRG1L, FAM134B, DAAM2, CCND3, ZNF484, SZT2, CKB, RBCK1, EIF3C, TRPS1, ARSJ, ARL15, GSDMC, ARHGAP24, ZMIZ2, SLC25A32, NCR3LG1, SEC23IP, ZBP1, VAMP5, FUBP3, RDH10, RPS6KA4, LUC7L2, ATP10D, PAPD4, SLC48A1, KCNJ12, RREB1, SAMD4A, CTU2, CCDC57, RPS9, SOCS1, UST, SSR3, FN3KRP, MMAB, VMAC, PSMF1, HNRNPC, MORC3, MPHOSPH6, SERPINH1, ATP1B3, GATAD2A, ASPSCR1, CREB3L2"

HTN-panukb

TableGrob (2 x 3) "arrange": 4 grobs
  z     cells    name                 grob
1 1 (2-2,1-1) arrange       gtable[layout]
2 2 (2-2,2-2) arrange       gtable[layout]
3 3 (2-2,3-3) arrange       gtable[layout]
4 4 (1-1,1-3) arrange text[GRID.text.2281]
$plot


$single_genes_unique
[1] "TAPBP"

[1] "Unique genes from multigroup: FES, PRRT1, EFS, RIN1, TMEM133, SEPT9, FBXO10, ADH1B, FAM212A, PECAM1, CLCN6, CEP170, TCF21, LRRC10B, PIP4K2B, ATP10A, CEP68, GUCY1A3, COLGALT2, SNRNP70, CTSF, SLCO3A1, ITGB5, NFRKB, TYMS, CLN8, NAA38, SWAP70, SHISA4, OAF, NMT1, SFXN4, MRAS"

PLT-panukb

TableGrob (2 x 3) "arrange": 4 grobs
  z     cells    name                 grob
1 1 (2-2,1-1) arrange       gtable[layout]
2 2 (2-2,2-2) arrange       gtable[layout]
3 3 (2-2,3-3) arrange       gtable[layout]
4 4 (1-1,1-3) arrange text[GRID.text.2484]
$plot


$single_genes_unique
[1] "WBP2"     "TRABD"    "HPR"      "HNRNPUL1" "JADE2"   

[1] "Unique genes from multigroup: CYB5A, FBXO34, CD151, THEMIS2, CRELD2, ARL17B, ARHGEF12, CCDC97, ANPEP, RTEL1, CBL, DFFB, SIRT5, RAB8A, SATB1, FARP2, ZMIZ2, ACVR1C, SEMA3F, TRDMT1, PLCG2, SEPT10, GSTP1, TFR2, FAM120B, LRCH4, MFN2, KIAA1109, ZNF385A, CAND2, REPS1, RAC2, ARIH2, CCDC38, PCIF1, AAMDC, SLC43A3, PRR5L, TRAM2, ALLC, PPP2R5C, ERN1, MAMDC2, DNTTIP2, MORC2, ARHGAP15, ARHGAP45, DLEU1, MFN1, PROSER2, PHF7, IL27, TNPO1, AHR, MS4A7, KMT5C, ARPC2, ZC3HC1, COL11A2, ZNF318, MAPKAP1, ZDHHC18, YWHAZ, C2CD2, HSD17B13, CDCA7L, POR, CFLAR, SYTL1, PDLIM2, SH3D21, TCTEX1D2"

RA-panukb

TableGrob (2 x 3) "arrange": 4 grobs
  z     cells    name                 grob
1 1 (2-2,1-1) arrange       gtable[layout]
2 2 (2-2,2-2) arrange       gtable[layout]
3 3 (2-2,3-3) arrange       gtable[layout]
4 4 (1-1,1-3) arrange text[GRID.text.2680]
$plot


$single_genes_unique
character(0)

[1] "Unique genes from multigroup: KIFC1, ASIC3, ITPR3, COL6A3"

RBC-panukb

TableGrob (2 x 3) "arrange": 4 grobs
  z     cells    name                 grob
1 1 (2-2,1-1) arrange       gtable[layout]
2 2 (2-2,2-2) arrange       gtable[layout]
3 3 (2-2,3-3) arrange       gtable[layout]
4 4 (1-1,1-3) arrange text[GRID.text.2891]
$plot


$single_genes_unique
 [1] "HLA-E"   "LRRC37A" "BST1"    "ESRRB"   "JMJD1C"  "PPP1R26" "H1F0"   
 [8] "PTPA"    "MAPK3"   "ZNF629" 

[1] "Unique genes from multigroup: HIST1H2AC, CD36, UBE2Q2, FCGRT, PARP6, CNIH4, LRCH4, EHBP1L1, INTS14, NOSIP, TFRC, ABHD12, NFE2L1, OSER1, EMP2, TP53, RTEL1, SRSF4, RGS14, FES, ACSM5, MAP2K2, ZMIZ2, CAT, ROCK1, XPC, TAL1, HOXD11, ADH1B, FHL3, FEM1B, PPP1R21, SPPL2A, RAB34, EDN1, PIK3R3, POLR2H, FADS1, CCDC92, FAM200B, PSMB5, TNFRSF10B, MYO1C, ELOVL3, ANKRD36C, PRR5L, TLE3, PIK3R1, RYBP, CFLAR, TYMS, PCGF3, PSD4, VLDLR, TST, TAF8, ADSL, MN1, GPATCH2L, RP11-437B10.1, PFKM, SLC9A3R2, ZFP36L2, BAHCC1, TFR2, ITSN1, EXOC3L2, WDR41, ZBTB7A, NUDT2, SLX4IP, NPRL3, TRPS1, PTEN, ZNF106, PQLC2, VRK2, MARCH2, TMC4, GLCE, DOK1, PICALM, RB1, HLF, CCND3, ANKRD10, ZBTB38, TTC13, PBRM1, SRPRB, CRLS1, DAZAP1, GOSR1, C6orf52, CD22, ACTR2, XAF1, RIPK3, ZNF236, SLC25A32, CRNN, DUS3L, UGT8, KANSL1"

WBC-ieu-b-30

TableGrob (2 x 3) "arrange": 4 grobs
  z     cells    name                 grob
1 1 (2-2,1-1) arrange       gtable[layout]
2 2 (2-2,2-2) arrange       gtable[layout]
3 3 (2-2,3-3) arrange       gtable[layout]
4 4 (1-1,1-3) arrange text[GRID.text.3114]
$plot


$single_genes_unique
 [1] "FCER1G" "ACAP1"  "APOBR"  "HPN"    "RNF181" "FOXJ2"  "GPN2"   "TIMM50"
 [9] "TPST1"  "JMJD6"  "ABI3"   "MARK2"  "PSMD2" 

[1] "Unique genes from multigroup: HLA-C, MED24, CSF3R, GSDMB, REST, S1PR2, CCDC125, SLX4IP, CNN2, NOSTRIN, RAB2A, ITSN2, LYZ, PTPRA, CLEC4M, RAB34, MFSD13A, PECAM1, MICALL2, SLC41A1, HSF2, CASP8, BAX, GSDMD, FAM120B, ARRB2, MARCH7, CSF1, HDHD5, PPP5C, CHSY1, CXCL6, ACVR1B, DPP4, SPAAR, KIAA1614, ARPC2, OPTN, ARHGEF25, C11orf21, RBPMS, KIF1B, MYL5, ZNF30, MYH10, CABLES1, BEND7, HSPA4, RHBDD3, MAP3K5, SLC25A24, SAE1, ING5, TMC4, CERS4, CD33, WWOX, LSM4, MIGA2, EEF1A1, CEP83, MYO1G, GPR157, RPN1, BCL2L2, SLC23A3, COTL1, SEC31A, ARHGAP45, RAPGEF3, EMILIN3, RAB29, TAGLN, IL17RA, KIAA1755, SIGLEC14, TSPAN32, PDLIM2, S1PR1, IGDCC4, MICAL3, PKD1, CD36, INPP5D, PGS1, MBNL1, PSD4, CD200R1, NINJ2, BICD2, AP1M1, EFHC1, PARP12, ZNF320, ADAM32, CTSC, POMT1, NAP1L4, SEC22A, TP53, DSTN, FN1, MKRN2, ABCC5, RBM38, SCPEP1, ATL2, PLCG2, LIMS2, TSPAN14, ANKRD44, ATP11A, CARS2, PRR16, FLT3LG, IRF5, MAP3K10, CACNA1H, CTDSPL, UPP1, MAPK14, CD300A, CSGALNACT1, SCD, ZNF713, FYCO1, CAPZB, VSIR, ZC3H12D, WDR86, MYO1C, LRBA, CD226, ZFP1, CEPT1, EIF4E2, SUSD1, TET2, VPS16, AMZ1, RNF139, ITGB2, PAPSS1, IL16, AKAP11, B4GALT3, CREB3L3, AMPD1, TBC1D10C, CCDC82, HEATR3, DLG5, ARHGAP31, SBNO2, SNX32, GOLGA5, CD46, TRAP1, GADD45G, TBX2, IFNAR1, ACKR2, DTNB, ZNF461, DEFB1, HOXC6, NPAS2, NUDT14, ZC3HC1, SLC45A4, RAP1GAP2, FIGNL1, SLC39A9, FBXO38, TRAF3IP3, TM7SF3, NOC3L, TBL2, UGDH, MAP3K7CL, ELMO1"

SCZ-ieu-b-5102

TableGrob (2 x 3) "arrange": 4 grobs
  z     cells    name                 grob
1 1 (2-2,1-1) arrange       gtable[layout]
2 2 (2-2,2-2) arrange       gtable[layout]
3 3 (2-2,3-3) arrange       gtable[layout]
4 4 (1-1,1-3) arrange text[GRID.text.3315]
$plot


$single_genes_unique
 [1] "SLC38A3"  "HLA-DMB"  "TMEM222"  "KLHL20"   "DRD2"     "LY6H"    
 [7] "TRMT2A"   "MLF2"     "C11orf80" "PUF60"   

[1] "Unique genes from multigroup: MAP3K12, SOHLH1, MRPS33, R3HDM2, LPCAT4, C4B, ACE, TRPV4"

ASD-ieu-a-1185

TableGrob (2 x 3) "arrange": 4 grobs
  z     cells    name                 grob
1 1 (2-2,1-1) arrange       gtable[layout]
2 2 (2-2,2-2) arrange       gtable[layout]
3 3 (2-2,3-3) arrange       gtable[layout]
4 4 (1-1,1-3) arrange text[GRID.text.3514]
$plot


$single_genes_unique
character(0)
[1] "Unique genes from multigroup: "

BIP-ieu-b-5110

TableGrob (2 x 3) "arrange": 4 grobs
  z     cells    name                 grob
1 1 (2-2,1-1) arrange       gtable[layout]
2 2 (2-2,2-2) arrange       gtable[layout]
3 3 (2-2,3-3) arrange       gtable[layout]
4 4 (1-1,1-3) arrange text[GRID.text.3704]
$plot


$single_genes_unique
[1] "ZDHHC2"

[1] "Unique genes from multigroup: BLOC1S2, EFL1, KIAA1109, PTDSS1, HTR6, GHITM, CNNM4"

MDD-ieu-b-102

TableGrob (2 x 3) "arrange": 4 grobs
  z     cells    name                 grob
1 1 (2-2,1-1) arrange       gtable[layout]
2 2 (2-2,2-2) arrange       gtable[layout]
3 3 (2-2,3-3) arrange       gtable[layout]
4 4 (1-1,1-3) arrange text[GRID.text.3901]
$plot


$single_genes_unique
character(0)

[1] "Unique genes from multigroup: MAP3K12, MED24"

PD-ieu-b-7

TableGrob (2 x 3) "arrange": 4 grobs
  z     cells    name                 grob
1 1 (2-2,1-1) arrange       gtable[layout]
2 2 (2-2,2-2) arrange       gtable[layout]
3 3 (2-2,3-3) arrange       gtable[layout]
4 4 (1-1,1-3) arrange text[GRID.text.4104]
$plot


$single_genes_unique
character(0)

[1] "Unique genes from multigroup: CDHR3, CTSB, ARSA"

ADHD-ieu-a-1183

TableGrob (2 x 3) "arrange": 4 grobs
  z     cells    name                 grob
1 1 (2-2,1-1) arrange       gtable[layout]
2 2 (2-2,2-2) arrange       gtable[layout]
3 3 (2-2,3-3) arrange       gtable[layout]
4 4 (1-1,1-3) arrange text[GRID.text.4297]
$plot


$single_genes_unique
character(0)
[1] "Unique genes from multigroup: "

NS-ukb-a-230

TableGrob (2 x 3) "arrange": 4 grobs
  z     cells    name                 grob
1 1 (2-2,1-1) arrange       gtable[layout]
2 2 (2-2,2-2) arrange       gtable[layout]
3 3 (2-2,3-3) arrange       gtable[layout]
4 4 (1-1,1-3) arrange text[GRID.text.4473]
$plot


$single_genes_unique
[1] "THRA"

[1] "Unique genes from multigroup: C12orf49"

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   dplyr_1.1.4    
[5] ggplot2_3.5.1   pheatmap_1.0.12 ctwas_0.5.19   

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 RColorBrewer_1.1-3         
 [65] AnnotationFilter_1.20.0     LDlinkR_1.2.3              
 [67] yaml_2.3.5                  curl_4.3.2                 
 [69] memoise_2.0.1               sass_0.4.1                 
 [71] biomaRt_2.54.1              stringi_1.7.6              
 [73] RSQLite_2.3.1               highr_0.9                  
 [75] S4Vectors_0.34.0            BiocIO_1.6.0               
 [77] GenomicFeatures_1.48.3      BiocGenerics_0.42.0        
 [79] filelock_1.0.2              BiocParallel_1.30.3        
 [81] repr_1.1.4                  GenomeInfoDb_1.39.9        
 [83] rlang_1.1.2                 pkgconfig_2.0.3            
 [85] matrixStats_0.62.0          bitops_1.0-7               
 [87] evaluate_0.15               lattice_0.20-45            
 [89] purrr_1.0.2                 labeling_0.4.2             
 [91] GenomicAlignments_1.32.0    htmlwidgets_1.5.4          
 [93] cowplot_1.1.1               bit_4.0.4                  
 [95] tidyselect_1.2.0            magrittr_2.0.3             
 [97] AMR_2.1.1                   R6_2.5.1                   
 [99] IRanges_2.30.0              generics_0.1.2             
[101] DelayedArray_0.22.0         DBI_1.2.2                  
[103] withr_2.5.0                 pgenlibr_0.3.3             
[105] pillar_1.9.0                KEGGREST_1.36.3            
[107] RCurl_1.98-1.7              mixsqp_0.3-43              
[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