parameters_apa_li
XSun
2025-04-09
Last updated: 2025-04-17
Checks: 6 1
Knit directory: multigroup_ctwas_analysis/
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Introduction
We estimated the parameters for the e+s+apa model in this analysis. The apa component follows the approach described in this study https://www.nature.com/articles/s41588-021-00864-5. For each gene, we used the lead QTL to construct a PredictDB model.
library(ctwas)
library(ggplot2)
library(tidyverse)
source("/project/xinhe/xsun/multi_group_ctwas/data/samplesize.R")
source("/project/xinhe/xsun/multi_group_ctwas/functions/0.functions.R")
folder_results_susieST <- "/project/xinhe/xsun/multi_group_ctwas/16.apa_li_weights/snakemake_outputs/"
folder_results_apaonly <- "/project/xinhe/xsun/multi_group_ctwas/16.apa_li_weights/snakemake_outputs_apaonly/"
folder_results_single <- "/project/xinhe/xsun/multi_group_ctwas/16.apa_li_weights/ctwas_output/apa/"
# 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)
colors <- c("#ff7f0e", "#2ca02c", "#d62728", "#9467bd", "#8c564b", "#e377c2", "#7f7f7f", "#bcbd22", "#17becf", "#f7b6d2", "#c5b0d5", "#9edae5", "#ffbb78", "#98df8a", "#ff9896" )
top_tissues <- c("Liver","Whole_Blood","Brain_Cerebellar_Hemisphere","Adipose_Subcutaneous","Brain_Cerebellum","Heart_Atrial_Appendage","Pituitary")
traits <- c("LDL-ukb-d-30780_irnt","IBD-ebi-a-GCST004131","BMI-panukb","RBC-panukb","SCZ-ieu-b-5102","aFib-ebi-a-GCST006414","T2D-panukb")
names(top_tissues) <- traits
plot_piechart <- function(ctwas_parameters, colors, by, title) {
# Create the initial data frame
data <- data.frame(
category = names(ctwas_parameters$prop_heritability),
percentage = ctwas_parameters$prop_heritability
)
# Split the category into context and type
data <- data %>%
mutate(
context = sub("\\|.*", "", category),
type = sub(".*\\|", "", category)
)
# Aggregate the data based on the 'by' parameter
if (by == "type") {
data <- data %>%
group_by(type) %>%
summarize(percentage = sum(percentage)) %>%
mutate(category = type) # Use type as the new category
} else if (by == "context") {
data <- data %>%
group_by(context) %>%
summarize(percentage = sum(percentage)) %>%
mutate(category = context) # Use context as the new category
} else {
stop("Invalid 'by' parameter. Use 'type' or 'context'.")
}
# Calculate percentage labels for the chart
data$percentage_label <- paste0(round(data$percentage * 100, 1), "%")
# Create the pie chart
pie <- ggplot(data, aes(x = "", y = percentage, fill = category)) +
geom_bar(stat = "identity", width = 1) +
coord_polar("y", start = 0) +
theme_void() + # Remove background and axes
geom_text(aes(label = percentage_label),
position = position_stack(vjust = 0.5), size = 3) + # Adjust size as needed
scale_fill_manual(values = colors) + # Custom colors
labs(fill = "Category") + # Legend title
ggtitle(title) # Title
return(pie)
}
plot_multi <- function(p1,p2,p3,title=NULL) {
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(p1)
pie2 <- fix_panel_size(p2)
pie3 <- fix_panel_size(p3)
# 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)
return(p)
}apaQTL summary
cis_files <- list.files(path = "/project2/xinhe/shared_data/multigroup_ctwas/weights/apa_li/",pattern = "cis.3aQTL.txt")
sum <- c()
for (file in cis_files){
tissue <- gsub(pattern = ".cis.3aQTL.txt",replacement = "",x = file)
cisdf <- data.table::fread(paste0("/project2/xinhe/shared_data/multigroup_ctwas/weights/apa_li/",file))
cisdf$fdr <- p.adjust(as.numeric(cisdf$p.value), method = "fdr")
cisdf_fdr005 <- cisdf[cisdf$fdr < 0.05,]
count_df <- cisdf_fdr005[, .N, by = transcript]
avg <- sum(count_df$N)/nrow(count_df)
tmp <- c(tissue,avg,nrow(count_df))
sum <- rbind(sum,tmp)
}
rownames(sum) <- NULL
colnames(sum) <- c("Tissue","avg_qtl_fdr005","num_gene")
DT::datatable(sum,caption = htmltools::tags$caption( style = 'caption-side: left; text-align: left; color:black; font-size:150% ;','Average number of apaQTL per gene'),options = list(pageLength = 10) )LDL-ukb-d-30780_irnt
trait <- "LDL-ukb-d-30780_irnt"
st <- "with_susieST"
gwas_n <- samplesize[trait]Setting: shared_all, thin = 1
thin <- 1
var_struc <- "shared_all"param_susieST <- readRDS(paste0(folder_results_susieST,"/",trait,"/",trait,".",st,".thin",thin,".",var_struc,".param.RDS"))
ctwas_parameters_susieST <- summarize_param(param_susieST, gwas_n)
total_nonSNPpve_susieST <- 1- ctwas_parameters_susieST$prop_heritability["SNP"]
pve_pie_by_type_susieST <- plot_piechart_topn(ctwas_parameters = ctwas_parameters_susieST, colors = colors, by = "type", title = "top-apa, multi-group")
param_apaonly <- readRDS(paste0(folder_results_apaonly,"/",trait,"/",trait,".",st,".thin",thin,".",var_struc,".param.RDS"))
ctwas_parameters_apaonly <- summarize_param(param_apaonly,gwas_n)
total_nonSNPpve_apaonly <- 1- ctwas_parameters_apaonly$prop_heritability["SNP"]
pve_pie_by_type_apaonly <- plot_piechart_topn(ctwas_parameters = ctwas_parameters_apaonly, colors = colors, by = "type", title = "top-apa, multi tissue apa")
top_tissue <- top_tissues[trait]
file_param_single <- paste0(folder_results_single, trait, "/", trait, "_", top_tissue, ".thin", thin, ".", var_struc, ".param.RDS")
param_single <- readRDS(file_param_single)
ctwas_parameters_single <- summarize_param(param_single, gwas_n)
total_nonSNPpve_single <- 1- ctwas_parameters_single$prop_heritability["SNP"]
pve_pie_by_type_single <- plot_piechart_topn(ctwas_parameters = ctwas_parameters_single, colors = colors, by = "context", title = "top-apa, single tissue apa")
plot_multi(pve_pie_by_type_single,pve_pie_by_type_apaonly,pve_pie_by_type_susieST, title=trait)
| Version | Author | Date |
|---|---|---|
| bda6e43 | XSun | 2025-04-09 |
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.136]IBD-ebi-a-GCST004131
trait <- "IBD-ebi-a-GCST004131"
st <- "with_susieST"
gwas_n <- samplesize[trait]Setting: shared_all, thin = 1
thin <- 1
var_struc <- "shared_all"param_susieST <- readRDS(paste0(folder_results_susieST,"/",trait,"/",trait,".",st,".thin",thin,".",var_struc,".param.RDS"))
ctwas_parameters_susieST <- summarize_param(param_susieST, gwas_n)
total_nonSNPpve_susieST <- 1- ctwas_parameters_susieST$prop_heritability["SNP"]
pve_pie_by_type_susieST <- plot_piechart_topn(ctwas_parameters = ctwas_parameters_susieST, colors = colors, by = "type", title = "top-apa, multi-group")
param_apaonly <- readRDS(paste0(folder_results_apaonly,"/",trait,"/",trait,".",st,".thin",thin,".",var_struc,".param.RDS"))
ctwas_parameters_apaonly <- summarize_param(param_apaonly,gwas_n)
total_nonSNPpve_apaonly <- 1- ctwas_parameters_apaonly$prop_heritability["SNP"]
pve_pie_by_type_apaonly <- plot_piechart_topn(ctwas_parameters = ctwas_parameters_apaonly, colors = colors, by = "type", title = "top-apa, multi tissue apa")
top_tissue <- top_tissues[trait]
file_param_single <- paste0(folder_results_single, trait, "/", trait, "_", top_tissue, ".thin", thin, ".", var_struc, ".param.RDS")
param_single <- readRDS(file_param_single)
ctwas_parameters_single <- summarize_param(param_single, gwas_n)
total_nonSNPpve_single <- 1- ctwas_parameters_single$prop_heritability["SNP"]
pve_pie_by_type_single <- plot_piechart_topn(ctwas_parameters = ctwas_parameters_single, colors = colors, by = "context", title = "top-apa, single tissue apa")
plot_multi(pve_pie_by_type_single,pve_pie_by_type_apaonly,pve_pie_by_type_susieST, title=trait)
TableGrob (2 x 3) "arrange": 4 grobs
z cells name grob
1 1 (2-2,1-1) arrange gtable[layout]
2 2 (2-2,2-2) arrange gtable[layout]
3 3 (2-2,3-3) arrange gtable[layout]
4 4 (1-1,1-3) arrange text[GRID.text.275]T2D-panukb
trait <- "T2D-panukb"
st <- "with_susieST"
gwas_n <- samplesize[trait]Setting: shared_all, thin = 1
thin <- 1
var_struc <- "shared_all"param_susieST <- readRDS(paste0(folder_results_susieST,"/",trait,"/",trait,".",st,".thin",thin,".",var_struc,".param.RDS"))
ctwas_parameters_susieST <- summarize_param(param_susieST, gwas_n)
total_nonSNPpve_susieST <- 1- ctwas_parameters_susieST$prop_heritability["SNP"]
pve_pie_by_type_susieST <- plot_piechart_topn(ctwas_parameters = ctwas_parameters_susieST, colors = colors, by = "type", title = "top-apa, multi-group")
param_apaonly <- readRDS(paste0(folder_results_apaonly,"/",trait,"/",trait,".",st,".thin",thin,".",var_struc,".param.RDS"))
ctwas_parameters_apaonly <- summarize_param(param_apaonly,gwas_n)
total_nonSNPpve_apaonly <- 1- ctwas_parameters_apaonly$prop_heritability["SNP"]
pve_pie_by_type_apaonly <- plot_piechart_topn(ctwas_parameters = ctwas_parameters_apaonly, colors = colors, by = "type", title = "top-apa, multi tissue apa")
top_tissue <- top_tissues[trait]
file_param_single <- paste0(folder_results_single, trait, "/", trait, "_", top_tissue, ".thin", thin, ".", var_struc, ".param.RDS")
param_single <- readRDS(file_param_single)
ctwas_parameters_single <- summarize_param(param_single, gwas_n)
total_nonSNPpve_single <- 1- ctwas_parameters_single$prop_heritability["SNP"]
pve_pie_by_type_single <- plot_piechart_topn(ctwas_parameters = ctwas_parameters_single, colors = colors, by = "context", title = "top-apa, single tissue apa")
plot_multi(pve_pie_by_type_single,pve_pie_by_type_apaonly,pve_pie_by_type_susieST, title=trait)
| Version | Author | Date |
|---|---|---|
| bda6e43 | XSun | 2025-04-09 |
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.414]BMI-panukb
trait <- "BMI-panukb"
st <- "with_susieST"
gwas_n <- samplesize[trait]Setting: shared_all, thin = 1
thin <- 1
var_struc <- "shared_all"param_susieST <- readRDS(paste0(folder_results_susieST,"/",trait,"/",trait,".",st,".thin",thin,".",var_struc,".param.RDS"))
ctwas_parameters_susieST <- summarize_param(param_susieST, gwas_n)
total_nonSNPpve_susieST <- 1- ctwas_parameters_susieST$prop_heritability["SNP"]
pve_pie_by_type_susieST <- plot_piechart_topn(ctwas_parameters = ctwas_parameters_susieST, colors = colors, by = "type", title = "top-apa, multi-group")
param_apaonly <- readRDS(paste0(folder_results_apaonly,"/",trait,"/",trait,".",st,".thin",thin,".",var_struc,".param.RDS"))
ctwas_parameters_apaonly <- summarize_param(param_apaonly,gwas_n)
total_nonSNPpve_apaonly <- 1- ctwas_parameters_apaonly$prop_heritability["SNP"]
pve_pie_by_type_apaonly <- plot_piechart_topn(ctwas_parameters = ctwas_parameters_apaonly, colors = colors, by = "type", title = "top-apa, multi tissue apa")
top_tissue <- top_tissues[trait]
file_param_single <- paste0(folder_results_single, trait, "/", trait, "_", top_tissue, ".thin", thin, ".", var_struc, ".param.RDS")
param_single <- readRDS(file_param_single)
ctwas_parameters_single <- summarize_param(param_single, gwas_n)
total_nonSNPpve_single <- 1- ctwas_parameters_single$prop_heritability["SNP"]
pve_pie_by_type_single <- plot_piechart_topn(ctwas_parameters = ctwas_parameters_single, colors = colors, by = "context", title = "top-apa, single tissue apa")
plot_multi(pve_pie_by_type_single,pve_pie_by_type_apaonly,pve_pie_by_type_susieST, title=trait)
TableGrob (2 x 3) "arrange": 4 grobs
z cells name grob
1 1 (2-2,1-1) arrange gtable[layout]
2 2 (2-2,2-2) arrange gtable[layout]
3 3 (2-2,3-3) arrange gtable[layout]
4 4 (1-1,1-3) arrange text[GRID.text.553]RBC-panukb
trait <- "RBC-panukb"
st <- "with_susieST"
gwas_n <- samplesize[trait]Setting: shared_all, thin = 1
thin <- 1
var_struc <- "shared_all"param_susieST <- readRDS(paste0(folder_results_susieST,"/",trait,"/",trait,".",st,".thin",thin,".",var_struc,".param.RDS"))
ctwas_parameters_susieST <- summarize_param(param_susieST, gwas_n)
total_nonSNPpve_susieST <- 1- ctwas_parameters_susieST$prop_heritability["SNP"]
pve_pie_by_type_susieST <- plot_piechart_topn(ctwas_parameters = ctwas_parameters_susieST, colors = colors, by = "type", title = "top-apa, multi-group")
param_apaonly <- readRDS(paste0(folder_results_apaonly,"/",trait,"/",trait,".",st,".thin",thin,".",var_struc,".param.RDS"))
ctwas_parameters_apaonly <- summarize_param(param_apaonly,gwas_n)
total_nonSNPpve_apaonly <- 1- ctwas_parameters_apaonly$prop_heritability["SNP"]
pve_pie_by_type_apaonly <- plot_piechart_topn(ctwas_parameters = ctwas_parameters_apaonly, colors = colors, by = "type", title = "top-apa, multi tissue apa")
top_tissue <- top_tissues[trait]
file_param_single <- paste0(folder_results_single, trait, "/", trait, "_", top_tissue, ".thin", thin, ".", var_struc, ".param.RDS")
param_single <- readRDS(file_param_single)
ctwas_parameters_single <- summarize_param(param_single, gwas_n)
total_nonSNPpve_single <- 1- ctwas_parameters_single$prop_heritability["SNP"]
pve_pie_by_type_single <- plot_piechart_topn(ctwas_parameters = ctwas_parameters_single, colors = colors, by = "context", title = "top-apa, single tissue apa")
plot_multi(pve_pie_by_type_single,pve_pie_by_type_apaonly,pve_pie_by_type_susieST, title=trait)
| Version | Author | Date |
|---|---|---|
| bda6e43 | XSun | 2025-04-09 |
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.692]aFib-ebi-a-GCST006414
trait <- "aFib-ebi-a-GCST006414"
st <- "with_susieST"
gwas_n <- samplesize[trait]Setting: shared_all, thin = 1
thin <- 1
var_struc <- "shared_all"param_susieST <- readRDS(paste0(folder_results_susieST,"/",trait,"/",trait,".",st,".thin",thin,".",var_struc,".param.RDS"))
ctwas_parameters_susieST <- summarize_param(param_susieST, gwas_n)
total_nonSNPpve_susieST <- 1- ctwas_parameters_susieST$prop_heritability["SNP"]
pve_pie_by_type_susieST <- plot_piechart_topn(ctwas_parameters = ctwas_parameters_susieST, colors = colors, by = "type", title = "top-apa, multi-group")
param_apaonly <- readRDS(paste0(folder_results_apaonly,"/",trait,"/",trait,".",st,".thin",thin,".",var_struc,".param.RDS"))
ctwas_parameters_apaonly <- summarize_param(param_apaonly,gwas_n)
total_nonSNPpve_apaonly <- 1- ctwas_parameters_apaonly$prop_heritability["SNP"]
pve_pie_by_type_apaonly <- plot_piechart_topn(ctwas_parameters = ctwas_parameters_apaonly, colors = colors, by = "type", title = "top-apa, multi tissue apa")
top_tissue <- top_tissues[trait]
file_param_single <- paste0(folder_results_single, trait, "/", trait, "_", top_tissue, ".thin", thin, ".", var_struc, ".param.RDS")
param_single <- readRDS(file_param_single)
ctwas_parameters_single <- summarize_param(param_single, gwas_n)
total_nonSNPpve_single <- 1- ctwas_parameters_single$prop_heritability["SNP"]
pve_pie_by_type_single <- plot_piechart_topn(ctwas_parameters = ctwas_parameters_single, colors = colors, by = "context", title = "top-apa, single tissue apa")
plot_multi(pve_pie_by_type_single,pve_pie_by_type_apaonly,pve_pie_by_type_susieST, title=trait)
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.831]
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 pheatmap_1.0.12
[5] forcats_0.5.1 stringr_1.5.1 dplyr_1.1.4 purrr_1.0.2
[9] readr_2.1.2 tidyr_1.3.0 tibble_3.2.1 tidyverse_1.3.1
[13] ggplot2_3.5.1 ctwas_0.5.4.9000
loaded via a namespace (and not attached):
[1] readxl_1.4.0 backports_1.4.1
[3] workflowr_1.7.0 BiocFileCache_2.4.0
[5] repr_1.1.4 lazyeval_0.2.2
[7] crosstalk_1.2.0 BiocParallel_1.30.3
[9] GenomeInfoDb_1.39.9 LDlinkR_1.2.3
[11] digest_0.6.29 ensembldb_2.20.2
[13] htmltools_0.5.2 fansi_1.0.3
[15] magrittr_2.0.3 memoise_2.0.1
[17] tzdb_0.4.0 Biostrings_2.64.0
[19] modelr_0.1.8 AMR_2.1.1
[21] matrixStats_0.62.0 locuszoomr_0.2.1
[23] prettyunits_1.1.1 colorspace_2.0-3
[25] skimr_2.1.4 blob_1.2.3
[27] rvest_1.0.2 rappdirs_0.3.3
[29] haven_2.5.0 xfun_0.41
[31] crayon_1.5.1 RCurl_1.98-1.7
[33] jsonlite_1.8.0 zoo_1.8-10
[35] glue_1.6.2 gtable_0.3.0
[37] zlibbioc_1.42.0 XVector_0.36.0
[39] DelayedArray_0.22.0 BiocGenerics_0.42.0
[41] scales_1.3.0 DBI_1.2.2
[43] Rcpp_1.0.12 viridisLite_0.4.0
[45] progress_1.2.2 bit_4.0.4
[47] DT_0.22 stats4_4.2.0
[49] htmlwidgets_1.5.4 httr_1.4.3
[51] RColorBrewer_1.1-3 ellipsis_0.3.2
[53] farver_2.1.0 pkgconfig_2.0.3
[55] XML_3.99-0.14 sass_0.4.1
[57] dbplyr_2.1.1 utf8_1.2.2
[59] labeling_0.4.2 tidyselect_1.2.0
[61] rlang_1.1.2 later_1.3.0
[63] AnnotationDbi_1.58.0 munsell_0.5.0
[65] pgenlibr_0.3.3 cellranger_1.1.0
[67] tools_4.2.0 cachem_1.0.6
[69] cli_3.6.1 generics_0.1.2
[71] RSQLite_2.3.1 broom_0.8.0
[73] evaluate_0.15 fastmap_1.1.0
[75] yaml_2.3.5 knitr_1.39
[77] bit64_4.0.5 fs_1.5.2
[79] KEGGREST_1.36.3 AnnotationFilter_1.20.0
[81] whisker_0.4 xml2_1.3.3
[83] biomaRt_2.54.1 compiler_4.2.0
[85] rstudioapi_0.13 plotly_4.10.0
[87] filelock_1.0.2 curl_4.3.2
[89] png_0.1-7 reprex_2.0.1
[91] bslib_0.3.1 stringi_1.7.6
[93] highr_0.9 GenomicFeatures_1.48.3
[95] lattice_0.20-45 ProtGenerics_1.28.0
[97] Matrix_1.5-3 vctrs_0.6.5
[99] pillar_1.9.0 lifecycle_1.0.4
[101] jquerylib_0.1.4 data.table_1.14.2
[103] cowplot_1.1.1 bitops_1.0-7
[105] irlba_2.3.5 httpuv_1.6.5
[107] rtracklayer_1.56.0 GenomicRanges_1.48.0
[109] R6_2.5.1 BiocIO_1.6.0
[111] promises_1.2.0.1 IRanges_2.30.0
[113] codetools_0.2-18 assertthat_0.2.1
[115] SummarizedExperiment_1.26.1 rprojroot_2.0.3
[117] rjson_0.2.21 withr_2.5.0
[119] GenomicAlignments_1.32.0 Rsamtools_2.12.0
[121] S4Vectors_0.34.0 GenomeInfoDbData_1.2.8
[123] parallel_4.2.0 hms_1.1.1
[125] gggrid_0.2-0 rmarkdown_2.25
[127] MatrixGenerics_1.8.0 logging_0.10-108
[129] git2r_0.30.1 mixsqp_0.3-43
[131] Biobase_2.56.0 lubridate_1.8.0
[133] base64enc_0.1-3 restfulr_0.0.14