6 Traits, 5 tissues, eQTL + sQTL + stQTL – compute ukbb LD, eqtl, sqtl from predictdb; stQTL from Munro et al; post-processing
XSun
2024-12-09
Last updated: 2024-12-10
Checks: 6 1
Knit directory: multigroup_ctwas_analysis/
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Introduction
We compare post-processed results with the original results: https://sq-96.github.io/multigroup_ctwas_analysis/multi_group_6traits_15weights_ess.html
The post-processing steps include the following:
Region Merging
For the regions with
susie_pip > 0.5LD Mismatch Fixing
- Regions were selected where nonSNP_PIP > 0.5.
- For genes with
susie_pip > thresholds(0.5 and 0.2), we performed LD mismatch diagnosis. - To address LD mismatches, two strategies were employed:
- Fine-mapping the region without LD.
- Removing mismatched SNPs, updating gene Z-scores, and re-fine-mapping with LD.
library(ctwas)
library(EnsDb.Hsapiens.v86)
library(ggplot2)
library(gridExtra)
library(dplyr)
ens_db <- EnsDb.Hsapiens.v86
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)
#
#
# compute_pip_per_cs <- function(combined_data, susie_data) {
# # Initialize an empty list to store results
# details <- list()
#
# # Iterate over each unique gene name in the combined data
# unique_genes <- unique(combined_data$gene_name)
#
# for (genename in unique_genes) {
# # dplyr::filter susie data for the current gene
# susie_alpha_res_multi_per_gene <- susie_data %>%
# dplyr::filter(gene_name == genename)
#
# # Get all unique credible sets for the current gene
# cs_all <- unique(susie_alpha_res_multi_per_gene$susie_set[susie_alpha_res_multi_per_gene$in_cs])
#
# if (length(cs_all) > 1) {
# # dplyr::filter complete cases and those in credible sets
# susie_alpha_res_multi_per_gene <- susie_alpha_res_multi_per_gene %>%
# dplyr::filter(complete.cases(cs), in_cs)
#
# # Summarize the data
# summed_alpha_with_details <- susie_alpha_res_multi_per_gene %>%
# group_by(susie_set) %>%
# summarise(
# total_susie_alpha = round(sum(susie_alpha, na.rm = TRUE), digits = 3),
# num_molecular_traits = n(),
# ids_pip = paste0(id, "(", round(susie_alpha, digits = 3), ")", collapse = ", ")
# )
#
# # Add gene name to the summarized data
# summed_alpha_with_details$gene_name <- genename
#
# # Append the result to the details list
# details[[length(details) + 1]] <- summed_alpha_with_details
# }
# }
#
# # Combine all results into a single data frame
# final_details <- bind_rows(details)
#
# if(nrow(final_details) > 0){
# final_details <- final_details[,c("gene_name","susie_set","total_susie_alpha","num_molecular_traits","ids_pip")]
# colnames(final_details) <- c("gene_name","CS","total_PIP_CS","num_molecular_traits_CS","ids_pip_CS")
# }
#
#
# return(final_details)
# }aFib-ebi-a-GCST006414
trait <- "aFib-ebi-a-GCST006414"
results_dir_origin <- paste0("/project/xinhe/xsun/multi_group_ctwas/11.multi_group_1008/results/",trait,"/")
ctwas_res_origin <- readRDS(paste0(results_dir_origin,trait,".ctwas.res.RDS"))
finemap_res_origin <- ctwas_res_origin$finemap_resRegion merge
load(paste0("/project/xinhe/xsun/multi_group_ctwas/11.multi_group_1008/post_process_rm_ld/rm_",trait,".rdata"))
finemap_res_rm <- res_regionmerge$finemap_res
finemap_res_rm_boundary_genes <- finemap_res_rm[finemap_res_rm$id %in%selected_boundary_genes$id,]
finemap_res_rm_boundary_genes_pip <- finemap_res_rm_boundary_genes[,c("id","susie_pip","cs")]
finemap_res_origin_boundary_genes <- finemap_res_origin[finemap_res_origin$id %in%selected_boundary_genes$id,]
finemap_res_origin_boundary_genes_pip <- finemap_res_origin_boundary_genes[,c("id","susie_pip","cs")]
finemap_res_compare_regionmerge <- merge(finemap_res_origin_boundary_genes_pip,finemap_res_rm_boundary_genes_pip, by = "id")
colnames(finemap_res_compare_regionmerge) <- c("id","susie_pip_origin","cs_origin","susie_pip_reginmerge","cs_reginmerge")
DT::datatable(finemap_res_compare_regionmerge,caption = htmltools::tags$caption( style = 'caption-side: left; text-align: left; color:black; font-size:150% ;','Selected boundary genes (susie_pip > 0.5)'),options = list(pageLength = 10) )LD-mismatch
Diagnosis
load(paste0("/project/xinhe/xsun/multi_group_ctwas/11.multi_group_1008/post_process_rm_ld/ldmismatch_diagnosis_pipthres02_", trait, ".rdata"))
pip_02 <- data.frame(
"PIP Threshold" = "0.2",
"Number of Selected Regions" = length(selected_region_ids),
"Number of Problematic Genes" = length(problematic_genes),
"Number of Problematic Regions" = length(problematic_region_ids),
"Number of Problematic SNPs" = length(res_ldmismatch$problematic_snps),
"Number of Flipped SNPs" = length(res_ldmismatch$flipped_snps)
)
load(paste0("/project/xinhe/xsun/multi_group_ctwas/11.multi_group_1008/post_process_rm_ld/ldmismatch_diagnosis_pipthres05_", trait, ".rdata"))
pip_05 <- data.frame(
"PIP Threshold" = "0.5",
"Number of Selected Regions" = length(selected_region_ids),
"Number of Problematic Genes" = length(problematic_genes),
"Number of Problematic Regions" = length(problematic_region_ids),
"Number of Problematic SNPs" = length(res_ldmismatch$problematic_snps),
"Number of Flipped SNPs" = length(res_ldmismatch$flipped_snps)
)
results_table <- rbind(pip_02, pip_05)
DT::datatable(results_table,caption = htmltools::tags$caption( style = 'caption-side: left; text-align: left; color:black; font-size:150% ;','LD mismatch diagnosis table for different gene cutoff'),options = list(pageLength = 10) )Comparing 2 LD mismatch fixing methods
load(paste0("/project/xinhe/xsun/multi_group_ctwas/11.multi_group_1008/post_process_rm_ld/ldmismatch_pipthres05_nold_",trait,".rdata"))
finemap_res_ldmm_nold <- res_ldmm_nold$finemap_res
load(paste0("/project/xinhe/xsun/multi_group_ctwas/11.multi_group_1008/post_process_rm_ld/ldmismatch_pipthres05_removesnp_",trait,".rdata"))
finemap_res_ldmm_removesnp <- res_ldmm_removesnp$finemap_res
finemap_res_ldmm_nold_problematic_gene <- finemap_res_ldmm_nold[finemap_res_ldmm_nold$region_id %in% problematic_region_ids & finemap_res_ldmm_nold$type != "SNP",]
finemap_res_ldmm_removesnp_problematic_gene <- finemap_res_ldmm_removesnp[finemap_res_ldmm_removesnp$region_id %in% problematic_region_ids & finemap_res_ldmm_removesnp$type != "SNP",]
merge_2method <- merge(finemap_res_ldmm_nold_problematic_gene,finemap_res_ldmm_removesnp_problematic_gene, by ="id")
p1 <- ggplot(data = merge_2method, aes(x= susie_pip.x, y= susie_pip.y)) +
geom_point() +
labs(x="PIP_noLD", y="PIP_removesnp") +
geom_abline(slope = 1, intercept = 0, col ="red") +
ggtitle("problematic regions only, genes only") +
theme_minimal()
finemap_res_rm_problematic_gene <- finemap_res_rm[finemap_res_rm$region_id %in% problematic_region_ids & finemap_res_rm$type != "SNP",]
merge_rm_ldmm_nold <- merge(finemap_res_rm_problematic_gene,finemap_res_ldmm_nold_problematic_gene, by ="id")
p2 <- ggplot(data = merge_rm_ldmm_nold, aes(x= susie_pip.x, y= susie_pip.y)) +
geom_point() +
labs(x="PIP_after_regionmerge", y="PIP_noLD") +
geom_abline(slope = 1, intercept = 0, col ="red") +
ggtitle("problematic regions only, genes only") +
theme_minimal()
merge_rm_ldmm_removesnp <- merge(finemap_res_rm_problematic_gene,finemap_res_ldmm_removesnp_problematic_gene, by ="id")
p3 <- ggplot(data = merge_rm_ldmm_removesnp, aes(x= susie_pip.x, y= susie_pip.y)) +
geom_point() +
labs(x="PIP_after_regionmerge", y="PIP_removesnp") +
geom_abline(slope = 1, intercept = 0, col ="red") +
ggtitle("problematic regions only, genes only") +
theme_minimal()
grid.arrange(p1,p2,p3, ncol = 3)
Comparing z-scores and susie_pip
finemap_res_origin <- ctwas_res_origin$finemap_res
finemap_res_origin_gene <- finemap_res_origin[finemap_res_origin$type != "SNP",]
p1 <- ggplot(data = finemap_res_origin_gene, aes(x= abs(z), y= susie_pip)) +
geom_point() +
ggtitle("Original ctwas results") +
theme_minimal()
finemap_res_rm_gene <- finemap_res_rm[finemap_res_rm$type != "SNP",]
p2 <- ggplot(data = finemap_res_rm_gene, aes(x= abs(z), y= susie_pip)) +
geom_point() +
ggtitle("After region merge") +
theme_minimal()
finemap_res_ldmm_nold_gene <- finemap_res_ldmm_nold[finemap_res_ldmm_nold$type !="SNP",]
p3 <- ggplot(data = finemap_res_ldmm_nold_gene, aes(x= abs(z), y= susie_pip)) +
geom_point() +
ggtitle("After LD mismatch fixed -- noLD") +
theme_minimal()
finemap_res_ldmm_removesnp_gene <- finemap_res_ldmm_removesnp[finemap_res_ldmm_removesnp$type !="SNP",]
p4 <- ggplot(data = finemap_res_ldmm_removesnp_gene, aes(x= abs(z), y= susie_pip)) +
geom_point() +
ggtitle("After LD mismatch fixed -- SNP removed") +
theme_minimal()
grid.arrange(p1,p2,p3,p4, ncol = 4)
LDL-ukb-d-30780_irnt
trait <- "LDL-ukb-d-30780_irnt"
results_dir_origin <- paste0("/project/xinhe/xsun/multi_group_ctwas/11.multi_group_1008/results/",trait,"/")
ctwas_res_origin <- readRDS(paste0(results_dir_origin,trait,".ctwas.res.RDS"))
finemap_res_origin <- ctwas_res_origin$finemap_resRegion merge
load(paste0("/project/xinhe/xsun/multi_group_ctwas/11.multi_group_1008/post_process_rm_ld/rm_",trait,".rdata"))
finemap_res_rm <- res_regionmerge$finemap_res
finemap_res_rm_boundary_genes <- finemap_res_rm[finemap_res_rm$id %in%selected_boundary_genes$id,]
finemap_res_rm_boundary_genes_pip <- finemap_res_rm_boundary_genes[,c("id","susie_pip","cs")]
finemap_res_origin_boundary_genes <- finemap_res_origin[finemap_res_origin$id %in%selected_boundary_genes$id,]
finemap_res_origin_boundary_genes_pip <- finemap_res_origin_boundary_genes[,c("id","susie_pip","cs")]
finemap_res_compare_regionmerge <- merge(finemap_res_origin_boundary_genes_pip,finemap_res_rm_boundary_genes_pip, by = "id")
colnames(finemap_res_compare_regionmerge) <- c("id","susie_pip_origin","cs_origin","susie_pip_reginmerge","cs_reginmerge")
DT::datatable(finemap_res_compare_regionmerge,caption = htmltools::tags$caption( style = 'caption-side: left; text-align: left; color:black; font-size:150% ;','Selected boundary genes (susie_pip > 0.5)'),options = list(pageLength = 10) )LD-mismatch
Diagnosis
load(paste0("/project/xinhe/xsun/multi_group_ctwas/11.multi_group_1008/post_process_rm_ld/ldmismatch_diagnosis_pipthres02_", trait, ".rdata"))
pip_02 <- data.frame(
"PIP Threshold" = "0.2",
"Number of Selected Regions" = length(selected_region_ids),
"Number of Problematic Genes" = length(problematic_genes),
"Number of Problematic Regions" = length(problematic_region_ids),
"Number of Problematic SNPs" = length(res_ldmismatch$problematic_snps),
"Number of Flipped SNPs" = length(res_ldmismatch$flipped_snps)
)
load(paste0("/project/xinhe/xsun/multi_group_ctwas/11.multi_group_1008/post_process_rm_ld/ldmismatch_diagnosis_pipthres05_", trait, ".rdata"))
pip_05 <- data.frame(
"PIP Threshold" = "0.5",
"Number of Selected Regions" = length(selected_region_ids),
"Number of Problematic Genes" = length(problematic_genes),
"Number of Problematic Regions" = length(problematic_region_ids),
"Number of Problematic SNPs" = length(res_ldmismatch$problematic_snps),
"Number of Flipped SNPs" = length(res_ldmismatch$flipped_snps)
)
results_table <- rbind(pip_02, pip_05)
DT::datatable(results_table,caption = htmltools::tags$caption( style = 'caption-side: left; text-align: left; color:black; font-size:150% ;','LD mismatch diagnosis table for different gene cutoff'),options = list(pageLength = 10) )Comparing 2 LD mismatch fixing methods
load(paste0("/project/xinhe/xsun/multi_group_ctwas/11.multi_group_1008/post_process_rm_ld/ldmismatch_pipthres05_nold_",trait,".rdata"))
finemap_res_ldmm_nold <- res_ldmm_nold$finemap_res
load(paste0("/project/xinhe/xsun/multi_group_ctwas/11.multi_group_1008/post_process_rm_ld/ldmismatch_pipthres05_removesnp_",trait,".rdata"))
finemap_res_ldmm_removesnp <- res_ldmm_removesnp$finemap_res
finemap_res_ldmm_nold_problematic_gene <- finemap_res_ldmm_nold[finemap_res_ldmm_nold$region_id %in% problematic_region_ids & finemap_res_ldmm_nold$type != "SNP",]
finemap_res_ldmm_removesnp_problematic_gene <- finemap_res_ldmm_removesnp[finemap_res_ldmm_removesnp$region_id %in% problematic_region_ids & finemap_res_ldmm_removesnp$type != "SNP",]
merge_2method <- merge(finemap_res_ldmm_nold_problematic_gene,finemap_res_ldmm_removesnp_problematic_gene, by ="id")
p1 <- ggplot(data = merge_2method, aes(x= susie_pip.x, y= susie_pip.y)) +
geom_point() +
labs(x="PIP_noLD", y="PIP_removesnp") +
geom_abline(slope = 1, intercept = 0, col ="red") +
ggtitle("problematic regions only, genes only") +
theme_minimal()
finemap_res_rm_problematic_gene <- finemap_res_rm[finemap_res_rm$region_id %in% problematic_region_ids & finemap_res_rm$type != "SNP",]
merge_rm_ldmm_nold <- merge(finemap_res_rm_problematic_gene,finemap_res_ldmm_nold_problematic_gene, by ="id")
p2 <- ggplot(data = merge_rm_ldmm_nold, aes(x= susie_pip.x, y= susie_pip.y)) +
geom_point() +
labs(x="PIP_after_regionmerge", y="PIP_noLD") +
geom_abline(slope = 1, intercept = 0, col ="red") +
ggtitle("problematic regions only, genes only") +
theme_minimal()
merge_rm_ldmm_removesnp <- merge(finemap_res_rm_problematic_gene,finemap_res_ldmm_removesnp_problematic_gene, by ="id")
p3 <- ggplot(data = merge_rm_ldmm_removesnp, aes(x= susie_pip.x, y= susie_pip.y)) +
geom_point() +
labs(x="PIP_after_regionmerge", y="PIP_removesnp") +
geom_abline(slope = 1, intercept = 0, col ="red") +
ggtitle("problematic regions only, genes only") +
theme_minimal()
grid.arrange(p1,p2,p3, ncol = 3)
Comparing z-scores and susie_pip
finemap_res_origin <- ctwas_res_origin$finemap_res
finemap_res_origin_gene <- finemap_res_origin[finemap_res_origin$type != "SNP",]
p1 <- ggplot(data = finemap_res_origin_gene, aes(x= abs(z), y= susie_pip)) +
geom_point() +
ggtitle("Original ctwas results") +
theme_minimal()
finemap_res_rm_gene <- finemap_res_rm[finemap_res_rm$type != "SNP",]
p2 <- ggplot(data = finemap_res_rm_gene, aes(x= abs(z), y= susie_pip)) +
geom_point() +
ggtitle("After region merge") +
theme_minimal()
finemap_res_ldmm_nold_gene <- finemap_res_ldmm_nold[finemap_res_ldmm_nold$type !="SNP",]
p3 <- ggplot(data = finemap_res_ldmm_nold_gene, aes(x= abs(z), y= susie_pip)) +
geom_point() +
ggtitle("After LD mismatch fixed -- noLD") +
theme_minimal()
finemap_res_ldmm_removesnp_gene <- finemap_res_ldmm_removesnp[finemap_res_ldmm_removesnp$type !="SNP",]
p4 <- ggplot(data = finemap_res_ldmm_removesnp_gene, aes(x= abs(z), y= susie_pip)) +
geom_point() +
ggtitle("After LD mismatch fixed -- SNP removed") +
theme_minimal()
grid.arrange(p1,p2,p3,p4, ncol = 4)
IBD-ebi-a-GCST004131
trait <- "IBD-ebi-a-GCST004131"
results_dir_origin <- paste0("/project/xinhe/xsun/multi_group_ctwas/11.multi_group_1008/results/",trait,"/")
ctwas_res_origin <- readRDS(paste0(results_dir_origin,trait,".ctwas.res.RDS"))
finemap_res_origin <- ctwas_res_origin$finemap_resRegion merge
load(paste0("/project/xinhe/xsun/multi_group_ctwas/11.multi_group_1008/post_process_rm_ld/rm_",trait,".rdata"))
finemap_res_rm <- res_regionmerge$finemap_res
finemap_res_rm_boundary_genes <- finemap_res_rm[finemap_res_rm$id %in%selected_boundary_genes$id,]
finemap_res_rm_boundary_genes_pip <- finemap_res_rm_boundary_genes[,c("id","susie_pip","cs")]
finemap_res_origin_boundary_genes <- finemap_res_origin[finemap_res_origin$id %in%selected_boundary_genes$id,]
finemap_res_origin_boundary_genes_pip <- finemap_res_origin_boundary_genes[,c("id","susie_pip","cs")]
finemap_res_compare_regionmerge <- merge(finemap_res_origin_boundary_genes_pip,finemap_res_rm_boundary_genes_pip, by = "id")
colnames(finemap_res_compare_regionmerge) <- c("id","susie_pip_origin","cs_origin","susie_pip_reginmerge","cs_reginmerge")
DT::datatable(finemap_res_compare_regionmerge,caption = htmltools::tags$caption( style = 'caption-side: left; text-align: left; color:black; font-size:150% ;','Selected boundary genes (susie_pip > 0.5)'),options = list(pageLength = 10) )LD-mismatch
Diagnosis
load(paste0("/project/xinhe/xsun/multi_group_ctwas/11.multi_group_1008/post_process_rm_ld/ldmismatch_diagnosis_pipthres02_", trait, ".rdata"))
pip_02 <- data.frame(
"PIP Threshold" = "0.2",
"Number of Selected Regions" = length(selected_region_ids),
"Number of Problematic Genes" = length(problematic_genes),
"Number of Problematic Regions" = length(problematic_region_ids),
"Number of Problematic SNPs" = length(res_ldmismatch$problematic_snps),
"Number of Flipped SNPs" = length(res_ldmismatch$flipped_snps)
)
load(paste0("/project/xinhe/xsun/multi_group_ctwas/11.multi_group_1008/post_process_rm_ld/ldmismatch_diagnosis_pipthres05_", trait, ".rdata"))
pip_05 <- data.frame(
"PIP Threshold" = "0.5",
"Number of Selected Regions" = length(selected_region_ids),
"Number of Problematic Genes" = length(problematic_genes),
"Number of Problematic Regions" = length(problematic_region_ids),
"Number of Problematic SNPs" = length(res_ldmismatch$problematic_snps),
"Number of Flipped SNPs" = length(res_ldmismatch$flipped_snps)
)
results_table <- rbind(pip_02, pip_05)
DT::datatable(results_table,caption = htmltools::tags$caption( style = 'caption-side: left; text-align: left; color:black; font-size:150% ;','LD mismatch diagnosis table for different gene cutoff'),options = list(pageLength = 10) )Comparing 2 LD mismatch fixing methods
load(paste0("/project/xinhe/xsun/multi_group_ctwas/11.multi_group_1008/post_process_rm_ld/ldmismatch_pipthres05_nold_",trait,".rdata"))
finemap_res_ldmm_nold <- res_ldmm_nold$finemap_res
load(paste0("/project/xinhe/xsun/multi_group_ctwas/11.multi_group_1008/post_process_rm_ld/ldmismatch_pipthres05_removesnp_",trait,".rdata"))
finemap_res_ldmm_removesnp <- res_ldmm_removesnp$finemap_res
finemap_res_ldmm_nold_problematic_gene <- finemap_res_ldmm_nold[finemap_res_ldmm_nold$region_id %in% problematic_region_ids & finemap_res_ldmm_nold$type != "SNP",]
finemap_res_ldmm_removesnp_problematic_gene <- finemap_res_ldmm_removesnp[finemap_res_ldmm_removesnp$region_id %in% problematic_region_ids & finemap_res_ldmm_removesnp$type != "SNP",]
merge_2method <- merge(finemap_res_ldmm_nold_problematic_gene,finemap_res_ldmm_removesnp_problematic_gene, by ="id")
p1 <- ggplot(data = merge_2method, aes(x= susie_pip.x, y= susie_pip.y)) +
geom_point() +
labs(x="PIP_noLD", y="PIP_removesnp") +
geom_abline(slope = 1, intercept = 0, col ="red") +
ggtitle("problematic regions only, genes only") +
theme_minimal()
finemap_res_rm_problematic_gene <- finemap_res_rm[finemap_res_rm$region_id %in% problematic_region_ids & finemap_res_rm$type != "SNP",]
merge_rm_ldmm_nold <- merge(finemap_res_rm_problematic_gene,finemap_res_ldmm_nold_problematic_gene, by ="id")
p2 <- ggplot(data = merge_rm_ldmm_nold, aes(x= susie_pip.x, y= susie_pip.y)) +
geom_point() +
labs(x="PIP_after_regionmerge", y="PIP_noLD") +
geom_abline(slope = 1, intercept = 0, col ="red") +
ggtitle("problematic regions only, genes only") +
theme_minimal()
merge_rm_ldmm_removesnp <- merge(finemap_res_rm_problematic_gene,finemap_res_ldmm_removesnp_problematic_gene, by ="id")
p3 <- ggplot(data = merge_rm_ldmm_removesnp, aes(x= susie_pip.x, y= susie_pip.y)) +
geom_point() +
labs(x="PIP_after_regionmerge", y="PIP_removesnp") +
geom_abline(slope = 1, intercept = 0, col ="red") +
ggtitle("problematic regions only, genes only") +
theme_minimal()
grid.arrange(p1,p2,p3, ncol = 3)
finemap_res_ldmm_nold <- anno_finemap_res(finemap_res_ldmm_nold,
snp_map = updated_data_res_regionmerge[["updated_snp_map"]],
mapping_table = mapping_two,
add_gene_annot = TRUE,
map_by = "molecular_id",
drop_unmapped = TRUE,
add_position = TRUE,
use_gene_pos = "mid")2024-12-10 18:13:35 INFO::Annotating fine-mapping result ...
2024-12-10 18:13:36 INFO::Map molecular traits to genes
2024-12-10 18:13:39 INFO::Split PIPs for molecular traits mapped to multiple genes
2024-12-10 18:13:42 INFO::Add gene positions
2024-12-10 18:13:42 INFO::Add SNP positionsComparing z-scores and susie_pip
finemap_res_origin <- ctwas_res_origin$finemap_res
finemap_res_origin_gene <- finemap_res_origin[finemap_res_origin$type != "SNP",]
p1 <- ggplot(data = finemap_res_origin_gene, aes(x= abs(z), y= susie_pip)) +
geom_point() +
ggtitle("Original ctwas results") +
theme_minimal()
finemap_res_rm_gene <- finemap_res_rm[finemap_res_rm$type != "SNP",]
p2 <- ggplot(data = finemap_res_rm_gene, aes(x= abs(z), y= susie_pip)) +
geom_point() +
ggtitle("After region merge") +
theme_minimal()
finemap_res_ldmm_nold_gene <- finemap_res_ldmm_nold[finemap_res_ldmm_nold$type !="SNP",]
p3 <- ggplot(data = finemap_res_ldmm_nold_gene, aes(x= abs(z), y= susie_pip)) +
geom_point() +
ggtitle("After LD mismatch fixed -- noLD") +
theme_minimal()
finemap_res_ldmm_removesnp_gene <- finemap_res_ldmm_removesnp[finemap_res_ldmm_removesnp$type !="SNP",]
p4 <- ggplot(data = finemap_res_ldmm_removesnp_gene, aes(x= abs(z), y= susie_pip)) +
geom_point() +
ggtitle("After LD mismatch fixed -- SNP removed") +
theme_minimal()
grid.arrange(p1,p2,p3,p4, ncol = 4)
weights_origin <- readRDS(paste0("/project/xinhe/xsun/multi_group_ctwas/11.multi_group_1008/results/",trait,"/",trait,".preprocessed.weights.RDS"))
finemap_res_rm <- anno_finemap_res(finemap_res_rm,
snp_map = updated_data_res_regionmerge[["updated_snp_map"]],
mapping_table = mapping_two,
add_gene_annot = TRUE,
map_by = "molecular_id",
drop_unmapped = TRUE,
add_position = TRUE,
use_gene_pos = "mid")2024-12-10 18:14:06 INFO::Annotating fine-mapping result ...
2024-12-10 18:14:06 INFO::Map molecular traits to genes
2024-12-10 18:14:07 INFO::Split PIPs for molecular traits mapped to multiple genes
2024-12-10 18:14:13 INFO::Add gene positions
2024-12-10 18:14:13 INFO::Add SNP positionsprint("locus plot -- after region merge")[1] "locus plot -- after region merge"make_locusplot(finemap_res_rm,
region_id = "9_136047132_136605890",
ens_db = ens_db,
weights = weights_origin,
highlight_pip = 0.8,
filter_protein_coding_genes = TRUE,
filter_cs = TRUE,
color_pval_by = "cs",
color_pip_by = "cs")2024-12-10 18:14:17 INFO::Limit to protein coding genes
2024-12-10 18:14:17 INFO::focal id: ENSG00000187796.13|Whole_Blood_eQTL
2024-12-10 18:14:17 INFO::focal molecular trait: CARD9 Whole_Blood eQTL
2024-12-10 18:14:17 INFO::Range of locus: chr9:136050811-136864302
2024-12-10 18:14:18 INFO::focal molecular trait QTL positions: 136373081
2024-12-10 18:14:18 INFO::Limit PIPs to credible sets
finemap_res_ldmm_nold <- anno_finemap_res(finemap_res_ldmm_nold,
snp_map = updated_data_res_regionmerge[["updated_snp_map"]],
mapping_table = mapping_two,
add_gene_annot = TRUE,
map_by = "molecular_id",
drop_unmapped = TRUE,
add_position = TRUE,
use_gene_pos = "mid")2024-12-10 18:14:23 INFO::Annotating fine-mapping result ...
2024-12-10 18:14:23 INFO::'gene_name' is already available. Skip annotating genes.
2024-12-10 18:14:23 INFO::Add gene positions
2024-12-10 18:14:23 INFO::Add SNP positionsprint("locus plot -- LD mismatch: no LD")[1] "locus plot -- LD mismatch: no LD"make_locusplot(finemap_res_ldmm_nold,
region_id = "9_136047132_136605890",
ens_db = ens_db,
weights = weights_origin,
highlight_pip = 0.8,
filter_protein_coding_genes = TRUE,
filter_cs = TRUE,
color_pval_by = "cs",
color_pip_by = "cs")2024-12-10 18:14:27 INFO::Limit to protein coding genes
2024-12-10 18:14:27 INFO::focal id: ENSG00000187796.13|Whole_Blood_eQTL
2024-12-10 18:14:27 INFO::focal molecular trait: CARD9 Whole_Blood eQTL
2024-12-10 18:14:27 INFO::Range of locus: chr9:136050811-136864302
2024-12-10 18:14:28 INFO::focal molecular trait QTL positions:
2024-12-10 18:14:28 INFO::Limit PIPs to credible sets
finemap_res_ldmm_removesnp <- anno_finemap_res(finemap_res_ldmm_removesnp,
snp_map = updated_data_res_regionmerge[["updated_snp_map"]],
mapping_table = mapping_two,
add_gene_annot = TRUE,
map_by = "molecular_id",
drop_unmapped = TRUE,
add_position = TRUE,
use_gene_pos = "mid")2024-12-10 18:14:30 INFO::Annotating fine-mapping result ...
2024-12-10 18:14:30 INFO::Map molecular traits to genes
2024-12-10 18:14:34 INFO::Split PIPs for molecular traits mapped to multiple genes
2024-12-10 18:14:36 INFO::Add gene positions
2024-12-10 18:14:36 INFO::Add SNP positionsprint("locus plot -- LD mismatch: snp removed")[1] "locus plot -- LD mismatch: snp removed"make_locusplot(finemap_res_ldmm_removesnp,
region_id = "9_136047132_136605890",
ens_db = ens_db,
weights = weights_origin,
highlight_pip = 0.8,
filter_protein_coding_genes = TRUE,
filter_cs = TRUE,
color_pval_by = "cs",
color_pip_by = "cs")2024-12-10 18:14:43 INFO::Limit to protein coding genes
2024-12-10 18:14:43 INFO::focal id: ENSG00000107223|Cells_Cultured_fibroblasts_stQTL
2024-12-10 18:14:43 INFO::focal molecular trait: EDF1 Cells_Cultured_fibroblasts stQTL
2024-12-10 18:14:43 INFO::Range of locus: chr9:136050811-136864302
2024-12-10 18:14:44 INFO::focal molecular trait QTL positions:
2024-12-10 18:14:44 INFO::Limit PIPs to credible sets
SBP-ukb-a-360
trait <- "SBP-ukb-a-360"
results_dir_origin <- paste0("/project/xinhe/xsun/multi_group_ctwas/11.multi_group_1008/results/",trait,"/")
ctwas_res_origin <- readRDS(paste0(results_dir_origin,trait,".ctwas.res.RDS"))
finemap_res_origin <- ctwas_res_origin$finemap_resRegion merge
load(paste0("/project/xinhe/xsun/multi_group_ctwas/11.multi_group_1008/post_process_rm_ld/rm_",trait,".rdata"))
finemap_res_rm <- res_regionmerge$finemap_res
finemap_res_rm_boundary_genes <- finemap_res_rm[finemap_res_rm$id %in%selected_boundary_genes$id,]
finemap_res_rm_boundary_genes_pip <- finemap_res_rm_boundary_genes[,c("id","susie_pip","cs")]
finemap_res_origin_boundary_genes <- finemap_res_origin[finemap_res_origin$id %in%selected_boundary_genes$id,]
finemap_res_origin_boundary_genes_pip <- finemap_res_origin_boundary_genes[,c("id","susie_pip","cs")]
finemap_res_compare_regionmerge <- merge(finemap_res_origin_boundary_genes_pip,finemap_res_rm_boundary_genes_pip, by = "id")
colnames(finemap_res_compare_regionmerge) <- c("id","susie_pip_origin","cs_origin","susie_pip_reginmerge","cs_reginmerge")
DT::datatable(finemap_res_compare_regionmerge,caption = htmltools::tags$caption( style = 'caption-side: left; text-align: left; color:black; font-size:150% ;','Selected boundary genes (susie_pip > 0.5)'),options = list(pageLength = 10) )LD-mismatch
Diagnosis
load(paste0("/project/xinhe/xsun/multi_group_ctwas/11.multi_group_1008/post_process_rm_ld/ldmismatch_diagnosis_pipthres02_", trait, ".rdata"))
pip_02 <- data.frame(
"PIP Threshold" = "0.2",
"Number of Selected Regions" = length(selected_region_ids),
"Number of Problematic Genes" = length(problematic_genes),
"Number of Problematic Regions" = length(problematic_region_ids),
"Number of Problematic SNPs" = length(res_ldmismatch$problematic_snps),
"Number of Flipped SNPs" = length(res_ldmismatch$flipped_snps)
)
load(paste0("/project/xinhe/xsun/multi_group_ctwas/11.multi_group_1008/post_process_rm_ld/ldmismatch_diagnosis_pipthres05_", trait, ".rdata"))
pip_05 <- data.frame(
"PIP Threshold" = "0.5",
"Number of Selected Regions" = length(selected_region_ids),
"Number of Problematic Genes" = length(problematic_genes),
"Number of Problematic Regions" = length(problematic_region_ids),
"Number of Problematic SNPs" = length(res_ldmismatch$problematic_snps),
"Number of Flipped SNPs" = length(res_ldmismatch$flipped_snps)
)
results_table <- rbind(pip_02, pip_05)
DT::datatable(results_table,caption = htmltools::tags$caption( style = 'caption-side: left; text-align: left; color:black; font-size:150% ;','LD mismatch diagnosis table for different gene cutoff'),options = list(pageLength = 10) )Comparing 2 LD mismatch fixing methods
load(paste0("/project/xinhe/xsun/multi_group_ctwas/11.multi_group_1008/post_process_rm_ld/ldmismatch_pipthres05_nold_",trait,".rdata"))
finemap_res_ldmm_nold <- res_ldmm_nold$finemap_res
load(paste0("/project/xinhe/xsun/multi_group_ctwas/11.multi_group_1008/post_process_rm_ld/ldmismatch_pipthres05_removesnp_",trait,".rdata"))
finemap_res_ldmm_removesnp <- res_ldmm_removesnp$finemap_res
finemap_res_ldmm_nold_problematic_gene <- finemap_res_ldmm_nold[finemap_res_ldmm_nold$region_id %in% problematic_region_ids & finemap_res_ldmm_nold$type != "SNP",]
finemap_res_ldmm_removesnp_problematic_gene <- finemap_res_ldmm_removesnp[finemap_res_ldmm_removesnp$region_id %in% problematic_region_ids & finemap_res_ldmm_removesnp$type != "SNP",]
merge_2method <- merge(finemap_res_ldmm_nold_problematic_gene,finemap_res_ldmm_removesnp_problematic_gene, by ="id")
p1 <- ggplot(data = merge_2method, aes(x= susie_pip.x, y= susie_pip.y)) +
geom_point() +
labs(x="PIP_noLD", y="PIP_removesnp") +
geom_abline(slope = 1, intercept = 0, col ="red") +
ggtitle("problematic regions only, genes only") +
theme_minimal()
finemap_res_rm_problematic_gene <- finemap_res_rm[finemap_res_rm$region_id %in% problematic_region_ids & finemap_res_rm$type != "SNP",]
merge_rm_ldmm_nold <- merge(finemap_res_rm_problematic_gene,finemap_res_ldmm_nold_problematic_gene, by ="id")
p2 <- ggplot(data = merge_rm_ldmm_nold, aes(x= susie_pip.x, y= susie_pip.y)) +
geom_point() +
labs(x="PIP_after_regionmerge", y="PIP_noLD") +
geom_abline(slope = 1, intercept = 0, col ="red") +
ggtitle("problematic regions only, genes only") +
theme_minimal()
merge_rm_ldmm_removesnp <- merge(finemap_res_rm_problematic_gene,finemap_res_ldmm_removesnp_problematic_gene, by ="id")
p3 <- ggplot(data = merge_rm_ldmm_removesnp, aes(x= susie_pip.x, y= susie_pip.y)) +
geom_point() +
labs(x="PIP_after_regionmerge", y="PIP_removesnp") +
geom_abline(slope = 1, intercept = 0, col ="red") +
ggtitle("problematic regions only, genes only") +
theme_minimal()
grid.arrange(p1,p2,p3, ncol = 3)
Comparing z-scores and susie_pip
finemap_res_origin <- ctwas_res_origin$finemap_res
finemap_res_origin_gene <- finemap_res_origin[finemap_res_origin$type != "SNP",]
p1 <- ggplot(data = finemap_res_origin_gene, aes(x= abs(z), y= susie_pip)) +
geom_point() +
ggtitle("Original ctwas results") +
theme_minimal()
finemap_res_rm_gene <- finemap_res_rm[finemap_res_rm$type != "SNP",]
p2 <- ggplot(data = finemap_res_rm_gene, aes(x= abs(z), y= susie_pip)) +
geom_point() +
ggtitle("After region merge") +
theme_minimal()
finemap_res_ldmm_nold_gene <- finemap_res_ldmm_nold[finemap_res_ldmm_nold$type !="SNP",]
p3 <- ggplot(data = finemap_res_ldmm_nold_gene, aes(x= abs(z), y= susie_pip)) +
geom_point() +
ggtitle("After LD mismatch fixed -- noLD") +
theme_minimal()
finemap_res_ldmm_removesnp_gene <- finemap_res_ldmm_removesnp[finemap_res_ldmm_removesnp$type !="SNP",]
p4 <- ggplot(data = finemap_res_ldmm_removesnp_gene, aes(x= abs(z), y= susie_pip)) +
geom_point() +
ggtitle("After LD mismatch fixed -- SNP removed") +
theme_minimal()
grid.arrange(p1,p2,p3,p4, ncol = 4)
SCZ-ieu-b-5102
trait <- "SCZ-ieu-b-5102"
results_dir_origin <- paste0("/project/xinhe/xsun/multi_group_ctwas/11.multi_group_1008/results/",trait,"/")
ctwas_res_origin <- readRDS(paste0(results_dir_origin,trait,".ctwas.res.RDS"))
finemap_res_origin <- ctwas_res_origin$finemap_resRegion merge
load(paste0("/project/xinhe/xsun/multi_group_ctwas/11.multi_group_1008/post_process_rm_ld/rm_",trait,".rdata"))
finemap_res_rm <- res_regionmerge$finemap_res
finemap_res_rm_boundary_genes <- finemap_res_rm[finemap_res_rm$id %in%selected_boundary_genes$id,]
finemap_res_rm_boundary_genes_pip <- finemap_res_rm_boundary_genes[,c("id","susie_pip","cs")]
finemap_res_origin_boundary_genes <- finemap_res_origin[finemap_res_origin$id %in%selected_boundary_genes$id,]
finemap_res_origin_boundary_genes_pip <- finemap_res_origin_boundary_genes[,c("id","susie_pip","cs")]
finemap_res_compare_regionmerge <- merge(finemap_res_origin_boundary_genes_pip,finemap_res_rm_boundary_genes_pip, by = "id")
colnames(finemap_res_compare_regionmerge) <- c("id","susie_pip_origin","cs_origin","susie_pip_reginmerge","cs_reginmerge")
DT::datatable(finemap_res_compare_regionmerge,caption = htmltools::tags$caption( style = 'caption-side: left; text-align: left; color:black; font-size:150% ;','Selected boundary genes (susie_pip > 0.5)'),options = list(pageLength = 10) )LD-mismatch
Diagnosis
load(paste0("/project/xinhe/xsun/multi_group_ctwas/11.multi_group_1008/post_process_rm_ld/ldmismatch_diagnosis_pipthres02_", trait, ".rdata"))
pip_02 <- data.frame(
"PIP Threshold" = "0.2",
"Number of Selected Regions" = length(selected_region_ids),
"Number of Problematic Genes" = length(problematic_genes),
"Number of Problematic Regions" = length(problematic_region_ids),
"Number of Problematic SNPs" = length(res_ldmismatch$problematic_snps),
"Number of Flipped SNPs" = length(res_ldmismatch$flipped_snps)
)
load(paste0("/project/xinhe/xsun/multi_group_ctwas/11.multi_group_1008/post_process_rm_ld/ldmismatch_diagnosis_pipthres05_", trait, ".rdata"))
pip_05 <- data.frame(
"PIP Threshold" = "0.5",
"Number of Selected Regions" = length(selected_region_ids),
"Number of Problematic Genes" = length(problematic_genes),
"Number of Problematic Regions" = length(problematic_region_ids),
"Number of Problematic SNPs" = length(res_ldmismatch$problematic_snps),
"Number of Flipped SNPs" = length(res_ldmismatch$flipped_snps)
)
results_table <- rbind(pip_02, pip_05)
DT::datatable(results_table,caption = htmltools::tags$caption( style = 'caption-side: left; text-align: left; color:black; font-size:150% ;','LD mismatch diagnosis table for different gene cutoff'),options = list(pageLength = 10) )Comparing 2 LD mismatch fixing methods
load(paste0("/project/xinhe/xsun/multi_group_ctwas/11.multi_group_1008/post_process_rm_ld/ldmismatch_pipthres05_nold_",trait,".rdata"))
finemap_res_ldmm_nold <- res_ldmm_nold$finemap_res
load(paste0("/project/xinhe/xsun/multi_group_ctwas/11.multi_group_1008/post_process_rm_ld/ldmismatch_pipthres05_removesnp_",trait,".rdata"))
finemap_res_ldmm_removesnp <- res_ldmm_removesnp$finemap_res
finemap_res_ldmm_nold_problematic_gene <- finemap_res_ldmm_nold[finemap_res_ldmm_nold$region_id %in% problematic_region_ids & finemap_res_ldmm_nold$type != "SNP",]
finemap_res_ldmm_removesnp_problematic_gene <- finemap_res_ldmm_removesnp[finemap_res_ldmm_removesnp$region_id %in% problematic_region_ids & finemap_res_ldmm_removesnp$type != "SNP",]
merge_2method <- merge(finemap_res_ldmm_nold_problematic_gene,finemap_res_ldmm_removesnp_problematic_gene, by ="id")
p1 <- ggplot(data = merge_2method, aes(x= susie_pip.x, y= susie_pip.y)) +
geom_point() +
labs(x="PIP_noLD", y="PIP_removesnp") +
geom_abline(slope = 1, intercept = 0, col ="red") +
ggtitle("problematic regions only, genes only") +
theme_minimal()
finemap_res_rm_problematic_gene <- finemap_res_rm[finemap_res_rm$region_id %in% problematic_region_ids & finemap_res_rm$type != "SNP",]
merge_rm_ldmm_nold <- merge(finemap_res_rm_problematic_gene,finemap_res_ldmm_nold_problematic_gene, by ="id")
p2 <- ggplot(data = merge_rm_ldmm_nold, aes(x= susie_pip.x, y= susie_pip.y)) +
geom_point() +
labs(x="PIP_after_regionmerge", y="PIP_noLD") +
geom_abline(slope = 1, intercept = 0, col ="red") +
ggtitle("problematic regions only, genes only") +
theme_minimal()
merge_rm_ldmm_removesnp <- merge(finemap_res_rm_problematic_gene,finemap_res_ldmm_removesnp_problematic_gene, by ="id")
p3 <- ggplot(data = merge_rm_ldmm_removesnp, aes(x= susie_pip.x, y= susie_pip.y)) +
geom_point() +
labs(x="PIP_after_regionmerge", y="PIP_removesnp") +
geom_abline(slope = 1, intercept = 0, col ="red") +
ggtitle("problematic regions only, genes only") +
theme_minimal()
grid.arrange(p1,p2,p3, ncol = 3)
| Version | Author | Date |
|---|---|---|
| 8578389 | XSun | 2024-12-10 |
Comparing z-scores and susie_pip
finemap_res_origin <- ctwas_res_origin$finemap_res
finemap_res_origin_gene <- finemap_res_origin[finemap_res_origin$type != "SNP",]
p1 <- ggplot(data = finemap_res_origin_gene, aes(x= abs(z), y= susie_pip)) +
geom_point() +
ggtitle("Original ctwas results") +
theme_minimal()
finemap_res_rm_gene <- finemap_res_rm[finemap_res_rm$type != "SNP",]
p2 <- ggplot(data = finemap_res_rm_gene, aes(x= abs(z), y= susie_pip)) +
geom_point() +
ggtitle("After region merge") +
theme_minimal()
finemap_res_ldmm_nold_gene <- finemap_res_ldmm_nold[finemap_res_ldmm_nold$type !="SNP",]
p3 <- ggplot(data = finemap_res_ldmm_nold_gene, aes(x= abs(z), y= susie_pip)) +
geom_point() +
ggtitle("After LD mismatch fixed -- noLD") +
theme_minimal()
finemap_res_ldmm_removesnp_gene <- finemap_res_ldmm_removesnp[finemap_res_ldmm_removesnp$type !="SNP",]
p4 <- ggplot(data = finemap_res_ldmm_removesnp_gene, aes(x= abs(z), y= susie_pip)) +
geom_point() +
ggtitle("After LD mismatch fixed -- SNP removed") +
theme_minimal()
grid.arrange(p1,p2,p3,p4, ncol = 4)
WBC-ieu-b-30
trait <- "WBC-ieu-b-30"
results_dir_origin <- paste0("/project/xinhe/xsun/multi_group_ctwas/11.multi_group_1008/results/",trait,"/")
ctwas_res_origin <- readRDS(paste0(results_dir_origin,trait,".ctwas.res.RDS"))
finemap_res_origin <- ctwas_res_origin$finemap_resRegion merge
load(paste0("/project/xinhe/xsun/multi_group_ctwas/11.multi_group_1008/post_process_rm_ld/rm_",trait,".rdata"))
finemap_res_rm <- res_regionmerge$finemap_res
finemap_res_rm_boundary_genes <- finemap_res_rm[finemap_res_rm$id %in%selected_boundary_genes$id,]
finemap_res_rm_boundary_genes_pip <- finemap_res_rm_boundary_genes[,c("id","susie_pip","cs")]
finemap_res_origin_boundary_genes <- finemap_res_origin[finemap_res_origin$id %in%selected_boundary_genes$id,]
finemap_res_origin_boundary_genes_pip <- finemap_res_origin_boundary_genes[,c("id","susie_pip","cs")]
finemap_res_compare_regionmerge <- merge(finemap_res_origin_boundary_genes_pip,finemap_res_rm_boundary_genes_pip, by = "id")
colnames(finemap_res_compare_regionmerge) <- c("id","susie_pip_origin","cs_origin","susie_pip_reginmerge","cs_reginmerge")
DT::datatable(finemap_res_compare_regionmerge,caption = htmltools::tags$caption( style = 'caption-side: left; text-align: left; color:black; font-size:150% ;','Selected boundary genes (susie_pip > 0.5)'),options = list(pageLength = 10) )LD-mismatch
Diagnosis
load(paste0("/project/xinhe/xsun/multi_group_ctwas/11.multi_group_1008/post_process_rm_ld/ldmismatch_diagnosis_pipthres02_", trait, ".rdata"))
pip_02 <- data.frame(
"PIP Threshold" = "0.2",
"Number of Selected Regions" = length(selected_region_ids),
"Number of Problematic Genes" = length(problematic_genes),
"Number of Problematic Regions" = length(problematic_region_ids),
"Number of Problematic SNPs" = length(res_ldmismatch$problematic_snps),
"Number of Flipped SNPs" = length(res_ldmismatch$flipped_snps)
)
load(paste0("/project/xinhe/xsun/multi_group_ctwas/11.multi_group_1008/post_process_rm_ld/ldmismatch_diagnosis_pipthres05_", trait, ".rdata"))
pip_05 <- data.frame(
"PIP Threshold" = "0.5",
"Number of Selected Regions" = length(selected_region_ids),
"Number of Problematic Genes" = length(problematic_genes),
"Number of Problematic Regions" = length(problematic_region_ids),
"Number of Problematic SNPs" = length(res_ldmismatch$problematic_snps),
"Number of Flipped SNPs" = length(res_ldmismatch$flipped_snps)
)
results_table <- rbind(pip_02, pip_05)
DT::datatable(results_table,caption = htmltools::tags$caption( style = 'caption-side: left; text-align: left; color:black; font-size:150% ;','LD mismatch diagnosis table for different gene cutoff'),options = list(pageLength = 10) )Comparing 2 LD mismatch fixing methods
load(paste0("/project/xinhe/xsun/multi_group_ctwas/11.multi_group_1008/post_process_rm_ld/ldmismatch_pipthres05_nold_",trait,".rdata"))
finemap_res_ldmm_nold <- res_ldmm_nold$finemap_res
load(paste0("/project/xinhe/xsun/multi_group_ctwas/11.multi_group_1008/post_process_rm_ld/ldmismatch_pipthres05_removesnp_",trait,".rdata"))
finemap_res_ldmm_removesnp <- res_ldmm_removesnp$finemap_res
finemap_res_ldmm_nold_problematic_gene <- finemap_res_ldmm_nold[finemap_res_ldmm_nold$region_id %in% problematic_region_ids & finemap_res_ldmm_nold$type != "SNP",]
finemap_res_ldmm_removesnp_problematic_gene <- finemap_res_ldmm_removesnp[finemap_res_ldmm_removesnp$region_id %in% problematic_region_ids & finemap_res_ldmm_removesnp$type != "SNP",]
merge_2method <- merge(finemap_res_ldmm_nold_problematic_gene,finemap_res_ldmm_removesnp_problematic_gene, by ="id")
p1 <- ggplot(data = merge_2method, aes(x= susie_pip.x, y= susie_pip.y)) +
geom_point() +
labs(x="PIP_noLD", y="PIP_removesnp") +
geom_abline(slope = 1, intercept = 0, col ="red") +
ggtitle("problematic regions only, genes only") +
theme_minimal()
finemap_res_rm_problematic_gene <- finemap_res_rm[finemap_res_rm$region_id %in% problematic_region_ids & finemap_res_rm$type != "SNP",]
merge_rm_ldmm_nold <- merge(finemap_res_rm_problematic_gene,finemap_res_ldmm_nold_problematic_gene, by ="id")
p2 <- ggplot(data = merge_rm_ldmm_nold, aes(x= susie_pip.x, y= susie_pip.y)) +
geom_point() +
labs(x="PIP_after_regionmerge", y="PIP_noLD") +
geom_abline(slope = 1, intercept = 0, col ="red") +
ggtitle("problematic regions only, genes only") +
theme_minimal()
merge_rm_ldmm_removesnp <- merge(finemap_res_rm_problematic_gene,finemap_res_ldmm_removesnp_problematic_gene, by ="id")
p3 <- ggplot(data = merge_rm_ldmm_removesnp, aes(x= susie_pip.x, y= susie_pip.y)) +
geom_point() +
labs(x="PIP_after_regionmerge", y="PIP_removesnp") +
geom_abline(slope = 1, intercept = 0, col ="red") +
ggtitle("problematic regions only, genes only") +
theme_minimal()
grid.arrange(p1,p2,p3, ncol = 3)
| Version | Author | Date |
|---|---|---|
| 8578389 | XSun | 2024-12-10 |
Comparing z-scores and susie_pip
finemap_res_origin <- ctwas_res_origin$finemap_res
finemap_res_origin_gene <- finemap_res_origin[finemap_res_origin$type != "SNP",]
p1 <- ggplot(data = finemap_res_origin_gene, aes(x= abs(z), y= susie_pip)) +
geom_point() +
ggtitle("Original ctwas results") +
theme_minimal()
finemap_res_rm_gene <- finemap_res_rm[finemap_res_rm$type != "SNP",]
p2 <- ggplot(data = finemap_res_rm_gene, aes(x= abs(z), y= susie_pip)) +
geom_point() +
ggtitle("After region merge") +
theme_minimal()
finemap_res_ldmm_nold_gene <- finemap_res_ldmm_nold[finemap_res_ldmm_nold$type !="SNP",]
p3 <- ggplot(data = finemap_res_ldmm_nold_gene, aes(x= abs(z), y= susie_pip)) +
geom_point() +
ggtitle("After LD mismatch fixed -- noLD") +
theme_minimal()
finemap_res_ldmm_removesnp_gene <- finemap_res_ldmm_removesnp[finemap_res_ldmm_removesnp$type !="SNP",]
p4 <- ggplot(data = finemap_res_ldmm_removesnp_gene, aes(x= abs(z), y= susie_pip)) +
geom_point() +
ggtitle("After LD mismatch fixed -- SNP removed") +
theme_minimal()
grid.arrange(p1,p2,p3,p4, ncol = 4)
sessionInfo()R version 4.2.0 (2022-04-22)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: CentOS Linux 8
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] dplyr_1.1.2 gridExtra_2.3
[3] ggplot2_3.4.2 EnsDb.Hsapiens.v86_2.99.0
[5] ensembldb_2.22.0 AnnotationFilter_1.22.0
[7] GenomicFeatures_1.50.4 AnnotationDbi_1.60.2
[9] Biobase_2.58.0 GenomicRanges_1.50.2
[11] GenomeInfoDb_1.34.9 IRanges_2.32.0
[13] S4Vectors_0.36.2 BiocGenerics_0.44.0
[15] ctwas_0.4.20
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] fs_1.5.2 rstudioapi_0.14
[9] farver_2.1.0 ggrepel_0.9.3
[11] DT_0.22 bit64_4.0.5
[13] fansi_1.0.3 xml2_1.3.3
[15] codetools_0.2-18 logging_0.10-108
[17] cachem_1.0.6 knitr_1.42
[19] jsonlite_1.8.7 workflowr_1.7.1
[21] Rsamtools_2.14.0 dbplyr_2.3.2
[23] png_0.1-7 readr_2.1.4
[25] compiler_4.2.0 httr_1.4.7
[27] Matrix_1.6-1.1 fastmap_1.1.0
[29] lazyeval_0.2.2 cli_3.6.2
[31] later_1.3.0 htmltools_0.5.7
[33] prettyunits_1.1.1 tools_4.2.0
[35] gtable_0.3.0 glue_1.6.2
[37] GenomeInfoDbData_1.2.9 rappdirs_0.3.3
[39] Rcpp_1.0.11 jquerylib_0.1.4
[41] vctrs_0.6.1 Biostrings_2.66.0
[43] rtracklayer_1.58.0 crosstalk_1.2.0
[45] xfun_0.38 stringr_1.5.0
[47] lifecycle_1.0.4 irlba_2.3.5
[49] restfulr_0.0.15 XML_3.99-0.9
[51] zlibbioc_1.44.0 scales_1.2.0
[53] gggrid_0.2-0 hms_1.1.3
[55] promises_1.2.0.1 MatrixGenerics_1.10.0
[57] ProtGenerics_1.30.0 parallel_4.2.0
[59] SummarizedExperiment_1.28.0 LDlinkR_1.3.0
[61] yaml_2.3.5 curl_4.3.2
[63] memoise_2.0.1 sass_0.4.1
[65] biomaRt_2.54.1 stringi_1.7.6
[67] RSQLite_2.3.1 highr_0.9
[69] BiocIO_1.8.0 filelock_1.0.2
[71] BiocParallel_1.32.6 rlang_1.1.2
[73] pkgconfig_2.0.3 matrixStats_1.2.0
[75] bitops_1.0-7 evaluate_0.15
[77] lattice_0.20-45 purrr_1.0.1
[79] labeling_0.4.2 GenomicAlignments_1.34.1
[81] htmlwidgets_1.6.2 cowplot_1.1.1
[83] bit_4.0.4 tidyselect_1.2.0
[85] magrittr_2.0.3 R6_2.5.1
[87] generics_0.1.3 DelayedArray_0.24.0
[89] DBI_1.1.2 pgenlibr_0.3.6
[91] pillar_1.9.0 whisker_0.4
[93] withr_2.5.0 KEGGREST_1.38.0
[95] RCurl_1.98-1.12 mixsqp_0.3-48
[97] tibble_3.2.1 crayon_1.5.1
[99] utf8_1.2.2 BiocFileCache_2.6.1
[101] plotly_4.10.0 tzdb_0.3.0
[103] rmarkdown_2.21 progress_1.2.2
[105] grid_4.2.0 data.table_1.14.4
[107] blob_1.2.3 git2r_0.30.1
[109] digest_0.6.29 tidyr_1.3.0
[111] httpuv_1.6.5 munsell_0.5.0
[113] viridisLite_0.4.0 bslib_0.3.1