Summary for multi-group analysis, e + s + st QTLs
XSun
2025-06-03
Last updated: 2025-06-05
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Knit directory: multigroup_ctwas_analysis/
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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,
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,".3qtls.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$group_pve)
param_single <- readRDS(paste0(folder_results_single, trait, "/", trait, "_", top_tissue, ".thin1.shared_all.param.RDS"))
ctwas_parameters_single <- summarize_param(param_single, gwas_n)
title <- paste0(trait, ", top tissue: ", top_tissue)
grid.newpage()
print(generate_piecharts_for_trait(title_top = title))
## Overlap
PIP_cutoff <- 0.8
combined_pip_by_group_single <- readRDS(paste0(folder_results_single, trait, "/", trait, "_", top_tissue, ".combined_pip_bygroup_final.RDS"))
combined_pip_by_group_multi <- readRDS(paste0(folder_results_multi,trait,"/",trait,".3qtls.combined_pip_bygroup_final.RDS"))
grid.newpage()
print(plot_overlap_barplot(combined_pip_by_group_multi = combined_pip_by_group_multi, combined_pip_by_group_single = combined_pip_by_group_single,PIP_cutoff = PIP_cutoff,tissue = top_tissue,trait = trait,return_unique_genes = T))
## heatmaps
combined_pip_by_group_multi_sig <- combined_pip_by_group_multi[combined_pip_by_group_multi$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.157]$plot
$single_genes_unique
[1] "PARP9" "DDX56" "MZF1" "CLDN23" "FUT2" "VIL1" "KLHDC7A"
[8] "WBP1L" 
[1] "Unique genes from multigroup: LDLR, PCSK9, CETP, CCNJ, PKN3, ABCA8, FCGRT, DNAJC13, LRCH4, HMGCR, ASGR1, APOB, ADH1B, FLT3, ZDHHC18, USP39, TIMD4, ZFYVE1, ACP6, NPC1L1, ERGIC3, MITF, PSRC1, SNX17, GABBR1, HMGN1, KIF13B, R3HDM2, MYPOP, SIPA1, USP3, PGS1, FAM117B, ADRB1, PHC1, WASHC4, TPD52, THOP1, ZFP28, DMTN, PDE4C, XPNPEP3"
IBD-ebi-a-GCST004131
TableGrob (2 x 3) "arrange": 4 grobs
z cells name grob
1 1 (2-2,1-1) arrange gtable[layout]
2 2 (2-2,2-2) arrange gtable[layout]
3 3 (2-2,3-3) arrange gtable[layout]
4 4 (1-1,1-3) arrange text[GRID.text.372]$plot
$single_genes_unique
character(0)
[1] "Unique genes from multigroup: HLA-DQB1, BRD7, PTPN2, FOSL2, ERI3, CD244, ADAM15, TNFRSF6B, IP6K2, FCGR2A, SBNO2, GPR35, SMAD3, MAST2, ACBD3, STAT3, CASC3, RORC, RGS14, ATG16L1, AUH"
aFib-ebi-a-GCST006414
TableGrob (2 x 3) "arrange": 4 grobs
z cells name grob
1 1 (2-2,1-1) arrange gtable[layout]
2 2 (2-2,2-2) arrange gtable[layout]
3 3 (2-2,3-3) arrange gtable[layout]
4 4 (1-1,1-3) arrange text[GRID.text.578]$plot
$single_genes_unique
[1] "DLEU1" "DEK" "MTSS1" "C5orf47"
[1] "Unique genes from multigroup: PRRX1, PCM1, PLEC, GMCL1, SCMH1, NACA, RBMS1, NCOR2, KCNH2, DNAJC12, RBM20, LRRC10, RUFY3, MYO18B, SSPN, PALMD, CAMK2D, XPO7, CCNB1IP1, CCT2, MICAL3, FLNC, AHSA2, CFL2, C21orf2, VPS13A, DDX17, SF3B1"
SBP-ukb-a-360
TableGrob (2 x 3) "arrange": 4 grobs
z cells name grob
1 1 (2-2,1-1) arrange gtable[layout]
2 2 (2-2,2-2) arrange gtable[layout]
3 3 (2-2,3-3) arrange gtable[layout]
4 4 (1-1,1-3) arrange text[GRID.text.779]$plot
$single_genes_unique
[1] "ZNF467" "HFE" "SHISA8" "SHBG" "PLK2" 
[1] "Unique genes from multigroup: BAG6, PKN2, PKP4, ENPEP, FES, RERE, NUDT5, RAB34, SLC9A3R2, TMBIM1, HOXA11, DMWD, UVSSA, TMEM175, MTMR9, ADH1B, SP140L, PPP3R1, NAA60, CCDC163, FHOD3, SGSM3, NPR1, PAQR5, CLCN6, THAP3, CAMK1D, TBX2, FBXO38, REXO1"
T1D-GCST90014023
TableGrob (2 x 3) "arrange": 4 grobs
z cells name grob
1 1 (2-2,1-1) arrange gtable[layout]
2 2 (2-2,2-2) arrange gtable[layout]
3 3 (2-2,3-3) arrange gtable[layout]
4 4 (1-1,1-3) arrange text[GRID.text.983]$plot
$single_genes_unique
[1] "ELMO3"
[1] "Unique genes from multigroup: C1QTNF6, CAMK4, PLEKHM1, CCDC88B, PGM1, ZMYND8, SLC11A1, PRCC, BATF3, RASGRP1, VSIR"
T2D-panukb
TableGrob (2 x 3) "arrange": 4 grobs
z cells name grob
1 1 (2-2,1-1) arrange gtable[layout]
2 2 (2-2,2-2) arrange gtable[layout]
3 3 (2-2,3-3) arrange gtable[layout]
4 4 (1-1,1-3) arrange text[GRID.text.1192]$plot
$single_genes_unique
[1] "CDKN1C" "JAZF1" 
[1] "Unique genes from multigroup: RREB1, PRKRIP1, ENHO, HLA-DRB5, AP3S2, CEP68, EMC1, CAMK1D"
ATH_gtexukb
TableGrob (2 x 3) "arrange": 4 grobs
z cells name grob
1 1 (2-2,1-1) arrange gtable[layout]
2 2 (2-2,2-2) arrange gtable[layout]
3 3 (2-2,3-3) arrange gtable[layout]
4 4 (1-1,1-3) arrange text[GRID.text.1402]$plot
$single_genes_unique
[1] "ZDHHC24" "CEP95" 
[1] "Unique genes from multigroup: GSDMB, IL21R, RNF219, NPNT, TRAPPC2L, AHI1, MRVI1, IL4R, SERPINB7, DCAF1, RAD50, SEPT8, ELP2, INPP5B"
BMI-panukb
TableGrob (2 x 3) "arrange": 4 grobs
z cells name grob
1 1 (2-2,1-1) arrange gtable[layout]
2 2 (2-2,2-2) arrange gtable[layout]
3 3 (2-2,3-3) arrange gtable[layout]
4 4 (1-1,1-3) arrange text[GRID.text.1608]$plot
$single_genes_unique
[1] "PSORS1C1" "MAPK11" "OSBPL3" "ENHO" "C1QTNF4" "FAM231B" "DLG4" 
[1] "Unique genes from multigroup: MFSD13A, NEGR1, AGAP3, PDIA2, PTOV1, NASP, ADGRB2, GALNT4, ENTPD6, CTBP2, NPY5R, REXO1, RNF187, EEF1A2, PRPF6, FLT3, SNRNP70, CDHR3, ACP7, EI24, TP53, ADH1B, GPR61, PRMT2, MEST, KHSRP, ECE2, KCNC4, ANAPC4, DENND1A, RSPO3, PIK3C3, SLIT1, RALY, EPHA4, PATJ, ATP13A2, R3HDM1, PPM1A"
HB-panukb
TableGrob (2 x 3) "arrange": 4 grobs
z cells name grob
1 1 (2-2,1-1) arrange gtable[layout]
2 2 (2-2,2-2) arrange gtable[layout]
3 3 (2-2,3-3) arrange gtable[layout]
4 4 (1-1,1-3) arrange text[GRID.text.1817]$plot
$single_genes_unique
[1] "MKRN2OS" "ENTPD6" "KIFC2" "PPP5C" "CCND2" "SH3GL1" "PLA2G6" 
[1] "Unique genes from multigroup: FCGRT, FCGR2A, CD36, ACVRL1, EMC10, FAM35A, MKRN2, CAT, PARP6, MAST2, ABHD12, ZMIZ2, REEP3, NOSIP, RGS14, ZNF589, LTBP4, SRSF4, CYP21A2, GMPR, RAB34, FEM1B, TBK1, TMEM176A, RAB8A, DAZAP1, TNPO1, FAM193B, SUMF1, ANKRD9, SLC9A3R2, ASPSCR1, PSMB9, TTC13, HGFAC, TNK2, TOR1B, HLF, GSTM4, TNFAIP8L3, CSF1, TMPRSS6, NT5DC1, EXOC3L2, PPP1R36, LIPA, ZNF384, PAFAH1B3, FBF1, POLI, C11orf84, SLC16A9, APOL3, HOXA7, BET1L, HDDC2, GPRC5A, NOTCH1, HBS1L, ZBTB38, UGT8, OPLAH, CCDC92, PC, BAHCC1, RIPK3, CDK7, ACVR1B, EFHC1, VEGFB, NECAP2, PFKM, NNT, SMG9"
Height-panukb
TableGrob (2 x 3) "arrange": 4 grobs
z cells name grob
1 1 (2-2,1-1) arrange gtable[layout]
2 2 (2-2,2-2) arrange gtable[layout]
3 3 (2-2,3-3) arrange gtable[layout]
4 4 (1-1,1-3) arrange text[GRID.text.2019]$plot
$single_genes_unique
[1] "RGP1" "ACHE" "SRSF12" "DVL3" "SPHK2"
[6] "PSMB9" "TOPORS-AS1" "UTP11" "MYLK4" 
[1] "Unique genes from multigroup: CD79B, PTH1R, HOMEZ, GNA12, TRIM41, DCAKD, HTR1F, FBLN5, RINT1, CDO1, RAB34, STAT6, CEP192, ACP1, BCS1L, SUPT3H, GALNT5, BRICD5, DBNL, TMEM8B, FHOD3, SCMH1, P2RX4, CBX2, PLAG1, UVSSA, GGPS1, TLE1, LRRC29, TRIM6, CNIH4, SSBP4, PIGC, LMF1, C2orf40, NOS3, IL11RA, GLT8D2, ASPH, CNDP2, HECTD1, SMAD3, KIAA1614, NUP37, MLF2, GDPD3, SPIN1, PPP2R5C, ABCC8, UBE2Q1, SF3A2, ODF2L, USP47, PCSK5, ZCCHC3, ANKS3, DNAJC5G, CRELD1, DKK3, SLC9A1, BAMBI, SLC22A3, CDK11A, ZNF565, ELN, HMGN1, ZC3H13, YAP1, ACTR1B, SRCAP, HYOU1, FBXW8, RFT1, EDEM3, SLC25A30, ITPK1, ZZEF1, ZBTB38, SPSB1, MAP2K2, RCN1, FAM114A1, AKR1C2, FAN1, MCTP2, ZNF438, DCUN1D5, SMCHD1, MYPN, ABHD15, TNS2, MYO7A, CHD1L, EXOSC9, PITRM1, SEL1L, FGFR3, TTLL6, ZNF680, MSRB3, RALGAPA2, FAM134B, MXD4, CKB, DAAM2, PARP12, TPRG1L, CCND3, ZNF484, RBCK1, TRPS1, ARHGAP24, ARL15, EIF3C, ARSJ, GSDMC, NCR3LG1, ZMIZ2, SLC25A32, SEC23IP, ZBP1, FUBP3, KCNJ12, RDH10, VAMP5, LUC7L2, ATP10D, RPS6KA4, PAPD4, RREB1, SAMD4A, CTU2, VMAC, SLC48A1, CCDC57, RPS9, MORC3, SOCS1, FN3KRP, UST, EMC9, SERPINH1, MPHOSPH6, MMAB, PSMF1, HNRNPC, ATP1B3, GATAD2A, ASPSCR1, SERPINF1, CREB3L2"
HTN-panukb
TableGrob (2 x 3) "arrange": 4 grobs
z cells name grob
1 1 (2-2,1-1) arrange gtable[layout]
2 2 (2-2,2-2) arrange gtable[layout]
3 3 (2-2,3-3) arrange gtable[layout]
4 4 (1-1,1-3) arrange text[GRID.text.2227]$plot
$single_genes_unique
[1] "TAPBP"
[1] "Unique genes from multigroup: FES, PRRT1, EFS, RIN1, TMEM133, SEPT9, FBXO10, ADH1B, FAM212A, CLCN6, CEP170, TCF21, LRRC10B, PIP4K2B, ATP10A, CEP68, GUCY1A3, COLGALT2, CTSF, SLCO3A1, ITGB5, NFRKB, TYMS, CLN8, NAA38, OAF, NMT1, SHISA4, SFXN4, MRAS"
PLT-panukb
TableGrob (2 x 3) "arrange": 4 grobs
z cells name grob
1 1 (2-2,1-1) arrange gtable[layout]
2 2 (2-2,2-2) arrange gtable[layout]
3 3 (2-2,3-3) arrange gtable[layout]
4 4 (1-1,1-3) arrange text[GRID.text.2425]$plot
$single_genes_unique
[1] "WBP2" "TRABD" "HPR" "HNRNPUL1" "JADE2" 
[1] "Unique genes from multigroup: CYB5A, FBXO34, CD151, THEMIS2, CRELD2, ARL17B, ARHGEF12, CCDC97, ANPEP, RTEL1, CBL, DFFB, SIRT5, RAB8A, SATB1, ZMIZ2, FARP2, ACVR1C, SEMA3F, TRDMT1, PLCG2, SEPT10, GSTP1, TFR2, FAM120B, LRCH4, MFN2, KIAA1109, ZNF385A, CAND2, REPS1, RAC2, ARIH2, CCDC38, PCIF1, AAMDC, SLC43A3, PRR5L, TRAM2, ALLC, PPP2R5C, ERN1, MAMDC2, DNTTIP2, MORC2, ARHGAP15, ARHGAP45, DLEU1, MFN1, PROSER2, PHF7, IL27, TNPO1, AHR, MS4A7, KMT5C, ARPC2, ZC3HC1, COL11A2, ZNF318, MAPKAP1, ZDHHC18, YWHAZ, C2CD2, HSD17B13, CDCA7L, POR, CFLAR, SYTL1, PDLIM2, SH3D21, TCTEX1D2"
RA-panukb
TableGrob (2 x 3) "arrange": 4 grobs
z cells name grob
1 1 (2-2,1-1) arrange gtable[layout]
2 2 (2-2,2-2) arrange gtable[layout]
3 3 (2-2,3-3) arrange gtable[layout]
4 4 (1-1,1-3) arrange text[GRID.text.2616]$plot
$single_genes_unique
character(0)
[1] "Unique genes from multigroup: KIFC1, ASIC3, ITPR3, COL6A3"
RBC-panukb
TableGrob (2 x 3) "arrange": 4 grobs
z cells name grob
1 1 (2-2,1-1) arrange gtable[layout]
2 2 (2-2,2-2) arrange gtable[layout]
3 3 (2-2,3-3) arrange gtable[layout]
4 4 (1-1,1-3) arrange text[GRID.text.2822]$plot
$single_genes_unique
[1] "HLA-E" "LRRC37A" "BST1" "ESRRB" "JMJD1C" "PPP1R26" "H1F0"
[8] "PTPA" "MAPK3" "ZNF629" 
[1] "Unique genes from multigroup: HIST1H2AC, CD36, UBE2Q2, FCGRT, PARP6, CNIH4, LRCH4, INTS14, EHBP1L1, NOSIP, ABHD12, TFRC, NFE2L1, OSER1, TP53, RTEL1, SRSF4, RGS14, FES, ACSM5, ZMIZ2, MAP2K2, CAT, ROCK1, TAL1, HOXD11, XPC, ADH1B, FHL3, FEM1B, PPP1R21, EDN1, SPPL2A, RAB34, FADS1, POLR2H, CCDC92, PIK3R3, TLE3, PSMB5, FAM200B, TNFRSF10B, ANKRD36C, ELOVL3, PRR5L, RYBP, TST, KANSL1, PCGF3, PARN, TYMS, VLDLR, DAP3, ADSL, TAF8, MN1, PFKM, GPATCH2L, ZFP36L2, SLC9A3R2, ITSN1, TFR2, BAHCC1, ZBTB7A, TRPS1, EXOC3L2, NUDT2, WDR41, PTEN, NPRL3, SLX4IP, ZNF106, ABCC1, MARCH2, DOK1, TMC4, GLCE, RB1, VRK2, HLF, DAZAP1, ZBTB38, PBRM1, CD22, TTC13, CCND3, EMP2, SRPRB, ACTR2, C6orf52, GOSR1, CRNN, RIPK3, SLC25A32, XAF1, ZNF236, METRNL, CFLAR, COL4A2, UFC1, UGT8"
WBC-ieu-b-30
TableGrob (2 x 3) "arrange": 4 grobs
z cells name grob
1 1 (2-2,1-1) arrange gtable[layout]
2 2 (2-2,2-2) arrange gtable[layout]
3 3 (2-2,3-3) arrange gtable[layout]
4 4 (1-1,1-3) arrange text[GRID.text.3040]$plot
$single_genes_unique
[1] "LSP1" "FCER1G" "ACAP1" "APOBR" "RNF181" "FOXJ2" "GPN2" "KCNN4"
[9] "TIMM50" "BAK1" "TPST1" "JMJD6" "ABI3" "MARK2" "PSMD2" 
[1] "Unique genes from multigroup: CSF3R, HLA-C, MED24, GSDMB, REST, S1PR2, CCDC125, SLX4IP, NOSTRIN, CNN2, RAB2A, ITSN2, PTPRA, CLEC4M, MFSD13A, RAB34, MICALL2, HSF2, GSDMD, BAX, FAM120B, LYZ, MARCH7, PPP5C, CSF1, HDHD5, CXCL6, KIAA1614, SPAAR, DPP4, ACVR1B, ARHGEF25, OPTN, RBPMS, RHBDD3, TSPAN32, MYL5, SLC25A24, BEND7, ZNF30, CD33, MYH10, HSPA4, CABLES1, MAP3K5, MIGA2, TMC4, MYO1G, CERS4, WWOX, LSM4, SLC41A1, SAE1, GPR157, CEP83, SEC31A, ARHGAP45, RPN1, COTL1, BCL2L2, PDLIM2, RAPGEF3, S1PR1, EMILIN3, KIAA1755, SIGLEC14, CD36, PSD4, INPP5D, MICAL3, ADAM32, PKD1, EFHC1, BICD2, MBNL1, IGDCC4, NINJ2, IL17RA, PARP12, CD200R1, MKRN2, AP1M1, CTSC, FLT3LG, RBM38, FN1, CACNA1H, POMT1, CARS2, LIMS2, ATP11A, TP53, ATL2, SEC22A, ZNF320, PLCG2, CD300A, FYCO1, TSPAN14, ABCC5, SUSD1, CSGALNACT1, ANKRD44, SMAP1, ZNF713, MAP3K10, CTDSPL, CD226, CAPZB, LRBA, PRR16, UPP1, SBNO2, IP6K2, SCD, ZC3H12D, TRAP1, ZFP1, IL16, CEPT1, DDX58, TET2, CCDC82, DLG5, VPS16, RNF139, GBAS, PAPSS1, ACKR2, VSIR, DTNB, ITGB2, CREB3L3, ARHGAP31, GOLGA5, SNX32, AKAP11, GADD45G, ZC3HC1, RAB5C, EIF4E2, TBC1D10C, TBX2, NPAS2, IRF5, WDR86, IFNAR1, SLC45A4, ZNF461, FBXO38, WDFY2, ANKRD11, DEFB1, HOXC6, NOC3L, CXCL12, RAP1GAP2, UGDH, FIGNL1, GIPC1, B4GALT3, NUDT14, SLC39A9, MAP3K7CL, LPP, RRP12, HNRNPK, NCF2, ACKR3, SPATA6L, STXBP3, TM7SF3, IFIH1, MANBA, ELMO1, OAS1, HSD17B13, FAM45A"
SCZ-ieu-b-5102
TableGrob (2 x 3) "arrange": 4 grobs
z cells name grob
1 1 (2-2,1-1) arrange gtable[layout]
2 2 (2-2,2-2) arrange gtable[layout]
3 3 (2-2,3-3) arrange gtable[layout]
4 4 (1-1,1-3) arrange text[GRID.text.3236]$plot
$single_genes_unique
[1] "HLA-DMB" "KLHL20" "DRD2" "LY6H" "TRMT2A" "MLF2" "C11orf80"
[8] "PUF60" 
[1] "Unique genes from multigroup: SOHLH1, MRPS33, R3HDM2, LPCAT4, ACE, TRPV4, C4B"
ASD-ieu-a-1185
TableGrob (2 x 3) "arrange": 4 grobs
z cells name grob
1 1 (2-2,1-1) arrange gtable[layout]
2 2 (2-2,2-2) arrange gtable[layout]
3 3 (2-2,3-3) arrange gtable[layout]
4 4 (1-1,1-3) arrange text[GRID.text.3430]$plot
$single_genes_unique
character(0)[1] "Unique genes from multigroup: "
BIP-ieu-b-5110
TableGrob (2 x 3) "arrange": 4 grobs
z cells name grob
1 1 (2-2,1-1) arrange gtable[layout]
2 2 (2-2,2-2) arrange gtable[layout]
3 3 (2-2,3-3) arrange gtable[layout]
4 4 (1-1,1-3) arrange text[GRID.text.3615]$plot
$single_genes_unique
[1] "ZDHHC2"
[1] "Unique genes from multigroup: BLOC1S2, EFL1, KIAA1109, GHITM, HTR6, PTDSS1, CNNM4"
MDD-ieu-b-102
TableGrob (2 x 3) "arrange": 4 grobs
z cells name grob
1 1 (2-2,1-1) arrange gtable[layout]
2 2 (2-2,2-2) arrange gtable[layout]
3 3 (2-2,3-3) arrange gtable[layout]
4 4 (1-1,1-3) arrange text[GRID.text.3807]$plot
$single_genes_unique
character(0)[1] "Unique genes from multigroup: "
PD-ieu-b-7
TableGrob (2 x 3) "arrange": 4 grobs
z cells name grob
1 1 (2-2,1-1) arrange gtable[layout]
2 2 (2-2,2-2) arrange gtable[layout]
3 3 (2-2,3-3) arrange gtable[layout]
4 4 (1-1,1-3) arrange text[GRID.text.3991]$plot
$single_genes_unique
character(0)
[1] "Unique genes from multigroup: CDHR3, ARSA"
ADHD-ieu-a-1183
TableGrob (2 x 3) "arrange": 4 grobs
z cells name grob
1 1 (2-2,1-1) arrange gtable[layout]
2 2 (2-2,2-2) arrange gtable[layout]
3 3 (2-2,3-3) arrange gtable[layout]
4 4 (1-1,1-3) arrange text[GRID.text.4179]$plot
$single_genes_unique
character(0)[1] "Unique genes from multigroup: "
NS-ukb-a-230
| Version | Author | Date |
|---|---|---|
| 612a153 | XSun | 2025-06-04 |
TableGrob (2 x 3) "arrange": 4 grobs
z cells name grob
1 1 (2-2,1-1) arrange gtable[layout]
2 2 (2-2,2-2) arrange gtable[layout]
3 3 (2-2,3-3) arrange gtable[layout]
4 4 (1-1,1-3) arrange text[GRID.text.4350]$plot
| Version | Author | Date |
|---|---|---|
| 612a153 | XSun | 2025-06-04 |
$single_genes_unique
[1] "THRA"
| Version | Author | Date |
|---|---|---|
| 612a153 | XSun | 2025-06-04 |
[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_0.5.21
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 whisker_0.4
[107] mixsqp_0.3-43 KEGGREST_1.36.3
[109] RCurl_1.98-1.7 tibble_3.2.1
[111] crayon_1.5.1 utf8_1.2.2
[113] BiocFileCache_2.4.0 plotly_4.10.0
[115] tzdb_0.4.0 rmarkdown_2.25
[117] progress_1.2.2 data.table_1.14.2
[119] blob_1.2.3 git2r_0.30.1
[121] digest_0.6.29 tidyr_1.3.0
[123] httpuv_1.6.5 stats4_4.2.0
[125] munsell_0.5.0 viridisLite_0.4.0
[127] skimr_2.1.4 bslib_0.3.1