Compare different settings: cs index filter or not
XSun
2025-04-28
Last updated: 2025-05-01
Checks: 6 1
Knit directory: multigroup_ctwas_analysis/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | fdbf208 | XSun | 2025-05-01 | update |
| html | fdbf208 | XSun | 2025-05-01 | update |
| Rmd | a742ef4 | XSun | 2025-04-30 | update |
| html | a742ef4 | XSun | 2025-04-30 | update |
| Rmd | 3b4edbe | XSun | 2025-04-28 | update |
| html | 3b4edbe | XSun | 2025-04-28 | update |
library(dplyr)
library(scales)
library(tidyr)
library(ggplot2)
library(eulerr)
library(patchwork)
library(ctwas)
library(EnsDb.Hsapiens.v86)
ens_db <- EnsDb.Hsapiens.v86
source("/project/xinhe/xsun/multi_group_ctwas/14.comparing_diff_settings/0.functions.R")
traits_silver <- c("T2D","LDL","BMI","RBC","IBD","SCZ","aFib")
names(traits_silver) <- c("T2D-panukb","LDL-ukb-d-30780_irnt","BMI-panukb","RBC-panukb","IBD-ebi-a-GCST004131","SCZ-ieu-b-5102","aFib-ebi-a-GCST006414")
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")
db <- "GO_Biological_Process_2023"
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)
snp_map <- readRDS("/project2/xinhe/shared_data/multigroup_ctwas/LD_region_info/snp_map.RDS")
folder_results <- "/project/xinhe/xsun/multi_group_ctwas/15.susie_weights/snakemake_outputs/"
create_summary_plot_withTP <- function(df, columns_to_plot, x_var = "setting", x_order = NULL, title = NULL) {
# Reshape data
df_long <- df %>%
pivot_longer(
cols = all_of(columns_to_plot),
names_to = "variable",
values_to = "value"
)
# Convert to factor with specified order if x_order is provided
if (!is.null(x_order)) {
df_long <- df_long %>%
mutate(across(all_of(x_var), ~factor(., levels = x_order)))
}
# Identify the max value for scaling
max_main <- max(df_long$value[df_long$variable != "TP_rate"], na.rm = TRUE)
max_tp_rate <- max(df_long$value[df_long$variable == "TP_rate"], na.rm = TRUE)
if(max_tp_rate ==0) {
max_tp_rate <- 1
}
# Rescale TP_rate
df_long <- df_long %>%
mutate(scaled_value = ifelse(variable == "TP_rate",
value * (max_main / max_tp_rate), value))
# Create plot
ggplot(df_long, aes(x = .data[[x_var]], y = scaled_value, color = variable, shape = variable)) +
#geom_point(size = 3, position = position_jitter(width = 0.2)) +
geom_point(size = 3) +
scale_y_continuous(
name = "Count",
sec.axis = sec_axis(~ . * (max_tp_rate / max_main), name = "TP Rate")
) +
labs(x = "Settings", title = title) +
theme_minimal() +
theme(
axis.text.x = element_text(angle = 45, hjust = 1, size = 10),
legend.position = "right",
legend.title = element_blank()
) +
scale_color_brewer(palette = "Set1")
}
DT::datatable(matrix())thin <- 1
var_struc <- "shared_all"
L <- 5
st <- "with_susieST"The number of genes at PIP > 0.8
setting_names <- c()
num_gene_pip08_all <- c()
num_silver_pip08_all <- c()
num_bystander_pip08_all <- c()
go_num_fdr005 <- c()
for (trait in traits) {
for (cs in c("CS_filtered","CS_NOT_filtered")){
if(cs == "CS_filtered"){
cs_setting <- NULL
}else{
cs_setting <- "_csF"
}
setting_names <- c(setting_names, paste0(traits_silver[trait],"-",cs))
# num_gene_pip08 -- cs filtered
combined_pip_by_group <- readRDS(paste0(folder_results,"/",trait,"/",trait,".",st,".thin",thin,".",var_struc,".L",L, ".combined_pip_bygroup_final",cs_setting,".RDS"))
combined_pip_sig <- combined_pip_by_group[combined_pip_by_group$combined_pip > 0.8,]
num_gene_pip08_all <- c(num_gene_pip08_all, nrow(combined_pip_sig))
# silver_standard genes
known <- readRDS(paste0("/project/xinhe/xsun/multi_group_ctwas/data/silverstandard/known_annotations_",traits_silver[trait],".RDS"))
bystander <- readRDS(paste0("/project/xinhe/xsun/multi_group_ctwas/data/silverstandard/bystanders_",traits_silver[trait],".RDS"))
num_silver_pip08_all <- c(num_silver_pip08_all,sum(combined_pip_sig$gene_name %in% known))
num_bystander_pip08_all <- c(num_bystander_pip08_all,sum(combined_pip_sig$gene_name %in% bystander))
file_go <- paste0(folder_results,trait,"/",trait,".",st,".thin",thin,".",var_struc,".L",L, ".enrichr_",db,"_",cs,".RDS")
if(!file.exists(file_go)){
go_num_fdr005 <- c(go_num_fdr005,0)
}else{
go <- readRDS(paste0(folder_results,trait,"/",trait,".",st,".thin",thin,".",var_struc,".L",L, ".enrichr_",db,"_",cs,".RDS"))
go_num_fdr005 <- c(go_num_fdr005,nrow(go))
}
}
}
df <- data.frame(setting = setting_names,
num_gene_pip08 = num_gene_pip08_all,
num_silver_pip08 = num_silver_pip08_all,
num_bystander_pip08 = num_bystander_pip08_all,
TP_rate = num_silver_pip08_all/(num_silver_pip08_all+num_bystander_pip08_all),
go_num_fdr005 = go_num_fdr005)
create_summary_plot_withTP(df,x_order = setting_names,
columns_to_plot = c("num_gene_pip08","num_silver_pip08","TP_rate"))
create_summary_plot_withTP(df,x_order = setting_names,
columns_to_plot = c("go_num_fdr005"), title = "Number of GO terms at p.adjust < 0.05")
DT::datatable(df,caption = htmltools::tags$caption( style = 'caption-side: left; text-align: left; color:black; font-size:150% ;',''),options = list(pageLength = 10) )Number of GO terms at adjusted.p < 0.05
traits <- c("LDL-ukb-d-30780_irnt","IBD-ebi-a-GCST004131")
for (trait in traits) {
file_go_filtered <- paste0(folder_results,trait,"/",trait,".",st,".thin",thin,".",var_struc,".L",L, ".enrichr_",db,"_CS_filtered.RDS")
if(!file.exists(file_go_filtered)){
go_filtered <- NULL
}else{
go_filtered <- readRDS(file_go_filtered)
}
file_go_nofiltered <- paste0(folder_results,trait,"/",trait,".",st,".thin",thin,".",var_struc,".L",L, ".enrichr_",db,"_CS_NOT_filtered.RDS")
if(!file.exists(file_go_nofiltered)){
go_nofiltered <- NULL
}else{
go_nofiltered <- readRDS(file_go_nofiltered)
}
set1 <- unique(go_filtered$Term)
set2 <- unique(go_nofiltered$Term)
venn_input <- list(
Filtered = set1,
NoFiltered = set2
)
fit <- euler(venn_input)
print(plot(fit,
fills = c("skyblue", "orange"),
labels = TRUE,
quantities = TRUE,
main = trait))
}
| Version | Author | Date |
|---|---|---|
| a742ef4 | XSun | 2025-04-30 |
traits <- c("LDL-ukb-d-30780_irnt","IBD-ebi-a-GCST004131")
for (trait in traits) {
file_go_filtered <- paste0(folder_results,trait,"/",trait,".",st,".thin",thin,".",var_struc,".L",L, ".enrichr_",db,"_CS_filtered.RDS")
if(!file.exists(file_go_filtered)){
go_filtered <- NULL
}else{
go_filtered <- readRDS(file_go_filtered)
}
file_go_nofiltered <- paste0(folder_results,trait,"/",trait,".",st,".thin",thin,".",var_struc,".L",L, ".enrichr_",db,"_CS_NOT_filtered.RDS")
if(!file.exists(file_go_nofiltered)){
go_nofiltered <- NULL
}else{
go_nofiltered <- readRDS(file_go_nofiltered)
}
cat("\n\n")
cat(knitr::knit_print(DT::datatable(go_filtered[!go_filtered$Term %in% go_nofiltered$Term,], caption = htmltools::tags$caption( style = 'font-size: 150%; caption-side: topleft; text-align = left; color:black;',paste0("Unique GO terms at p.adjust < 0.05 -- ",trait, "--CS_filtered")),options = list(pageLength = 5))))
cat("\n\n")
cat("\n\n")
cat(knitr::knit_print(DT::datatable(go_nofiltered[!go_nofiltered$Term %in% go_filtered$Term,], caption = htmltools::tags$caption( style = 'font-size: 150%; caption-side: topleft; text-align = left; color:black;',paste0("Unique GO terms at p.adjust < 0.05 -- ",trait, "--CS_NOT_filtered")),options = list(pageLength = 5))))
cat("\n\n")
}Comparing PIPs at gene-level
LDL-ukb-d-30780_irnt
trait <- "LDL-ukb-d-30780_irnt"
combined_pip_filtered <- readRDS(paste0(folder_results,"/",trait,"/",trait,".",st,".thin",thin,".",var_struc,".L",L, ".combined_pip_bygroup_final_csincluded.RDS"))
combined_pip_filtered_plot <- combined_pip_filtered[,c("gene_name","combined_pip")]
colnames(combined_pip_filtered_plot) <- c("gene_name","combined_pip_csfiltered")
combined_pip_NOTfiltered <- readRDS(paste0(folder_results,"/",trait,"/",trait,".",st,".thin",thin,".",var_struc,".L",L, ".combined_pip_bygroup_final_csF.RDS"))
combined_pip_NOTfiltered_plot <- combined_pip_NOTfiltered[,c("gene_name","combined_pip")]
colnames(combined_pip_NOTfiltered_plot) <- c("gene_name","combined_pip_csNOTfiltered")
combined_pip_filtered_sig <- combined_pip_filtered[combined_pip_filtered$combined_pip > 0.8,]
combined_pip_NOTfiltered_sig <- combined_pip_NOTfiltered[combined_pip_NOTfiltered$combined_pip > 0.8,]
combined_pip_NOTfiltered_sig_plot <- combined_pip_NOTfiltered_plot[combined_pip_NOTfiltered_plot$combined_pip > 0.8,]
df_merge <- merge(combined_pip_NOTfiltered_sig_plot, combined_pip_filtered_plot, by = "gene_name", all.x = T)
df_merge$combined_pip_csfiltered[is.na(df_merge$combined_pip_csfiltered)] <- 1.1
p <-ggplot(data = df_merge,aes(x = combined_pip_csfiltered, y = combined_pip_csNOTfiltered)) +
#p[[trait]] <-ggplot(data = df_merge,aes(x = combined_pip_csfiltered, y = combined_pip_csNOTfiltered)) +
geom_point() +
labs(x = "Combined PIP -- CS filtered", y = "Combined PIP -- CS NOT filtered") +
geom_abline(slope = 1, intercept = 0, col = "Red") +
ggtitle(trait) +
theme_minimal()
print("PIP = 1.1 means PIP = NA when filtering CS")[1] “PIP = 1.1 means PIP = NA when filtering CS”
print(p)
| Version | Author | Date |
|---|---|---|
| fdbf208 | XSun | 2025-05-01 |
# Merge the two data frames by 'gene_name' with suffixes to distinguish columns
merged_df <- merge(combined_pip_NOTfiltered_sig, combined_pip_filtered,
by = "gene_name", suffixes = c("_NOTfiltered", "_filtered"),all.x=T)
# List of columns to process (excluding 'gene_name')
original_cols <- setdiff(names(combined_pip_NOTfiltered_sig), "gene_name")
# Iterate over each column and concatenate values from both data frames
for (col in original_cols) {
notfiltered_col <- paste0(col, "_NOTfiltered")
filtered_col <- paste0(col, "_filtered")
merged_df[[col]] <- paste(round(merged_df[[notfiltered_col]],digits = 5), round(merged_df[[filtered_col]],digits = 5), sep = "-")
}
# Select the relevant columns to match the original structure
df <- merged_df[, c("gene_name", original_cols)]
DT::datatable(df, caption = htmltools::tags$caption( style = 'font-size: 150%; caption-side: topleft; text-align = left; color:black;', trait),options = list(pageLength = 5))| Gene | Involvement in LDL/Lipid Metabolism | Evidence Level |
|---|---|---|
| KIF13B | Enhances LDL uptake via LRP1 | Strong |
| TTC39B | Regulates cholesterol homeostasis via LXR degradation | Strong |
| USP53 | Modulates SR-A ubiquitination affecting LDL clearance | Moderate |
| ACVR1C | Influences adipose lipid metabolism | Moderate |
| LRRK2 | Affects lipid storage and lysosomal function | Moderate |
| ACP6 | Involved in LPA metabolism; indirect link to LDL | Limited |
| CD163L1 | Scavenger receptor; unclear role in LDL metabolism | Limited |
| KDSR | Sphingolipid biosynthesis; indirect association with LDL | Limited |
| ZNF518A | Function not well-characterized; no direct link to LDL | Limited |
file_ctwas_result <- get_ctwas_file(trait, tissue = NULL, folder_results,st,thin, var_struc, L)
ctwas_res <- readRDS(file_ctwas_result)
weights <- readRDS(paste0(folder_results,"/",trait,"/",trait,".",st,".preprocessed.weights.RDS"))
finemap_res <- ctwas_res$finemap_res
finemap_res$molecular_id <- get_molecular_ids(finemap_res)
finemap_res <- anno_finemap_res(finemap_res,
snp_map = snp_map,
mapping_table = mapping_two,
add_gene_annot = TRUE,
map_by = "molecular_id",
drop_unmapped = TRUE,
add_position = TRUE,
use_gene_pos = "mid")2025-05-01 15:47:22 INFO::Annotating fine-mapping result … 2025-05-01 15:47:27 INFO::Map molecular traits to genes 2025-05-01 15:47:27 INFO::Split PIPs for molecular traits mapped to multiple genes 2025-05-01 15:48:04 INFO::Add gene positions 2025-05-01 15:48:05 INFO::Add SNP positions
finemap_res <- finemap_res[complete.cases(finemap_res$id),]
make_locusplot(finemap_res,
region_id = "8_28304875_29470379",
ens_db = ens_db,
weights = weights,
highlight_pip = 0.8,
filter_protein_coding_genes = TRUE,
filter_cs = F,
focal_gene = "KIF13B",
color_pval_by = "cs",
color_pip_by = "cs",
label.text.size = 6,
axis.text.size = 16,
axis.title.size = 16,
legend.text.size = 16,
point.sizes = c(3,5,5,5))2025-05-01 15:48:30 INFO::Limit to protein coding genes 2025-05-01 15:48:30 INFO::focal id: intron_8_29092878_29099133|Whole_Blood_sQTL 2025-05-01 15:48:30 INFO::focal molecular trait: KIF13B Whole_Blood sQTL 2025-05-01 15:48:30 INFO::Range of locus: chr8:28306293-29470014 2025-05-01 15:48:32 INFO::focal molecular trait QTL positions: 29092792 
make_locusplot(finemap_res,
region_id = "9_14836365_16659657",
ens_db = ens_db,
weights = weights,
highlight_pip = 0.8,
filter_protein_coding_genes = TRUE,
filter_cs = F,
focal_gene = "TTC39B",
color_pval_by = "cs",
color_pip_by = "cs",
label.text.size = 6,
axis.text.size = 16,
axis.title.size = 16,
legend.text.size = 16,
point.sizes = c(3,5,5,5))2025-05-01 15:48:34 INFO::Limit to protein coding genes 2025-05-01 15:48:34 INFO::focal id: ENSG00000155158.20|Liver_eQTL 2025-05-01 15:48:34 INFO::focal molecular trait: TTC39B Liver eQTL 2025-05-01 15:48:34 INFO::Range of locus: chr9:14822730-16659361 2025-05-01 15:48:35 INFO::focal molecular trait QTL positions: 15280189,15303585,15306294 
make_locusplot(finemap_res,
region_id = "4_119012357_119471529",
ens_db = ens_db,
weights = weights,
highlight_pip = 0.8,
filter_protein_coding_genes = TRUE,
filter_cs = F,
focal_gene = "USP53",
color_pval_by = "cs",
color_pip_by = "cs",
label.text.size = 6,
axis.text.size = 16,
axis.title.size = 16,
legend.text.size = 16,
point.sizes = c(3,5,5,5))2025-05-01 15:48:37 INFO::Limit to protein coding genes 2025-05-01 15:48:37 INFO::focal id: ENSG00000145390.11|Liver_eQTL 2025-05-01 15:48:37 INFO::focal molecular trait: USP53 Liver eQTL 2025-05-01 15:48:37 INFO::Range of locus: chr4:118955868-119561793 2025-05-01 15:48:38 INFO::focal molecular trait QTL positions: 119212999,119214746 
make_locusplot(finemap_res,
region_id = "2_156704123_157676706",
ens_db = ens_db,
weights = weights,
highlight_pip = 0.8,
filter_protein_coding_genes = TRUE,
filter_cs = F,
focal_gene = "ACVR1C",
color_pval_by = "cs",
color_pip_by = "cs",
label.text.size = 6,
axis.text.size = 16,
axis.title.size = 16,
legend.text.size = 16,
point.sizes = c(3,5,5,5))2025-05-01 15:48:39 INFO::Limit to protein coding genes 2025-05-01 15:48:39 INFO::focal id: ENSG00000123612.15|Liver_eQTL 2025-05-01 15:48:39 INFO::focal molecular trait: ACVR1C Liver eQTL 2025-05-01 15:48:39 INFO::Range of locus: chr2:156704023-157675796 2025-05-01 15:48:41 INFO::focal molecular trait QTL positions: 157625480,157628563 
IBD-ebi-a-GCST004131
trait <- "IBD-ebi-a-GCST004131"
combined_pip_filtered <- readRDS(paste0(folder_results,"/",trait,"/",trait,".",st,".thin",thin,".",var_struc,".L",L, ".combined_pip_bygroup_final_csincluded.RDS"))
combined_pip_filtered_plot <- combined_pip_filtered[,c("gene_name","combined_pip")]
colnames(combined_pip_filtered_plot) <- c("gene_name","combined_pip_csfiltered")
combined_pip_NOTfiltered <- readRDS(paste0(folder_results,"/",trait,"/",trait,".",st,".thin",thin,".",var_struc,".L",L, ".combined_pip_bygroup_final_csF.RDS"))
combined_pip_NOTfiltered_plot <- combined_pip_NOTfiltered[,c("gene_name","combined_pip")]
colnames(combined_pip_NOTfiltered_plot) <- c("gene_name","combined_pip_csNOTfiltered")
combined_pip_filtered_sig <- combined_pip_filtered[combined_pip_filtered$combined_pip > 0.8,]
combined_pip_NOTfiltered_sig <- combined_pip_NOTfiltered[combined_pip_NOTfiltered$combined_pip > 0.8,]
combined_pip_NOTfiltered_sig_plot <- combined_pip_NOTfiltered_plot[combined_pip_NOTfiltered_plot$combined_pip > 0.8,]
df_merge <- merge(combined_pip_NOTfiltered_sig_plot, combined_pip_filtered_plot, by = "gene_name", all.x = T)
df_merge$combined_pip_csfiltered[is.na(df_merge$combined_pip_csfiltered)] <- 1.1
p <-ggplot(data = df_merge,aes(x = combined_pip_csfiltered, y = combined_pip_csNOTfiltered)) +
#p[[trait]] <-ggplot(data = df_merge,aes(x = combined_pip_csfiltered, y = combined_pip_csNOTfiltered)) +
geom_point() +
labs(x = "Combined PIP -- CS filtered", y = "Combined PIP -- CS NOT filtered") +
geom_abline(slope = 1, intercept = 0, col = "Red") +
ggtitle(trait) +
theme_minimal()
print("PIP = 1.1 means PIP = NA when filtering CS")[1] “PIP = 1.1 means PIP = NA when filtering CS”
print(p)
# Merge the two data frames by 'gene_name' with suffixes to distinguish columns
merged_df <- merge(combined_pip_NOTfiltered_sig, combined_pip_filtered,
by = "gene_name", suffixes = c("_NOTfiltered", "_filtered"),all.x=T)
# List of columns to process (excluding 'gene_name')
original_cols <- setdiff(names(combined_pip_NOTfiltered_sig), "gene_name")
# Iterate over each column and concatenate values from both data frames
for (col in original_cols) {
notfiltered_col <- paste0(col, "_NOTfiltered")
filtered_col <- paste0(col, "_filtered")
merged_df[[col]] <- paste(round(merged_df[[notfiltered_col]],digits = 5), round(merged_df[[filtered_col]],digits = 5), sep = "-")
}
# Select the relevant columns to match the original structure
df <- merged_df[, c("gene_name", original_cols)]
DT::datatable(df, caption = htmltools::tags$caption( style = 'font-size: 150%; caption-side: topleft; text-align = left; color:black;', trait),options = list(pageLength = 5))| Gene | Evidence of Association with IBD | Evidence Level |
|---|---|---|
| SLC26A3 | Identified as an IBD susceptibility gene; downregulation impairs epithelial barrier and increases inflammation. | Strong |
| MAST2 | Regulates NF-κB signaling; involved in inflammatory response modulation. | Moderate |
| DOCK8 | Deficiency leads to immune dysregulation with IBD-like enteropathy and colitis. | Moderate |
| FOXN2 | Regulates REG Iβ, which is upregulated in IBD mucosa. | Limited |
| ACBD3 | No direct link to IBD; studied more in cancer and viral replication contexts. | Limited |
| TMEM151B | No known evidence linking it to IBD. | None |
BMI-panukb
trait <- "BMI-panukb"
combined_pip_filtered <- readRDS(paste0(folder_results,"/",trait,"/",trait,".",st,".thin",thin,".",var_struc,".L",L, ".combined_pip_bygroup_final_csincluded.RDS"))
combined_pip_filtered_plot <- combined_pip_filtered[,c("gene_name","combined_pip")]
colnames(combined_pip_filtered_plot) <- c("gene_name","combined_pip_csfiltered")
combined_pip_NOTfiltered <- readRDS(paste0(folder_results,"/",trait,"/",trait,".",st,".thin",thin,".",var_struc,".L",L, ".combined_pip_bygroup_final_csF.RDS"))
combined_pip_NOTfiltered_plot <- combined_pip_NOTfiltered[,c("gene_name","combined_pip")]
colnames(combined_pip_NOTfiltered_plot) <- c("gene_name","combined_pip_csNOTfiltered")
combined_pip_filtered_sig <- combined_pip_filtered[combined_pip_filtered$combined_pip > 0.8,]
combined_pip_NOTfiltered_sig <- combined_pip_NOTfiltered[combined_pip_NOTfiltered$combined_pip > 0.8,]
combined_pip_NOTfiltered_sig_plot <- combined_pip_NOTfiltered_plot[combined_pip_NOTfiltered_plot$combined_pip > 0.8,]
df_merge <- merge(combined_pip_NOTfiltered_sig_plot, combined_pip_filtered_plot, by = "gene_name", all.x = T)
df_merge$combined_pip_csfiltered[is.na(df_merge$combined_pip_csfiltered)] <- 1.1
p <-ggplot(data = df_merge,aes(x = combined_pip_csfiltered, y = combined_pip_csNOTfiltered)) +
#p[[trait]] <-ggplot(data = df_merge,aes(x = combined_pip_csfiltered, y = combined_pip_csNOTfiltered)) +
geom_point() +
labs(x = "Combined PIP -- CS filtered", y = "Combined PIP -- CS NOT filtered") +
geom_abline(slope = 1, intercept = 0, col = "Red") +
ggtitle(trait) +
theme_minimal()
print("PIP = 1.1 means PIP = NA when filtering CS")[1] “PIP = 1.1 means PIP = NA when filtering CS”
print(p)
# Merge the two data frames by 'gene_name' with suffixes to distinguish columns
merged_df <- merge(combined_pip_NOTfiltered_sig, combined_pip_filtered,
by = "gene_name", suffixes = c("_NOTfiltered", "_filtered"),all.x=T)
# List of columns to process (excluding 'gene_name')
original_cols <- setdiff(names(combined_pip_NOTfiltered_sig), "gene_name")
# Iterate over each column and concatenate values from both data frames
for (col in original_cols) {
notfiltered_col <- paste0(col, "_NOTfiltered")
filtered_col <- paste0(col, "_filtered")
merged_df[[col]] <- paste(round(merged_df[[notfiltered_col]],digits = 5), round(merged_df[[filtered_col]],digits = 5), sep = "-")
}
# Select the relevant columns to match the original structure
df <- merged_df[, c("gene_name", original_cols)]
DT::datatable(df, caption = htmltools::tags$caption( style = 'font-size: 150%; caption-side: topleft; text-align = left; color:black;', trait),options = list(pageLength = 5))RBC-panukb
trait <- "RBC-panukb"
combined_pip_filtered <- readRDS(paste0(folder_results,"/",trait,"/",trait,".",st,".thin",thin,".",var_struc,".L",L, ".combined_pip_bygroup_final_csincluded.RDS"))
combined_pip_filtered_plot <- combined_pip_filtered[,c("gene_name","combined_pip")]
colnames(combined_pip_filtered_plot) <- c("gene_name","combined_pip_csfiltered")
combined_pip_NOTfiltered <- readRDS(paste0(folder_results,"/",trait,"/",trait,".",st,".thin",thin,".",var_struc,".L",L, ".combined_pip_bygroup_final_csF.RDS"))
combined_pip_NOTfiltered_plot <- combined_pip_NOTfiltered[,c("gene_name","combined_pip")]
colnames(combined_pip_NOTfiltered_plot) <- c("gene_name","combined_pip_csNOTfiltered")
combined_pip_filtered_sig <- combined_pip_filtered[combined_pip_filtered$combined_pip > 0.8,]
combined_pip_NOTfiltered_sig <- combined_pip_NOTfiltered[combined_pip_NOTfiltered$combined_pip > 0.8,]
combined_pip_NOTfiltered_sig_plot <- combined_pip_NOTfiltered_plot[combined_pip_NOTfiltered_plot$combined_pip > 0.8,]
df_merge <- merge(combined_pip_NOTfiltered_sig_plot, combined_pip_filtered_plot, by = "gene_name", all.x = T)
df_merge$combined_pip_csfiltered[is.na(df_merge$combined_pip_csfiltered)] <- 1.1
p <-ggplot(data = df_merge,aes(x = combined_pip_csfiltered, y = combined_pip_csNOTfiltered)) +
#p[[trait]] <-ggplot(data = df_merge,aes(x = combined_pip_csfiltered, y = combined_pip_csNOTfiltered)) +
geom_point() +
labs(x = "Combined PIP -- CS filtered", y = "Combined PIP -- CS NOT filtered") +
geom_abline(slope = 1, intercept = 0, col = "Red") +
ggtitle(trait) +
theme_minimal()
print("PIP = 1.1 means PIP = NA when filtering CS")[1] “PIP = 1.1 means PIP = NA when filtering CS”
print(p)
# Merge the two data frames by 'gene_name' with suffixes to distinguish columns
merged_df <- merge(combined_pip_NOTfiltered_sig, combined_pip_filtered,
by = "gene_name", suffixes = c("_NOTfiltered", "_filtered"),all.x=T)
# List of columns to process (excluding 'gene_name')
original_cols <- setdiff(names(combined_pip_NOTfiltered_sig), "gene_name")
# Iterate over each column and concatenate values from both data frames
for (col in original_cols) {
notfiltered_col <- paste0(col, "_NOTfiltered")
filtered_col <- paste0(col, "_filtered")
merged_df[[col]] <- paste(round(merged_df[[notfiltered_col]],digits = 5), round(merged_df[[filtered_col]],digits = 5), sep = "-")
}
# Select the relevant columns to match the original structure
df <- merged_df[, c("gene_name", original_cols)]
DT::datatable(df, caption = htmltools::tags$caption( style = 'font-size: 150%; caption-side: topleft; text-align = left; color:black;', trait),options = list(pageLength = 5))SCZ-ieu-b-5102
trait <- "SCZ-ieu-b-5102"
combined_pip_filtered <- readRDS(paste0(folder_results,"/",trait,"/",trait,".",st,".thin",thin,".",var_struc,".L",L, ".combined_pip_bygroup_final_csincluded.RDS"))
combined_pip_filtered_plot <- combined_pip_filtered[,c("gene_name","combined_pip")]
colnames(combined_pip_filtered_plot) <- c("gene_name","combined_pip_csfiltered")
combined_pip_NOTfiltered <- readRDS(paste0(folder_results,"/",trait,"/",trait,".",st,".thin",thin,".",var_struc,".L",L, ".combined_pip_bygroup_final_csF.RDS"))
combined_pip_NOTfiltered_plot <- combined_pip_NOTfiltered[,c("gene_name","combined_pip")]
colnames(combined_pip_NOTfiltered_plot) <- c("gene_name","combined_pip_csNOTfiltered")
combined_pip_filtered_sig <- combined_pip_filtered[combined_pip_filtered$combined_pip > 0.8,]
combined_pip_NOTfiltered_sig <- combined_pip_NOTfiltered[combined_pip_NOTfiltered$combined_pip > 0.8,]
combined_pip_NOTfiltered_sig_plot <- combined_pip_NOTfiltered_plot[combined_pip_NOTfiltered_plot$combined_pip > 0.8,]
df_merge <- merge(combined_pip_NOTfiltered_sig_plot, combined_pip_filtered_plot, by = "gene_name", all.x = T)
df_merge$combined_pip_csfiltered[is.na(df_merge$combined_pip_csfiltered)] <- 1.1
p <-ggplot(data = df_merge,aes(x = combined_pip_csfiltered, y = combined_pip_csNOTfiltered)) +
#p[[trait]] <-ggplot(data = df_merge,aes(x = combined_pip_csfiltered, y = combined_pip_csNOTfiltered)) +
geom_point() +
labs(x = "Combined PIP -- CS filtered", y = "Combined PIP -- CS NOT filtered") +
geom_abline(slope = 1, intercept = 0, col = "Red") +
ggtitle(trait) +
theme_minimal()
print("PIP = 1.1 means PIP = NA when filtering CS")[1] “PIP = 1.1 means PIP = NA when filtering CS”
print(p)
# Merge the two data frames by 'gene_name' with suffixes to distinguish columns
merged_df <- merge(combined_pip_NOTfiltered_sig, combined_pip_filtered,
by = "gene_name", suffixes = c("_NOTfiltered", "_filtered"),all.x=T)
# List of columns to process (excluding 'gene_name')
original_cols <- setdiff(names(combined_pip_NOTfiltered_sig), "gene_name")
# Iterate over each column and concatenate values from both data frames
for (col in original_cols) {
notfiltered_col <- paste0(col, "_NOTfiltered")
filtered_col <- paste0(col, "_filtered")
merged_df[[col]] <- paste(round(merged_df[[notfiltered_col]],digits = 5), round(merged_df[[filtered_col]],digits = 5), sep = "-")
}
# Select the relevant columns to match the original structure
df <- merged_df[, c("gene_name", original_cols)]
DT::datatable(df, caption = htmltools::tags$caption( style = 'font-size: 150%; caption-side: topleft; text-align = left; color:black;', trait),options = list(pageLength = 5))aFib-ebi-a-GCST006414
trait <- "aFib-ebi-a-GCST006414"
combined_pip_filtered <- readRDS(paste0(folder_results,"/",trait,"/",trait,".",st,".thin",thin,".",var_struc,".L",L, ".combined_pip_bygroup_final_csincluded.RDS"))
combined_pip_filtered_plot <- combined_pip_filtered[,c("gene_name","combined_pip")]
colnames(combined_pip_filtered_plot) <- c("gene_name","combined_pip_csfiltered")
combined_pip_NOTfiltered <- readRDS(paste0(folder_results,"/",trait,"/",trait,".",st,".thin",thin,".",var_struc,".L",L, ".combined_pip_bygroup_final_csF.RDS"))
combined_pip_NOTfiltered_plot <- combined_pip_NOTfiltered[,c("gene_name","combined_pip")]
colnames(combined_pip_NOTfiltered_plot) <- c("gene_name","combined_pip_csNOTfiltered")
combined_pip_filtered_sig <- combined_pip_filtered[combined_pip_filtered$combined_pip > 0.8,]
combined_pip_NOTfiltered_sig <- combined_pip_NOTfiltered[combined_pip_NOTfiltered$combined_pip > 0.8,]
combined_pip_NOTfiltered_sig_plot <- combined_pip_NOTfiltered_plot[combined_pip_NOTfiltered_plot$combined_pip > 0.8,]
df_merge <- merge(combined_pip_NOTfiltered_sig_plot, combined_pip_filtered_plot, by = "gene_name", all.x = T)
df_merge$combined_pip_csfiltered[is.na(df_merge$combined_pip_csfiltered)] <- 1.1
p <-ggplot(data = df_merge,aes(x = combined_pip_csfiltered, y = combined_pip_csNOTfiltered)) +
#p[[trait]] <-ggplot(data = df_merge,aes(x = combined_pip_csfiltered, y = combined_pip_csNOTfiltered)) +
geom_point() +
labs(x = "Combined PIP -- CS filtered", y = "Combined PIP -- CS NOT filtered") +
geom_abline(slope = 1, intercept = 0, col = "Red") +
ggtitle(trait) +
theme_minimal()
print("PIP = 1.1 means PIP = NA when filtering CS")[1] “PIP = 1.1 means PIP = NA when filtering CS”
print(p)
# Merge the two data frames by 'gene_name' with suffixes to distinguish columns
merged_df <- merge(combined_pip_NOTfiltered_sig, combined_pip_filtered,
by = "gene_name", suffixes = c("_NOTfiltered", "_filtered"),all.x=T)
# List of columns to process (excluding 'gene_name')
original_cols <- setdiff(names(combined_pip_NOTfiltered_sig), "gene_name")
# Iterate over each column and concatenate values from both data frames
for (col in original_cols) {
notfiltered_col <- paste0(col, "_NOTfiltered")
filtered_col <- paste0(col, "_filtered")
merged_df[[col]] <- paste(round(merged_df[[notfiltered_col]],digits = 5), round(merged_df[[filtered_col]],digits = 5), sep = "-")
}
# Select the relevant columns to match the original structure
df <- merged_df[, c("gene_name", original_cols)]
DT::datatable(df, caption = htmltools::tags$caption( style = 'font-size: 150%; caption-side: topleft; text-align = left; color:black;', trait),options = list(pageLength = 5))traits <- c("LDL-ukb-d-30780_irnt","IBD-ebi-a-GCST004131","BMI-panukb","RBC-panukb","SCZ-ieu-b-5102","aFib-ebi-a-GCST006414","T2D-panukb")
p <- list()
for (trait in traits) {
combined_pip_filtered <- readRDS(paste0(folder_results,"/",trait,"/",trait,".",st,".thin",thin,".",var_struc,".L",L, ".combined_pip_bygroup_final_csincluded.RDS"))
combined_pip_filtered_plot <- combined_pip_filtered[,c("gene_name","combined_pip")]
colnames(combined_pip_filtered_plot) <- c("gene_name","combined_pip_csfiltered")
combined_pip_NOTfiltered <- readRDS(paste0(folder_results,"/",trait,"/",trait,".",st,".thin",thin,".",var_struc,".L",L, ".combined_pip_bygroup_final_csF.RDS"))
combined_pip_NOTfiltered_plot <- combined_pip_NOTfiltered[,c("gene_name","combined_pip")]
colnames(combined_pip_NOTfiltered_plot) <- c("gene_name","combined_pip_csNOTfiltered")
combined_pip_filtered_sig <- combined_pip_filtered[combined_pip_filtered$combined_pip > 0.8,]
combined_pip_NOTfiltered_sig <- combined_pip_NOTfiltered[combined_pip_NOTfiltered$combined_pip > 0.8,]
combined_pip_NOTfiltered_sig_plot <- combined_pip_NOTfiltered_plot[combined_pip_NOTfiltered_plot$combined_pip > 0.8,]
df_merge <- merge(combined_pip_NOTfiltered_sig_plot, combined_pip_filtered_plot, by = "gene_name", all.x = T)
df_merge$combined_pip_csfiltered[is.na(df_merge$combined_pip_csfiltered)] <- 1.1
p <-ggplot(data = df_merge,aes(x = combined_pip_csfiltered, y = combined_pip_csNOTfiltered)) +
#p[[trait]] <-ggplot(data = df_merge,aes(x = combined_pip_csfiltered, y = combined_pip_csNOTfiltered)) +
geom_point() +
labs(x = "Combined PIP -- CS filtered", y = "Combined PIP -- CS NOT filtered") +
geom_abline(slope = 1, intercept = 0, col = "Red") +
ggtitle(trait) +
theme_minimal()
print("PIP = 1.1 means PIP = NA when filtering CS")
print(p)
# Merge the two data frames by 'gene_name' with suffixes to distinguish columns
merged_df <- merge(combined_pip_NOTfiltered_sig, combined_pip_filtered,
by = "gene_name", suffixes = c("_NOTfiltered", "_filtered"),all.x=T)
# List of columns to process (excluding 'gene_name')
original_cols <- setdiff(names(combined_pip_NOTfiltered_sig), "gene_name")
# Iterate over each column and concatenate values from both data frames
for (col in original_cols) {
notfiltered_col <- paste0(col, "_NOTfiltered")
filtered_col <- paste0(col, "_filtered")
merged_df[[col]] <- paste(round(merged_df[[notfiltered_col]],digits = 5), round(merged_df[[filtered_col]],digits = 5), sep = "-")
}
# Select the relevant columns to match the original structure
df <- merged_df[, c("gene_name", original_cols)]
cat("\n\n")
cat(knitr::knit_print(DT::datatable(df, caption = htmltools::tags$caption( style = 'font-size: 150%; caption-side: topleft; text-align = left; color:black;', trait),options = list(pageLength = 5))))
cat("\n\n")
}[1] “PIP = 1.1 means PIP = NA when filtering CS” 
[1] “PIP = 1.1 means PIP = NA when filtering CS” 
[1] “PIP = 1.1 means PIP = NA when filtering CS” 
[1] “PIP = 1.1 means PIP = NA when filtering CS” 
[1] “PIP = 1.1 means PIP = NA when filtering CS” 
[1] “PIP = 1.1 means PIP = NA when filtering CS” 
[1] “PIP = 1.1 means PIP = NA when filtering CS” 
#wrap_plots(lapply(p, wrap_elements), ncol = 4)LDL:
| Gene | Involvement in LDL/Lipid Metabolism | Evidence Level |
|---|---|---|
| KIF13B | Enhances LDL uptake via LRP1 | Strong |
| TTC39B | Regulates cholesterol homeostasis via LXR degradation | Strong |
| USP53 | Modulates SR-A ubiquitination affecting LDL clearance | Moderate |
| ACVR1C | Influences adipose lipid metabolism | Moderate |
| LRRK2 | Affects lipid storage and lysosomal function | Moderate |
| ACP6 | Involved in LPA metabolism; indirect link to LDL | Limited |
| CD163L1 | Scavenger receptor; unclear role in LDL metabolism | Limited |
| KDSR | Sphingolipid biosynthesis; indirect association with LDL | Limited |
| ZNF518A | Function not well-characterized; no direct link to LDL | Limited |
T2D-panukb
trait <- "T2D-panukb"
combined_pip_filtered <- readRDS(paste0(folder_results,"/",trait,"/",trait,".",st,".thin",thin,".",var_struc,".L",L, ".combined_pip_bygroup_final_csincluded.RDS"))
combined_pip_filtered_plot <- combined_pip_filtered[,c("gene_name","combined_pip")]
colnames(combined_pip_filtered_plot) <- c("gene_name","combined_pip_csfiltered")
combined_pip_NOTfiltered <- readRDS(paste0(folder_results,"/",trait,"/",trait,".",st,".thin",thin,".",var_struc,".L",L, ".combined_pip_bygroup_final_csF.RDS"))
combined_pip_NOTfiltered_plot <- combined_pip_NOTfiltered[,c("gene_name","combined_pip")]
colnames(combined_pip_NOTfiltered_plot) <- c("gene_name","combined_pip_csNOTfiltered")
combined_pip_filtered_sig <- combined_pip_filtered[combined_pip_filtered$combined_pip > 0.8,]
combined_pip_NOTfiltered_sig <- combined_pip_NOTfiltered[combined_pip_NOTfiltered$combined_pip > 0.8,]
combined_pip_NOTfiltered_sig_plot <- combined_pip_NOTfiltered_plot[combined_pip_NOTfiltered_plot$combined_pip > 0.8,]
df_merge <- merge(combined_pip_NOTfiltered_sig_plot, combined_pip_filtered_plot, by = "gene_name", all.x = T)
df_merge$combined_pip_csfiltered[is.na(df_merge$combined_pip_csfiltered)] <- 1.1
p <-ggplot(data = df_merge,aes(x = combined_pip_csfiltered, y = combined_pip_csNOTfiltered)) +
#p[[trait]] <-ggplot(data = df_merge,aes(x = combined_pip_csfiltered, y = combined_pip_csNOTfiltered)) +
geom_point() +
labs(x = "Combined PIP -- CS filtered", y = "Combined PIP -- CS NOT filtered") +
geom_abline(slope = 1, intercept = 0, col = "Red") +
ggtitle(trait) +
theme_minimal()
print("PIP = 1.1 means PIP = NA when filtering CS")[1] “PIP = 1.1 means PIP = NA when filtering CS”
print(p)
# Merge the two data frames by 'gene_name' with suffixes to distinguish columns
merged_df <- merge(combined_pip_NOTfiltered_sig, combined_pip_filtered,
by = "gene_name", suffixes = c("_NOTfiltered", "_filtered"),all.x=T)
# List of columns to process (excluding 'gene_name')
original_cols <- setdiff(names(combined_pip_NOTfiltered_sig), "gene_name")
# Iterate over each column and concatenate values from both data frames
for (col in original_cols) {
notfiltered_col <- paste0(col, "_NOTfiltered")
filtered_col <- paste0(col, "_filtered")
merged_df[[col]] <- paste(round(merged_df[[notfiltered_col]],digits = 5), round(merged_df[[filtered_col]],digits = 5), sep = "-")
}
# Select the relevant columns to match the original structure
df <- merged_df[, c("gene_name", original_cols)]
DT::datatable(df, caption = htmltools::tags$caption( style = 'font-size: 150%; caption-side: topleft; text-align = left; color:black;', trait),options = list(pageLength = 5))
sessionInfo()R version 4.2.0 (2022-04-22)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Red Hat Enterprise Linux 8.4 (Ootpa)
Matrix products: default
BLAS/LAPACK: /software/openblas-0.3.13-el8-x86_64/lib/libopenblas_skylakexp-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] stats4 stats graphics grDevices utils datasets methods
[8] base
other attached packages:
[1] EnsDb.Hsapiens.v86_2.99.0 ensembldb_2.22.0
[3] AnnotationFilter_1.22.0 GenomicFeatures_1.50.4
[5] AnnotationDbi_1.60.2 Biobase_2.58.0
[7] GenomicRanges_1.50.2 GenomeInfoDb_1.34.9
[9] IRanges_2.32.0 S4Vectors_0.36.2
[11] BiocGenerics_0.44.0 ctwas_0.5.13
[13] patchwork_1.1.1 eulerr_7.0.2
[15] ggplot2_3.4.2 tidyr_1.3.0
[17] scales_1.2.0 dplyr_1.1.2
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_0.38.0 locuszoomr_0.1.5
[7] base64enc_0.1-3 fs_1.5.2
[9] rstudioapi_0.14 farver_2.1.0
[11] DT_0.22 ggrepel_0.9.3
[13] bit64_4.0.5 fansi_1.0.3
[15] xml2_1.3.3 codetools_0.2-18
[17] logging_0.10-108 cachem_1.0.6
[19] knitr_1.42 polyclip_1.10-0
[21] jsonlite_1.8.9 workflowr_1.7.1
[23] Rsamtools_2.14.0 dbplyr_2.3.2
[25] png_0.1-7 readr_2.1.4
[27] compiler_4.2.0 httr_1.4.7
[29] Matrix_1.6-1.1 fastmap_1.1.0
[31] lazyeval_0.2.2 cli_3.6.2
[33] later_1.3.0 htmltools_0.5.7
[35] prettyunits_1.1.1 tools_4.2.0
[37] gtable_0.3.0 glue_1.6.2
[39] GenomeInfoDbData_1.2.9 rappdirs_0.3.3
[41] Rcpp_1.0.14 jquerylib_0.1.4
[43] vctrs_0.6.1 Biostrings_2.66.0
[45] rtracklayer_1.58.0 crosstalk_1.2.0
[47] polylabelr_0.3.0 xfun_0.38
[49] stringr_1.5.0 irlba_2.3.5
[51] lifecycle_1.0.4 restfulr_0.0.15
[53] XML_3.99-0.9 zlibbioc_1.44.0
[55] gggrid_0.2-0 hms_1.1.3
[57] promises_1.2.0.1 MatrixGenerics_1.10.0
[59] ProtGenerics_1.30.0 parallel_4.2.0
[61] SummarizedExperiment_1.28.0 RColorBrewer_1.1-3
[63] LDlinkR_1.3.0 yaml_2.3.5
[65] curl_4.3.2 memoise_2.0.1
[67] sass_0.4.1 biomaRt_2.54.1
[69] stringi_1.7.6 RSQLite_2.3.1
[71] highr_0.9 BiocIO_1.8.0
[73] filelock_1.0.2 BiocParallel_1.32.6
[75] repr_1.1.4 rlang_1.1.2
[77] pkgconfig_2.0.3 matrixStats_1.2.0
[79] bitops_1.0-7 evaluate_0.15
[81] lattice_0.20-45 purrr_1.0.1
[83] labeling_0.4.2 GenomicAlignments_1.34.1
[85] htmlwidgets_1.6.2 cowplot_1.1.1
[87] bit_4.0.4 tidyselect_1.2.0
[89] magrittr_2.0.3 AMR_2.1.1
[91] R6_2.5.1 generics_0.1.3
[93] DelayedArray_0.24.0 DBI_1.1.2
[95] pgenlibr_0.3.6 pillar_1.9.0
[97] whisker_0.4 withr_2.5.0
[99] KEGGREST_1.38.0 RCurl_1.98-1.12
[101] mixsqp_0.3-48 tibble_3.2.1
[103] crayon_1.5.1 utf8_1.2.2
[105] BiocFileCache_2.6.1 plotly_4.10.0
[107] tzdb_0.3.0 rmarkdown_2.21
[109] progress_1.2.2 grid_4.2.0
[111] data.table_1.14.4 blob_1.2.3
[113] git2r_0.30.1 digest_0.6.29
[115] httpuv_1.6.5 munsell_0.5.0
[117] viridisLite_0.4.0 skimr_2.1.4
[119] bslib_0.3.1