Compare ld-mismatch setting
XSun
2025-04-07
Last updated: 2025-04-25
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Introduction
We compare different ld - mismatch settings
- remove the ld-mismatched snp, recompute the z-scores and re-do the finemapping
- re-finemap without LD
library(ggplot2)
library(ctwas)
library(dplyr)
Attaching package: 'dplyr'The following objects are masked from 'package:stats':
filter, lagThe following objects are masked from 'package:base':
intersect, setdiff, setequal, unionfolder_results_susieST <- "/project/xinhe/xsun/multi_group_ctwas/15.susie_weights/snakemake_outputs/"
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)IBD-ebi-a-GCST004131
Setting: shared_all, thin = 1, L = 5, susieST
trait <- "IBD-ebi-a-GCST004131"
thin <- 1
var_struc <- "shared_all"
st <- "with_susieST"
L <- 5
ctwas_res_origin <- readRDS(paste0(folder_results_susieST,trait,"/",trait,".",st,".thin",thin,".",var_struc,".L",L,".finemap_regions_res.RDS"))
finemap_res_origin <- ctwas_res_origin$finemap_res
finemap_res_gene_origin <- finemap_res_origin[finemap_res_origin$type != "SNP",]
ctwas_res_regionmerge <- readRDS(paste0(folder_results_susieST,trait,"/",trait,".",st,".thin",thin,".",var_struc,".L",L,".regionmerge_finemap_regions_res.RDS"))
finemap_res_regionmerge <- ctwas_res_regionmerge$finemap_res
finemap_res_gene_regionmerge <- finemap_res_regionmerge[finemap_res_regionmerge$type != "SNP",]
df_summary <- c()
susie_alpha_res_regionmerge <- ctwas_res_regionmerge$susie_alpha_res
susie_alpha_res_regionmerge <- anno_susie_alpha_res(susie_alpha_res_regionmerge,
mapping_table = mapping_two,
map_by = "molecular_id",
drop_unmapped = TRUE)2025-04-25 11:58:04 INFO::Annotating susie alpha result ...
2025-04-25 11:58:06 INFO::Map molecular traits to genes
2025-04-25 11:58:10 INFO::Split PIPs for molecular traits mapped to multiple genescombined_pip_by_group_regionmerge <- combine_gene_pips(susie_alpha_res_regionmerge,
group_by = "gene_name",
by = "group",
method = "combine_cs",
filter_cs = TRUE,
include_cs_id = F)Thresholds for ld mismatch diagnosis: nonSNP PIP = 0.5, SNP p_diff = 5e-8
threshold_nonSNP_PIPs <- 0.5
threshold_SNP_p <- "5e-08"file_res_ldmm <- paste0(folder_results_susieST,trait,"/",trait,".",st,".thin",thin,".",var_struc,".L",L,".ldmismatch_res_genepip",threshold_nonSNP_PIPs,"_snpp_",threshold_SNP_p,".RDS")
res_ldmm <- readRDS(file_res_ldmm)
n_problematic_snp <- length(res_ldmm$problematic_snps)
file_problematic_genes <- paste0(folder_results_susieST,trait,"/",trait,".",st,".thin",thin,".",var_struc,".L",L,".ldmismatch_problematic_genes_genepip",threshold_nonSNP_PIPs,"_snpp_",threshold_SNP_p,".RDS")
if(file.exists(file_problematic_genes)) {
problematic_genes <- readRDS(paste0(folder_results_susieST,trait,"/",trait,".",st,".thin",thin,".",var_struc,".L",L,".ldmismatch_problematic_genes_genepip",threshold_nonSNP_PIPs,"_snpp_",threshold_SNP_p,".RDS"))
n_problematic_gene <- length(problematic_genes)
finemap_res_gene_origin$highlight <- ifelse(finemap_res_gene_origin$id %in% problematic_genes, "problematic genes", "good genes")
p1 <- ggplot(data = finemap_res_gene_origin, aes(x= abs(z), y= susie_pip, color = highlight)) +
geom_point() +
scale_color_manual(values = c("problematic genes" = "red", "good genes" = "black")) +
ggtitle("Original ctwas results") +
theme_minimal()
finemap_res_gene_regionmerge$highlight <- ifelse(finemap_res_gene_regionmerge$id %in% problematic_genes, "problematic genes", "good genes")
p2 <- ggplot(data = finemap_res_gene_regionmerge, aes(x= abs(z), y= susie_pip, color = highlight)) +
geom_point() +
scale_color_manual(values = c("problematic genes" = "red", "good genes" = "black")) +
ggtitle("After region merge") +
theme_minimal()
ctwas_res_ldmismatch <- readRDS(paste0(folder_results_susieST,trait,"/",trait,".",st,".thin",thin,".",var_struc,".L",L,".ldmismatch_finemap_regions_res_genepip",threshold_nonSNP_PIPs,"_snpp_",threshold_SNP_p,".RDS"))
finemap_res_ldmismatch <- ctwas_res_ldmismatch$finemap_res
finemap_res_gene_ldmismatch <- finemap_res_ldmismatch[finemap_res_ldmismatch$type != "SNP",]
finemap_res_gene_ldmismatch$highlight <- ifelse(finemap_res_gene_ldmismatch$id %in% problematic_genes, "problematic genes", "good genes")
p3 <- ggplot(data = finemap_res_gene_ldmismatch, aes(x= abs(z), y= susie_pip, color = highlight)) +
geom_point() +
scale_color_manual(values = c("problematic genes" = "red", "good genes" = "black")) +
ggtitle("LD mismatch fixed -- remove the snp") +
theme_minimal()
ctwas_res_ldmismatchnoLD <- readRDS(paste0(folder_results_susieST,trait,"/",trait,".",st,".thin",thin,".",var_struc,".L",L,".ldmismatch_noLD_finemap_regions_res_genepip",threshold_nonSNP_PIPs,"_snpp_",threshold_SNP_p,".RDS"))
finemap_res_ldmismatchnoLD <- ctwas_res_ldmismatchnoLD$finemap_res
finemap_res_gene_ldmismatchnoLD <- finemap_res_ldmismatchnoLD[finemap_res_ldmismatchnoLD$type != "SNP",]
finemap_res_gene_ldmismatchnoLD$highlight <- ifelse(finemap_res_gene_ldmismatchnoLD$id %in% problematic_genes, "problematic genes", "good genes")
p4 <- ggplot(data = finemap_res_gene_ldmismatchnoLD, aes(x= abs(z), y= susie_pip, color = highlight)) +
geom_point() +
scale_color_manual(values = c("problematic genes" = "red", "good genes" = "black")) +
ggtitle("LD mismatch fixed -- remove the snp") +
theme_minimal()
grid::grid.newpage()
gridExtra::grid.arrange(p1,p2,p3,p4,
ncol = 4,
top = paste0(trait,"-nonSNP_PIPs:",threshold_nonSNP_PIPs,"-SNP_p:",threshold_SNP_p))
susie_alpha_res_ldmismatch <- ctwas_res_ldmismatch$susie_alpha_res
susie_alpha_res_ldmismatch <- anno_susie_alpha_res(susie_alpha_res_ldmismatch,
mapping_table = mapping_two,
map_by = "molecular_id",
drop_unmapped = TRUE)
combined_pip_by_group_ldmismatch <- combine_gene_pips(susie_alpha_res_ldmismatch,
group_by = "gene_name",
by = "group",
method = "combine_cs",
filter_cs = TRUE,
include_cs_id = F)
susie_alpha_res_ldmismatchnoLD <- ctwas_res_ldmismatchnoLD$susie_alpha_res
susie_alpha_res_ldmismatchnoLD <- anno_susie_alpha_res(susie_alpha_res_ldmismatchnoLD,
mapping_table = mapping_two,
map_by = "molecular_id",
drop_unmapped = TRUE)
combined_pip_by_group_ldmismatchnoLD <- combine_gene_pips(susie_alpha_res_ldmismatchnoLD,
group_by = "gene_name",
by = "group",
method = "combine_cs",
filter_cs = TRUE,
include_cs_id = F)
combined_pip_by_group_ldmismatch <- combined_pip_by_group_ldmismatch[,c("gene_name","combined_pip")]
combined_pip_by_group_regionmerge <- combined_pip_by_group_regionmerge[,c("gene_name","combined_pip")]
combined_pip_by_group_ldmismatchnoLD <- combined_pip_by_group_ldmismatchnoLD[,c("gene_name","combined_pip")]
merged <- merge(combined_pip_by_group_regionmerge,combined_pip_by_group_ldmismatch, by = "gene_name", all.x = T)
merged <- merge(merged,combined_pip_by_group_ldmismatchnoLD, by = "gene_name", all.x = T)
colnames(merged) <- c("gene_name","combined_pip_regionmerge", "combined_pip_ldmismatch_remove", "combined_pip_ldmismatchnoLD")
genes_diff <- merged %>%
rowwise() %>%
dplyr::filter(n_distinct(c(combined_pip_regionmerge, combined_pip_ldmismatch_remove, combined_pip_ldmismatchnoLD)) > 1)
known <- readRDS(paste0("/project/xinhe/xsun/multi_group_ctwas/data/silverstandard/known_annotations_IBD.RDS"))
bystander <- readRDS(paste0("/project/xinhe/xsun/multi_group_ctwas/data/silverstandard/bystanders_IBD.RDS"))
genes_diff <- genes_diff %>%
mutate(
is_silver = gene_name %in% known,
is_bystander = gene_name %in% bystander
)
}else {
n_problematic_gene <- 0
}
2025-04-25 12:01:03 INFO::Annotating susie alpha result ...
2025-04-25 12:01:04 INFO::Map molecular traits to genes
2025-04-25 12:01:08 INFO::Split PIPs for molecular traits mapped to multiple genes
2025-04-25 12:02:13 INFO::Annotating susie alpha result ...
2025-04-25 12:02:14 INFO::Map molecular traits to genes
2025-04-25 12:02:16 INFO::Split PIPs for molecular traits mapped to multiple genes# setting <- paste0("thin",thin,".",var_struc,".L",L,".genepip",threshold_nonSNP_PIPs,"_snpp_",threshold_SNP_p)
setting <- paste0("genepip",threshold_nonSNP_PIPs,"_snpp_",threshold_SNP_p)
tmp <- c(setting, n_problematic_snp, n_problematic_gene)
df_summary <- rbind(df_summary,tmp)
DT::datatable(genes_diff,caption = htmltools::tags$caption( style = 'caption-side: left; text-align: left; color:black; font-size:150% ;','Genes -- PIP changed'),options = list(pageLength = 10) )Thresholds for ld mismatch diagnosis: nonSNP PIP = 0.5, SNP p_diff = 5e-6
threshold_nonSNP_PIPs <- 0.5
threshold_SNP_p <- "5e-06"file_res_ldmm <- paste0(folder_results_susieST,trait,"/",trait,".",st,".thin",thin,".",var_struc,".L",L,".ldmismatch_res_genepip",threshold_nonSNP_PIPs,"_snpp_",threshold_SNP_p,".RDS")
res_ldmm <- readRDS(file_res_ldmm)
n_problematic_snp <- length(res_ldmm$problematic_snps)
file_problematic_genes <- paste0(folder_results_susieST,trait,"/",trait,".",st,".thin",thin,".",var_struc,".L",L,".ldmismatch_problematic_genes_genepip",threshold_nonSNP_PIPs,"_snpp_",threshold_SNP_p,".RDS")
if(file.exists(file_problematic_genes)) {
problematic_genes <- readRDS(paste0(folder_results_susieST,trait,"/",trait,".",st,".thin",thin,".",var_struc,".L",L,".ldmismatch_problematic_genes_genepip",threshold_nonSNP_PIPs,"_snpp_",threshold_SNP_p,".RDS"))
n_problematic_gene <- length(problematic_genes)
finemap_res_gene_origin$highlight <- ifelse(finemap_res_gene_origin$id %in% problematic_genes, "problematic genes", "good genes")
p1 <- ggplot(data = finemap_res_gene_origin, aes(x= abs(z), y= susie_pip, color = highlight)) +
geom_point() +
scale_color_manual(values = c("problematic genes" = "red", "good genes" = "black")) +
ggtitle("Original ctwas results") +
theme_minimal()
finemap_res_gene_regionmerge$highlight <- ifelse(finemap_res_gene_regionmerge$id %in% problematic_genes, "problematic genes", "good genes")
p2 <- ggplot(data = finemap_res_gene_regionmerge, aes(x= abs(z), y= susie_pip, color = highlight)) +
geom_point() +
scale_color_manual(values = c("problematic genes" = "red", "good genes" = "black")) +
ggtitle("After region merge") +
theme_minimal()
ctwas_res_ldmismatch <- readRDS(paste0(folder_results_susieST,trait,"/",trait,".",st,".thin",thin,".",var_struc,".L",L,".ldmismatch_finemap_regions_res_genepip",threshold_nonSNP_PIPs,"_snpp_",threshold_SNP_p,".RDS"))
finemap_res_ldmismatch <- ctwas_res_ldmismatch$finemap_res
finemap_res_gene_ldmismatch <- finemap_res_ldmismatch[finemap_res_ldmismatch$type != "SNP",]
finemap_res_gene_ldmismatch$highlight <- ifelse(finemap_res_gene_ldmismatch$id %in% problematic_genes, "problematic genes", "good genes")
p3 <- ggplot(data = finemap_res_gene_ldmismatch, aes(x= abs(z), y= susie_pip, color = highlight)) +
geom_point() +
scale_color_manual(values = c("problematic genes" = "red", "good genes" = "black")) +
ggtitle("LD mismatch fixed -- remove the snp") +
theme_minimal()
ctwas_res_ldmismatchnoLD <- readRDS(paste0(folder_results_susieST,trait,"/",trait,".",st,".thin",thin,".",var_struc,".L",L,".ldmismatch_noLD_finemap_regions_res_genepip",threshold_nonSNP_PIPs,"_snpp_",threshold_SNP_p,".RDS"))
finemap_res_ldmismatchnoLD <- ctwas_res_ldmismatchnoLD$finemap_res
finemap_res_gene_ldmismatchnoLD <- finemap_res_ldmismatchnoLD[finemap_res_ldmismatchnoLD$type != "SNP",]
finemap_res_gene_ldmismatchnoLD$highlight <- ifelse(finemap_res_gene_ldmismatchnoLD$id %in% problematic_genes, "problematic genes", "good genes")
p4 <- ggplot(data = finemap_res_gene_ldmismatchnoLD, aes(x= abs(z), y= susie_pip, color = highlight)) +
geom_point() +
scale_color_manual(values = c("problematic genes" = "red", "good genes" = "black")) +
ggtitle("LD mismatch fixed -- remove the snp") +
theme_minimal()
grid::grid.newpage()
gridExtra::grid.arrange(p1,p2,p3,p4,
ncol = 4,
top = paste0(trait,"-nonSNP_PIPs:",threshold_nonSNP_PIPs,"-SNP_p:",threshold_SNP_p))
susie_alpha_res_ldmismatch <- ctwas_res_ldmismatch$susie_alpha_res
susie_alpha_res_ldmismatch <- anno_susie_alpha_res(susie_alpha_res_ldmismatch,
mapping_table = mapping_two,
map_by = "molecular_id",
drop_unmapped = TRUE)
combined_pip_by_group_ldmismatch <- combine_gene_pips(susie_alpha_res_ldmismatch,
group_by = "gene_name",
by = "group",
method = "combine_cs",
filter_cs = TRUE,
include_cs_id = F)
susie_alpha_res_ldmismatchnoLD <- ctwas_res_ldmismatchnoLD$susie_alpha_res
susie_alpha_res_ldmismatchnoLD <- anno_susie_alpha_res(susie_alpha_res_ldmismatchnoLD,
mapping_table = mapping_two,
map_by = "molecular_id",
drop_unmapped = TRUE)
combined_pip_by_group_ldmismatchnoLD <- combine_gene_pips(susie_alpha_res_ldmismatchnoLD,
group_by = "gene_name",
by = "group",
method = "combine_cs",
filter_cs = TRUE,
include_cs_id = F)
combined_pip_by_group_ldmismatch <- combined_pip_by_group_ldmismatch[,c("gene_name","combined_pip")]
combined_pip_by_group_regionmerge <- combined_pip_by_group_regionmerge[,c("gene_name","combined_pip")]
combined_pip_by_group_ldmismatchnoLD <- combined_pip_by_group_ldmismatchnoLD[,c("gene_name","combined_pip")]
merged <- merge(combined_pip_by_group_regionmerge,combined_pip_by_group_ldmismatch, by = "gene_name", all.x = T)
merged <- merge(merged,combined_pip_by_group_ldmismatchnoLD, by = "gene_name", all.x = T)
colnames(merged) <- c("gene_name","combined_pip_regionmerge", "combined_pip_ldmismatch_remove", "combined_pip_ldmismatchnoLD")
genes_diff <- merged %>%
rowwise() %>%
dplyr::filter(n_distinct(c(combined_pip_regionmerge, combined_pip_ldmismatch_remove, combined_pip_ldmismatchnoLD)) > 1)
known <- readRDS(paste0("/project/xinhe/xsun/multi_group_ctwas/data/silverstandard/known_annotations_IBD.RDS"))
bystander <- readRDS(paste0("/project/xinhe/xsun/multi_group_ctwas/data/silverstandard/bystanders_IBD.RDS"))
genes_diff <- genes_diff %>%
mutate(
is_silver = gene_name %in% known,
is_bystander = gene_name %in% bystander
)
}else {
n_problematic_gene <- 0
}
2025-04-25 12:05:38 INFO::Annotating susie alpha result ...
2025-04-25 12:05:39 INFO::Map molecular traits to genes
2025-04-25 12:05:45 INFO::Split PIPs for molecular traits mapped to multiple genes
2025-04-25 12:06:47 INFO::Annotating susie alpha result ...
2025-04-25 12:06:48 INFO::Map molecular traits to genes
2025-04-25 12:06:50 INFO::Split PIPs for molecular traits mapped to multiple genes# setting <- paste0("thin",thin,".",var_struc,".L",L,".genepip",threshold_nonSNP_PIPs,"_snpp_",threshold_SNP_p)
setting <- paste0("genepip",threshold_nonSNP_PIPs,"_snpp_",threshold_SNP_p)
tmp <- c(setting, n_problematic_snp, n_problematic_gene)
df_summary <- rbind(df_summary,tmp)
DT::datatable(genes_diff,caption = htmltools::tags$caption( style = 'caption-side: left; text-align: left; color:black; font-size:150% ;','Genes -- PIP changed'),options = list(pageLength = 10) )Thresholds for ld mismatch diagnosis: nonSNP PIP = 0.2, SNP p_diff = 5e-8
threshold_nonSNP_PIPs <- 0.2
threshold_SNP_p <- "5e-08"file_res_ldmm <- paste0(folder_results_susieST,trait,"/",trait,".",st,".thin",thin,".",var_struc,".L",L,".ldmismatch_res_genepip",threshold_nonSNP_PIPs,"_snpp_",threshold_SNP_p,".RDS")
res_ldmm <- readRDS(file_res_ldmm)
n_problematic_snp <- length(res_ldmm$problematic_snps)
file_problematic_genes <- paste0(folder_results_susieST,trait,"/",trait,".",st,".thin",thin,".",var_struc,".L",L,".ldmismatch_problematic_genes_genepip",threshold_nonSNP_PIPs,"_snpp_",threshold_SNP_p,".RDS")
if(file.exists(file_problematic_genes)) {
problematic_genes <- readRDS(paste0(folder_results_susieST,trait,"/",trait,".",st,".thin",thin,".",var_struc,".L",L,".ldmismatch_problematic_genes_genepip",threshold_nonSNP_PIPs,"_snpp_",threshold_SNP_p,".RDS"))
n_problematic_gene <- length(problematic_genes)
finemap_res_gene_origin$highlight <- ifelse(finemap_res_gene_origin$id %in% problematic_genes, "problematic genes", "good genes")
p1 <- ggplot(data = finemap_res_gene_origin, aes(x= abs(z), y= susie_pip, color = highlight)) +
geom_point() +
scale_color_manual(values = c("problematic genes" = "red", "good genes" = "black")) +
ggtitle("Original ctwas results") +
theme_minimal()
finemap_res_gene_regionmerge$highlight <- ifelse(finemap_res_gene_regionmerge$id %in% problematic_genes, "problematic genes", "good genes")
p2 <- ggplot(data = finemap_res_gene_regionmerge, aes(x= abs(z), y= susie_pip, color = highlight)) +
geom_point() +
scale_color_manual(values = c("problematic genes" = "red", "good genes" = "black")) +
ggtitle("After region merge") +
theme_minimal()
ctwas_res_ldmismatch <- readRDS(paste0(folder_results_susieST,trait,"/",trait,".",st,".thin",thin,".",var_struc,".L",L,".ldmismatch_finemap_regions_res_genepip",threshold_nonSNP_PIPs,"_snpp_",threshold_SNP_p,".RDS"))
finemap_res_ldmismatch <- ctwas_res_ldmismatch$finemap_res
finemap_res_gene_ldmismatch <- finemap_res_ldmismatch[finemap_res_ldmismatch$type != "SNP",]
finemap_res_gene_ldmismatch$highlight <- ifelse(finemap_res_gene_ldmismatch$id %in% problematic_genes, "problematic genes", "good genes")
p3 <- ggplot(data = finemap_res_gene_ldmismatch, aes(x= abs(z), y= susie_pip, color = highlight)) +
geom_point() +
scale_color_manual(values = c("problematic genes" = "red", "good genes" = "black")) +
ggtitle("LD mismatch fixed -- remove the snp") +
theme_minimal()
ctwas_res_ldmismatchnoLD <- readRDS(paste0(folder_results_susieST,trait,"/",trait,".",st,".thin",thin,".",var_struc,".L",L,".ldmismatch_noLD_finemap_regions_res_genepip",threshold_nonSNP_PIPs,"_snpp_",threshold_SNP_p,".RDS"))
finemap_res_ldmismatchnoLD <- ctwas_res_ldmismatchnoLD$finemap_res
finemap_res_gene_ldmismatchnoLD <- finemap_res_ldmismatchnoLD[finemap_res_ldmismatchnoLD$type != "SNP",]
finemap_res_gene_ldmismatchnoLD$highlight <- ifelse(finemap_res_gene_ldmismatchnoLD$id %in% problematic_genes, "problematic genes", "good genes")
p4 <- ggplot(data = finemap_res_gene_ldmismatchnoLD, aes(x= abs(z), y= susie_pip, color = highlight)) +
geom_point() +
scale_color_manual(values = c("problematic genes" = "red", "good genes" = "black")) +
ggtitle("LD mismatch fixed -- remove the snp") +
theme_minimal()
grid::grid.newpage()
gridExtra::grid.arrange(p1,p2,p3,p4,
ncol = 4,
top = paste0(trait,"-nonSNP_PIPs:",threshold_nonSNP_PIPs,"-SNP_p:",threshold_SNP_p))
susie_alpha_res_ldmismatch <- ctwas_res_ldmismatch$susie_alpha_res
susie_alpha_res_ldmismatch <- anno_susie_alpha_res(susie_alpha_res_ldmismatch,
mapping_table = mapping_two,
map_by = "molecular_id",
drop_unmapped = TRUE)
combined_pip_by_group_ldmismatch <- combine_gene_pips(susie_alpha_res_ldmismatch,
group_by = "gene_name",
by = "group",
method = "combine_cs",
filter_cs = TRUE,
include_cs_id = F)
susie_alpha_res_ldmismatchnoLD <- ctwas_res_ldmismatchnoLD$susie_alpha_res
susie_alpha_res_ldmismatchnoLD <- anno_susie_alpha_res(susie_alpha_res_ldmismatchnoLD,
mapping_table = mapping_two,
map_by = "molecular_id",
drop_unmapped = TRUE)
combined_pip_by_group_ldmismatchnoLD <- combine_gene_pips(susie_alpha_res_ldmismatchnoLD,
group_by = "gene_name",
by = "group",
method = "combine_cs",
filter_cs = TRUE,
include_cs_id = F)
combined_pip_by_group_ldmismatch <- combined_pip_by_group_ldmismatch[,c("gene_name","combined_pip")]
combined_pip_by_group_regionmerge <- combined_pip_by_group_regionmerge[,c("gene_name","combined_pip")]
combined_pip_by_group_ldmismatchnoLD <- combined_pip_by_group_ldmismatchnoLD[,c("gene_name","combined_pip")]
merged <- merge(combined_pip_by_group_regionmerge,combined_pip_by_group_ldmismatch, by = "gene_name", all.x = T)
merged <- merge(merged,combined_pip_by_group_ldmismatchnoLD, by = "gene_name", all.x = T)
colnames(merged) <- c("gene_name","combined_pip_regionmerge", "combined_pip_ldmismatch_remove", "combined_pip_ldmismatchnoLD")
genes_diff <- merged %>%
rowwise() %>%
dplyr::filter(n_distinct(c(combined_pip_regionmerge, combined_pip_ldmismatch_remove, combined_pip_ldmismatchnoLD)) > 1)
known <- readRDS(paste0("/project/xinhe/xsun/multi_group_ctwas/data/silverstandard/known_annotations_IBD.RDS"))
bystander <- readRDS(paste0("/project/xinhe/xsun/multi_group_ctwas/data/silverstandard/bystanders_IBD.RDS"))
genes_diff <- genes_diff %>%
mutate(
is_silver = gene_name %in% known,
is_bystander = gene_name %in% bystander
)
}else {
n_problematic_gene <- 0
}
2025-04-25 12:10:13 INFO::Annotating susie alpha result ...
2025-04-25 12:10:13 INFO::Map molecular traits to genes
2025-04-25 12:10:15 INFO::Split PIPs for molecular traits mapped to multiple genes
2025-04-25 12:11:18 INFO::Annotating susie alpha result ...
2025-04-25 12:11:19 INFO::Map molecular traits to genes
2025-04-25 12:11:21 INFO::Split PIPs for molecular traits mapped to multiple genes# setting <- paste0("thin",thin,".",var_struc,".L",L,".genepip",threshold_nonSNP_PIPs,"_snpp_",threshold_SNP_p)
setting <- paste0("genepip",threshold_nonSNP_PIPs,"_snpp_",threshold_SNP_p)
tmp <- c(setting, n_problematic_snp, n_problematic_gene)
df_summary <- rbind(df_summary,tmp)
DT::datatable(genes_diff,caption = htmltools::tags$caption( style = 'caption-side: left; text-align: left; color:black; font-size:150% ;','Genes -- PIP changed'),options = list(pageLength = 10) )Thresholds for ld mismatch diagnosis: nonSNP PIP = 0.2, SNP p_diff = 5e-6
threshold_nonSNP_PIPs <- 0.2
threshold_SNP_p <- "5e-06"file_res_ldmm <- paste0(folder_results_susieST,trait,"/",trait,".",st,".thin",thin,".",var_struc,".L",L,".ldmismatch_res_genepip",threshold_nonSNP_PIPs,"_snpp_",threshold_SNP_p,".RDS")
res_ldmm <- readRDS(file_res_ldmm)
n_problematic_snp <- length(res_ldmm$problematic_snps)
file_problematic_genes <- paste0(folder_results_susieST,trait,"/",trait,".",st,".thin",thin,".",var_struc,".L",L,".ldmismatch_problematic_genes_genepip",threshold_nonSNP_PIPs,"_snpp_",threshold_SNP_p,".RDS")
if(file.exists(file_problematic_genes)) {
problematic_genes <- readRDS(paste0(folder_results_susieST,trait,"/",trait,".",st,".thin",thin,".",var_struc,".L",L,".ldmismatch_problematic_genes_genepip",threshold_nonSNP_PIPs,"_snpp_",threshold_SNP_p,".RDS"))
n_problematic_gene <- length(problematic_genes)
finemap_res_gene_origin$highlight <- ifelse(finemap_res_gene_origin$id %in% problematic_genes, "problematic genes", "good genes")
p1 <- ggplot(data = finemap_res_gene_origin, aes(x= abs(z), y= susie_pip, color = highlight)) +
geom_point() +
scale_color_manual(values = c("problematic genes" = "red", "good genes" = "black")) +
ggtitle("Original ctwas results") +
theme_minimal()
finemap_res_gene_regionmerge$highlight <- ifelse(finemap_res_gene_regionmerge$id %in% problematic_genes, "problematic genes", "good genes")
p2 <- ggplot(data = finemap_res_gene_regionmerge, aes(x= abs(z), y= susie_pip, color = highlight)) +
geom_point() +
scale_color_manual(values = c("problematic genes" = "red", "good genes" = "black")) +
ggtitle("After region merge") +
theme_minimal()
ctwas_res_ldmismatch <- readRDS(paste0(folder_results_susieST,trait,"/",trait,".",st,".thin",thin,".",var_struc,".L",L,".ldmismatch_finemap_regions_res_genepip",threshold_nonSNP_PIPs,"_snpp_",threshold_SNP_p,".RDS"))
finemap_res_ldmismatch <- ctwas_res_ldmismatch$finemap_res
finemap_res_gene_ldmismatch <- finemap_res_ldmismatch[finemap_res_ldmismatch$type != "SNP",]
finemap_res_gene_ldmismatch$highlight <- ifelse(finemap_res_gene_ldmismatch$id %in% problematic_genes, "problematic genes", "good genes")
p3 <- ggplot(data = finemap_res_gene_ldmismatch, aes(x= abs(z), y= susie_pip, color = highlight)) +
geom_point() +
scale_color_manual(values = c("problematic genes" = "red", "good genes" = "black")) +
ggtitle("LD mismatch fixed -- remove the snp") +
theme_minimal()
ctwas_res_ldmismatchnoLD <- readRDS(paste0(folder_results_susieST,trait,"/",trait,".",st,".thin",thin,".",var_struc,".L",L,".ldmismatch_noLD_finemap_regions_res_genepip",threshold_nonSNP_PIPs,"_snpp_",threshold_SNP_p,".RDS"))
finemap_res_ldmismatchnoLD <- ctwas_res_ldmismatchnoLD$finemap_res
finemap_res_gene_ldmismatchnoLD <- finemap_res_ldmismatchnoLD[finemap_res_ldmismatchnoLD$type != "SNP",]
finemap_res_gene_ldmismatchnoLD$highlight <- ifelse(finemap_res_gene_ldmismatchnoLD$id %in% problematic_genes, "problematic genes", "good genes")
p4 <- ggplot(data = finemap_res_gene_ldmismatchnoLD, aes(x= abs(z), y= susie_pip, color = highlight)) +
geom_point() +
scale_color_manual(values = c("problematic genes" = "red", "good genes" = "black")) +
ggtitle("LD mismatch fixed -- remove the snp") +
theme_minimal()
grid::grid.newpage()
gridExtra::grid.arrange(p1,p2,p3,p4,
ncol = 4,
top = paste0(trait,"-nonSNP_PIPs:",threshold_nonSNP_PIPs,"-SNP_p:",threshold_SNP_p))
susie_alpha_res_ldmismatch <- ctwas_res_ldmismatch$susie_alpha_res
susie_alpha_res_ldmismatch <- anno_susie_alpha_res(susie_alpha_res_ldmismatch,
mapping_table = mapping_two,
map_by = "molecular_id",
drop_unmapped = TRUE)
combined_pip_by_group_ldmismatch <- combine_gene_pips(susie_alpha_res_ldmismatch,
group_by = "gene_name",
by = "group",
method = "combine_cs",
filter_cs = TRUE,
include_cs_id = F)
susie_alpha_res_ldmismatchnoLD <- ctwas_res_ldmismatchnoLD$susie_alpha_res
susie_alpha_res_ldmismatchnoLD <- anno_susie_alpha_res(susie_alpha_res_ldmismatchnoLD,
mapping_table = mapping_two,
map_by = "molecular_id",
drop_unmapped = TRUE)
combined_pip_by_group_ldmismatchnoLD <- combine_gene_pips(susie_alpha_res_ldmismatchnoLD,
group_by = "gene_name",
by = "group",
method = "combine_cs",
filter_cs = TRUE,
include_cs_id = F)
combined_pip_by_group_ldmismatch <- combined_pip_by_group_ldmismatch[,c("gene_name","combined_pip")]
combined_pip_by_group_regionmerge <- combined_pip_by_group_regionmerge[,c("gene_name","combined_pip")]
combined_pip_by_group_ldmismatchnoLD <- combined_pip_by_group_ldmismatchnoLD[,c("gene_name","combined_pip")]
merged <- merge(combined_pip_by_group_regionmerge,combined_pip_by_group_ldmismatch, by = "gene_name", all.x = T)
merged <- merge(merged,combined_pip_by_group_ldmismatchnoLD, by = "gene_name", all.x = T)
colnames(merged) <- c("gene_name","combined_pip_regionmerge", "combined_pip_ldmismatch_remove", "combined_pip_ldmismatchnoLD")
genes_diff <- merged %>%
rowwise() %>%
dplyr::filter(n_distinct(c(combined_pip_regionmerge, combined_pip_ldmismatch_remove, combined_pip_ldmismatchnoLD)) > 1)
known <- readRDS(paste0("/project/xinhe/xsun/multi_group_ctwas/data/silverstandard/known_annotations_IBD.RDS"))
bystander <- readRDS(paste0("/project/xinhe/xsun/multi_group_ctwas/data/silverstandard/bystanders_IBD.RDS"))
genes_diff <- genes_diff %>%
mutate(
is_silver = gene_name %in% known,
is_bystander = gene_name %in% bystander
)
}else {
n_problematic_gene <- 0
}
2025-04-25 12:14:41 INFO::Annotating susie alpha result ...
2025-04-25 12:14:42 INFO::Map molecular traits to genes
2025-04-25 12:14:44 INFO::Split PIPs for molecular traits mapped to multiple genes
2025-04-25 12:15:47 INFO::Annotating susie alpha result ...
2025-04-25 12:15:48 INFO::Map molecular traits to genes
2025-04-25 12:15:50 INFO::Split PIPs for molecular traits mapped to multiple genes# setting <- paste0("thin",thin,".",var_struc,".L",L,".genepip",threshold_nonSNP_PIPs,"_snpp_",threshold_SNP_p)
setting <- paste0("genepip",threshold_nonSNP_PIPs,"_snpp_",threshold_SNP_p)
tmp <- c(setting, n_problematic_snp, n_problematic_gene)
df_summary <- rbind(df_summary,tmp)
DT::datatable(genes_diff,caption = htmltools::tags$caption( style = 'caption-side: left; text-align: left; color:black; font-size:150% ;','Genes -- PIP changed'),options = list(pageLength = 10) )Thresholds for ld mismatch diagnosis: nonSNP PIP = 0.2, SNP p_diff = 5e-4
threshold_nonSNP_PIPs <- 0.2
threshold_SNP_p <- "5e-04"file_res_ldmm <- paste0(folder_results_susieST,trait,"/",trait,".",st,".thin",thin,".",var_struc,".L",L,".ldmismatch_res_genepip",threshold_nonSNP_PIPs,"_snpp_",threshold_SNP_p,".RDS")
res_ldmm <- readRDS(file_res_ldmm)
n_problematic_snp <- length(res_ldmm$problematic_snps)
file_problematic_genes <- paste0(folder_results_susieST,trait,"/",trait,".",st,".thin",thin,".",var_struc,".L",L,".ldmismatch_problematic_genes_genepip",threshold_nonSNP_PIPs,"_snpp_",threshold_SNP_p,".RDS")
if(file.exists(file_problematic_genes)) {
problematic_genes <- readRDS(paste0(folder_results_susieST,trait,"/",trait,".",st,".thin",thin,".",var_struc,".L",L,".ldmismatch_problematic_genes_genepip",threshold_nonSNP_PIPs,"_snpp_",threshold_SNP_p,".RDS"))
n_problematic_gene <- length(problematic_genes)
finemap_res_gene_origin$highlight <- ifelse(finemap_res_gene_origin$id %in% problematic_genes, "problematic genes", "good genes")
p1 <- ggplot(data = finemap_res_gene_origin, aes(x= abs(z), y= susie_pip, color = highlight)) +
geom_point() +
scale_color_manual(values = c("problematic genes" = "red", "good genes" = "black")) +
ggtitle("Original ctwas results") +
theme_minimal()
finemap_res_gene_regionmerge$highlight <- ifelse(finemap_res_gene_regionmerge$id %in% problematic_genes, "problematic genes", "good genes")
p2 <- ggplot(data = finemap_res_gene_regionmerge, aes(x= abs(z), y= susie_pip, color = highlight)) +
geom_point() +
scale_color_manual(values = c("problematic genes" = "red", "good genes" = "black")) +
ggtitle("After region merge") +
theme_minimal()
ctwas_res_ldmismatch <- readRDS(paste0(folder_results_susieST,trait,"/",trait,".",st,".thin",thin,".",var_struc,".L",L,".ldmismatch_finemap_regions_res_genepip",threshold_nonSNP_PIPs,"_snpp_",threshold_SNP_p,".RDS"))
finemap_res_ldmismatch <- ctwas_res_ldmismatch$finemap_res
finemap_res_gene_ldmismatch <- finemap_res_ldmismatch[finemap_res_ldmismatch$type != "SNP",]
finemap_res_gene_ldmismatch$highlight <- ifelse(finemap_res_gene_ldmismatch$id %in% problematic_genes, "problematic genes", "good genes")
p3 <- ggplot(data = finemap_res_gene_ldmismatch, aes(x= abs(z), y= susie_pip, color = highlight)) +
geom_point() +
scale_color_manual(values = c("problematic genes" = "red", "good genes" = "black")) +
ggtitle("LD mismatch fixed -- remove the snp") +
theme_minimal()
ctwas_res_ldmismatchnoLD <- readRDS(paste0(folder_results_susieST,trait,"/",trait,".",st,".thin",thin,".",var_struc,".L",L,".ldmismatch_noLD_finemap_regions_res_genepip",threshold_nonSNP_PIPs,"_snpp_",threshold_SNP_p,".RDS"))
finemap_res_ldmismatchnoLD <- ctwas_res_ldmismatchnoLD$finemap_res
finemap_res_gene_ldmismatchnoLD <- finemap_res_ldmismatchnoLD[finemap_res_ldmismatchnoLD$type != "SNP",]
finemap_res_gene_ldmismatchnoLD$highlight <- ifelse(finemap_res_gene_ldmismatchnoLD$id %in% problematic_genes, "problematic genes", "good genes")
p4 <- ggplot(data = finemap_res_gene_ldmismatchnoLD, aes(x= abs(z), y= susie_pip, color = highlight)) +
geom_point() +
scale_color_manual(values = c("problematic genes" = "red", "good genes" = "black")) +
ggtitle("LD mismatch fixed -- remove the snp") +
theme_minimal()
grid::grid.newpage()
gridExtra::grid.arrange(p1,p2,p3,p4,
ncol = 4,
top = paste0(trait,"-nonSNP_PIPs:",threshold_nonSNP_PIPs,"-SNP_p:",threshold_SNP_p))
susie_alpha_res_ldmismatch <- ctwas_res_ldmismatch$susie_alpha_res
susie_alpha_res_ldmismatch <- anno_susie_alpha_res(susie_alpha_res_ldmismatch,
mapping_table = mapping_two,
map_by = "molecular_id",
drop_unmapped = TRUE)
combined_pip_by_group_ldmismatch <- combine_gene_pips(susie_alpha_res_ldmismatch,
group_by = "gene_name",
by = "group",
method = "combine_cs",
filter_cs = TRUE,
include_cs_id = F)
susie_alpha_res_ldmismatchnoLD <- ctwas_res_ldmismatchnoLD$susie_alpha_res
susie_alpha_res_ldmismatchnoLD <- anno_susie_alpha_res(susie_alpha_res_ldmismatchnoLD,
mapping_table = mapping_two,
map_by = "molecular_id",
drop_unmapped = TRUE)
combined_pip_by_group_ldmismatchnoLD <- combine_gene_pips(susie_alpha_res_ldmismatchnoLD,
group_by = "gene_name",
by = "group",
method = "combine_cs",
filter_cs = TRUE,
include_cs_id = F)
combined_pip_by_group_ldmismatch <- combined_pip_by_group_ldmismatch[,c("gene_name","combined_pip")]
combined_pip_by_group_regionmerge <- combined_pip_by_group_regionmerge[,c("gene_name","combined_pip")]
combined_pip_by_group_ldmismatchnoLD <- combined_pip_by_group_ldmismatchnoLD[,c("gene_name","combined_pip")]
merged <- merge(combined_pip_by_group_regionmerge,combined_pip_by_group_ldmismatch, by = "gene_name", all.x = T)
merged <- merge(merged,combined_pip_by_group_ldmismatchnoLD, by = "gene_name", all.x = T)
colnames(merged) <- c("gene_name","combined_pip_regionmerge", "combined_pip_ldmismatch_remove", "combined_pip_ldmismatchnoLD")
genes_diff <- merged %>%
rowwise() %>%
dplyr::filter(n_distinct(c(combined_pip_regionmerge, combined_pip_ldmismatch_remove, combined_pip_ldmismatchnoLD)) > 1)
known <- readRDS(paste0("/project/xinhe/xsun/multi_group_ctwas/data/silverstandard/known_annotations_IBD.RDS"))
bystander <- readRDS(paste0("/project/xinhe/xsun/multi_group_ctwas/data/silverstandard/bystanders_IBD.RDS"))
genes_diff <- genes_diff %>%
mutate(
is_silver = gene_name %in% known,
is_bystander = gene_name %in% bystander
)
}else {
n_problematic_gene <- 0
}
| Version | Author | Date |
|---|---|---|
| 6e49457 | XSun | 2025-04-17 |
2025-04-25 12:19:12 INFO::Annotating susie alpha result ...
2025-04-25 12:19:12 INFO::Map molecular traits to genes
2025-04-25 12:19:15 INFO::Split PIPs for molecular traits mapped to multiple genes
2025-04-25 12:20:18 INFO::Annotating susie alpha result ...
2025-04-25 12:20:19 INFO::Map molecular traits to genes
2025-04-25 12:20:21 INFO::Split PIPs for molecular traits mapped to multiple genes# setting <- paste0("thin",thin,".",var_struc,".L",L,".genepip",threshold_nonSNP_PIPs,"_snpp_",threshold_SNP_p)
setting <- paste0("genepip",threshold_nonSNP_PIPs,"_snpp_",threshold_SNP_p)
tmp <- c(setting, n_problematic_snp, n_problematic_gene)
df_summary <- rbind(df_summary,tmp)
DT::datatable(genes_diff,caption = htmltools::tags$caption( style = 'caption-side: left; text-align: left; color:black; font-size:150% ;','Genes -- PIP changed'),options = list(pageLength = 10) )colnames(df_summary) <- c("threshold","num_problematic_snp","num_problematic_gene")
rownames(df_summary) <- NULL
DT::datatable(df_summary,caption = htmltools::tags$caption( style = 'caption-side: left; text-align: left; color:black; font-size:150% ;','Number of problematci genes & snps'),options = list(pageLength = 10) )aFib-ebi-a-GCST006414
Setting: shared_all, thin = 1, L = 5, susieST
trait <- "aFib-ebi-a-GCST006414"
thin <- 1
var_struc <- "shared_all"
st <- "with_susieST"
L <- 5
ctwas_res_origin <- readRDS(paste0(folder_results_susieST,trait,"/",trait,".",st,".thin",thin,".",var_struc,".L",L,".finemap_regions_res.RDS"))
finemap_res_origin <- ctwas_res_origin$finemap_res
finemap_res_gene_origin <- finemap_res_origin[finemap_res_origin$type != "SNP",]
ctwas_res_regionmerge <- readRDS(paste0(folder_results_susieST,trait,"/",trait,".",st,".thin",thin,".",var_struc,".L",L,".regionmerge_finemap_regions_res.RDS"))
finemap_res_regionmerge <- ctwas_res_regionmerge$finemap_res
finemap_res_gene_regionmerge <- finemap_res_regionmerge[finemap_res_regionmerge$type != "SNP",]
df_summary <- c()Thresholds for ld mismatch diagnosis: nonSNP PIP = 0.5, SNP p_diff = 5e-8
threshold_nonSNP_PIPs <- 0.5
threshold_SNP_p <- "5e-08"file_res_ldmm <- paste0(folder_results_susieST,trait,"/",trait,".",st,".thin",thin,".",var_struc,".L",L,".ldmismatch_res_genepip",threshold_nonSNP_PIPs,"_snpp_",threshold_SNP_p,".RDS")
res_ldmm <- readRDS(file_res_ldmm)
n_problematic_snp <- length(res_ldmm$problematic_snps)
file_problematic_genes <- paste0(folder_results_susieST,trait,"/",trait,".",st,".thin",thin,".",var_struc,".L",L,".ldmismatch_problematic_genes_genepip",threshold_nonSNP_PIPs,"_snpp_",threshold_SNP_p,".RDS")
if(file.exists(file_problematic_genes)) {
problematic_genes <- readRDS(paste0(folder_results_susieST,trait,"/",trait,".",st,".thin",thin,".",var_struc,".L",L,".ldmismatch_problematic_genes_genepip",threshold_nonSNP_PIPs,"_snpp_",threshold_SNP_p,".RDS"))
n_problematic_gene <- length(problematic_genes)
finemap_res_gene_origin$highlight <- ifelse(finemap_res_gene_origin$id %in% problematic_genes, "problematic genes", "good genes")
p1 <- ggplot(data = finemap_res_gene_origin, aes(x= abs(z), y= susie_pip, color = highlight)) +
geom_point() +
scale_color_manual(values = c("problematic genes" = "red", "good genes" = "black")) +
ggtitle("Original ctwas results") +
theme_minimal()
finemap_res_gene_regionmerge$highlight <- ifelse(finemap_res_gene_regionmerge$id %in% problematic_genes, "problematic genes", "good genes")
p2 <- ggplot(data = finemap_res_gene_regionmerge, aes(x= abs(z), y= susie_pip, color = highlight)) +
geom_point() +
scale_color_manual(values = c("problematic genes" = "red", "good genes" = "black")) +
ggtitle("After region merge") +
theme_minimal()
ctwas_res_ldmismatch <- readRDS(paste0(folder_results_susieST,trait,"/",trait,".",st,".thin",thin,".",var_struc,".L",L,".ldmismatch_finemap_regions_res_genepip",threshold_nonSNP_PIPs,"_snpp_",threshold_SNP_p,".RDS"))
finemap_res_ldmismatch <- ctwas_res_ldmismatch$finemap_res
finemap_res_gene_ldmismatch <- finemap_res_ldmismatch[finemap_res_ldmismatch$type != "SNP",]
finemap_res_gene_ldmismatch$highlight <- ifelse(finemap_res_gene_ldmismatch$id %in% problematic_genes, "problematic genes", "good genes")
p3 <- ggplot(data = finemap_res_gene_ldmismatch, aes(x= abs(z), y= susie_pip, color = highlight)) +
geom_point() +
scale_color_manual(values = c("problematic genes" = "red", "good genes" = "black")) +
ggtitle("LD mismatch fixed -- remove the snp") +
theme_minimal()
ctwas_res_ldmismatchnoLD <- readRDS(paste0(folder_results_susieST,trait,"/",trait,".",st,".thin",thin,".",var_struc,".L",L,".ldmismatch_noLD_finemap_regions_res_genepip",threshold_nonSNP_PIPs,"_snpp_",threshold_SNP_p,".RDS"))
finemap_res_ldmismatchnoLD <- ctwas_res_ldmismatchnoLD$finemap_res
finemap_res_gene_ldmismatchnoLD <- finemap_res_ldmismatchnoLD[finemap_res_ldmismatchnoLD$type != "SNP",]
finemap_res_gene_ldmismatchnoLD$highlight <- ifelse(finemap_res_gene_ldmismatchnoLD$id %in% problematic_genes, "problematic genes", "good genes")
p4 <- ggplot(data = finemap_res_gene_ldmismatchnoLD, aes(x= abs(z), y= susie_pip, color = highlight)) +
geom_point() +
scale_color_manual(values = c("problematic genes" = "red", "good genes" = "black")) +
ggtitle("LD mismatch fixed -- remove the snp") +
theme_minimal()
grid::grid.newpage()
gridExtra::grid.arrange(p1,p2,p3,p4,
ncol = 4,
top = paste0(trait,"-nonSNP_PIPs:",threshold_nonSNP_PIPs,"-SNP_p:",threshold_SNP_p))
}else {
n_problematic_gene <- 0
}
# setting <- paste0("thin",thin,".",var_struc,".L",L,".genepip",threshold_nonSNP_PIPs,"_snpp_",threshold_SNP_p)
setting <- paste0("genepip",threshold_nonSNP_PIPs,"_snpp_",threshold_SNP_p)
tmp <- c(setting, n_problematic_snp, n_problematic_gene)
df_summary <- rbind(df_summary,tmp)Thresholds for ld mismatch diagnosis: nonSNP PIP = 0.5, SNP p_diff = 5e-6
threshold_nonSNP_PIPs <- 0.5
threshold_SNP_p <- "5e-06"file_res_ldmm <- paste0(folder_results_susieST,trait,"/",trait,".",st,".thin",thin,".",var_struc,".L",L,".ldmismatch_res_genepip",threshold_nonSNP_PIPs,"_snpp_",threshold_SNP_p,".RDS")
res_ldmm <- readRDS(file_res_ldmm)
n_problematic_snp <- length(res_ldmm$problematic_snps)
file_problematic_genes <- paste0(folder_results_susieST,trait,"/",trait,".",st,".thin",thin,".",var_struc,".L",L,".ldmismatch_problematic_genes_genepip",threshold_nonSNP_PIPs,"_snpp_",threshold_SNP_p,".RDS")
if(file.exists(file_problematic_genes)) {
problematic_genes <- readRDS(paste0(folder_results_susieST,trait,"/",trait,".",st,".thin",thin,".",var_struc,".L",L,".ldmismatch_problematic_genes_genepip",threshold_nonSNP_PIPs,"_snpp_",threshold_SNP_p,".RDS"))
n_problematic_gene <- length(problematic_genes)
finemap_res_gene_origin$highlight <- ifelse(finemap_res_gene_origin$id %in% problematic_genes, "problematic genes", "good genes")
p1 <- ggplot(data = finemap_res_gene_origin, aes(x= abs(z), y= susie_pip, color = highlight)) +
geom_point() +
scale_color_manual(values = c("problematic genes" = "red", "good genes" = "black")) +
ggtitle("Original ctwas results") +
theme_minimal()
finemap_res_gene_regionmerge$highlight <- ifelse(finemap_res_gene_regionmerge$id %in% problematic_genes, "problematic genes", "good genes")
p2 <- ggplot(data = finemap_res_gene_regionmerge, aes(x= abs(z), y= susie_pip, color = highlight)) +
geom_point() +
scale_color_manual(values = c("problematic genes" = "red", "good genes" = "black")) +
ggtitle("After region merge") +
theme_minimal()
ctwas_res_ldmismatch <- readRDS(paste0(folder_results_susieST,trait,"/",trait,".",st,".thin",thin,".",var_struc,".L",L,".ldmismatch_finemap_regions_res_genepip",threshold_nonSNP_PIPs,"_snpp_",threshold_SNP_p,".RDS"))
finemap_res_ldmismatch <- ctwas_res_ldmismatch$finemap_res
finemap_res_gene_ldmismatch <- finemap_res_ldmismatch[finemap_res_ldmismatch$type != "SNP",]
finemap_res_gene_ldmismatch$highlight <- ifelse(finemap_res_gene_ldmismatch$id %in% problematic_genes, "problematic genes", "good genes")
p3 <- ggplot(data = finemap_res_gene_ldmismatch, aes(x= abs(z), y= susie_pip, color = highlight)) +
geom_point() +
scale_color_manual(values = c("problematic genes" = "red", "good genes" = "black")) +
ggtitle("LD mismatch fixed -- remove the snp") +
theme_minimal()
ctwas_res_ldmismatchnoLD <- readRDS(paste0(folder_results_susieST,trait,"/",trait,".",st,".thin",thin,".",var_struc,".L",L,".ldmismatch_noLD_finemap_regions_res_genepip",threshold_nonSNP_PIPs,"_snpp_",threshold_SNP_p,".RDS"))
finemap_res_ldmismatchnoLD <- ctwas_res_ldmismatchnoLD$finemap_res
finemap_res_gene_ldmismatchnoLD <- finemap_res_ldmismatchnoLD[finemap_res_ldmismatchnoLD$type != "SNP",]
finemap_res_gene_ldmismatchnoLD$highlight <- ifelse(finemap_res_gene_ldmismatchnoLD$id %in% problematic_genes, "problematic genes", "good genes")
p4 <- ggplot(data = finemap_res_gene_ldmismatchnoLD, aes(x= abs(z), y= susie_pip, color = highlight)) +
geom_point() +
scale_color_manual(values = c("problematic genes" = "red", "good genes" = "black")) +
ggtitle("LD mismatch fixed -- remove the snp") +
theme_minimal()
grid::grid.newpage()
gridExtra::grid.arrange(p1,p2,p3,p4,
ncol = 4,
top = paste0(trait,"-nonSNP_PIPs:",threshold_nonSNP_PIPs,"-SNP_p:",threshold_SNP_p))
}else {
n_problematic_gene <- 0
}
# setting <- paste0("thin",thin,".",var_struc,".L",L,".genepip",threshold_nonSNP_PIPs,"_snpp_",threshold_SNP_p)
setting <- paste0("genepip",threshold_nonSNP_PIPs,"_snpp_",threshold_SNP_p)
tmp <- c(setting, n_problematic_snp, n_problematic_gene)
df_summary <- rbind(df_summary,tmp)Thresholds for ld mismatch diagnosis: nonSNP PIP = 0.2, SNP p_diff = 5e-8
threshold_nonSNP_PIPs <- 0.2
threshold_SNP_p <- "5e-08"file_res_ldmm <- paste0(folder_results_susieST,trait,"/",trait,".",st,".thin",thin,".",var_struc,".L",L,".ldmismatch_res_genepip",threshold_nonSNP_PIPs,"_snpp_",threshold_SNP_p,".RDS")
res_ldmm <- readRDS(file_res_ldmm)
n_problematic_snp <- length(res_ldmm$problematic_snps)
file_problematic_genes <- paste0(folder_results_susieST,trait,"/",trait,".",st,".thin",thin,".",var_struc,".L",L,".ldmismatch_problematic_genes_genepip",threshold_nonSNP_PIPs,"_snpp_",threshold_SNP_p,".RDS")
if(file.exists(file_problematic_genes)) {
problematic_genes <- readRDS(paste0(folder_results_susieST,trait,"/",trait,".",st,".thin",thin,".",var_struc,".L",L,".ldmismatch_problematic_genes_genepip",threshold_nonSNP_PIPs,"_snpp_",threshold_SNP_p,".RDS"))
n_problematic_gene <- length(problematic_genes)
finemap_res_gene_origin$highlight <- ifelse(finemap_res_gene_origin$id %in% problematic_genes, "problematic genes", "good genes")
p1 <- ggplot(data = finemap_res_gene_origin, aes(x= abs(z), y= susie_pip, color = highlight)) +
geom_point() +
scale_color_manual(values = c("problematic genes" = "red", "good genes" = "black")) +
ggtitle("Original ctwas results") +
theme_minimal()
finemap_res_gene_regionmerge$highlight <- ifelse(finemap_res_gene_regionmerge$id %in% problematic_genes, "problematic genes", "good genes")
p2 <- ggplot(data = finemap_res_gene_regionmerge, aes(x= abs(z), y= susie_pip, color = highlight)) +
geom_point() +
scale_color_manual(values = c("problematic genes" = "red", "good genes" = "black")) +
ggtitle("After region merge") +
theme_minimal()
ctwas_res_ldmismatch <- readRDS(paste0(folder_results_susieST,trait,"/",trait,".",st,".thin",thin,".",var_struc,".L",L,".ldmismatch_finemap_regions_res_genepip",threshold_nonSNP_PIPs,"_snpp_",threshold_SNP_p,".RDS"))
finemap_res_ldmismatch <- ctwas_res_ldmismatch$finemap_res
finemap_res_gene_ldmismatch <- finemap_res_ldmismatch[finemap_res_ldmismatch$type != "SNP",]
finemap_res_gene_ldmismatch$highlight <- ifelse(finemap_res_gene_ldmismatch$id %in% problematic_genes, "problematic genes", "good genes")
p3 <- ggplot(data = finemap_res_gene_ldmismatch, aes(x= abs(z), y= susie_pip, color = highlight)) +
geom_point() +
scale_color_manual(values = c("problematic genes" = "red", "good genes" = "black")) +
ggtitle("LD mismatch fixed -- remove the snp") +
theme_minimal()
ctwas_res_ldmismatchnoLD <- readRDS(paste0(folder_results_susieST,trait,"/",trait,".",st,".thin",thin,".",var_struc,".L",L,".ldmismatch_noLD_finemap_regions_res_genepip",threshold_nonSNP_PIPs,"_snpp_",threshold_SNP_p,".RDS"))
finemap_res_ldmismatchnoLD <- ctwas_res_ldmismatchnoLD$finemap_res
finemap_res_gene_ldmismatchnoLD <- finemap_res_ldmismatchnoLD[finemap_res_ldmismatchnoLD$type != "SNP",]
finemap_res_gene_ldmismatchnoLD$highlight <- ifelse(finemap_res_gene_ldmismatchnoLD$id %in% problematic_genes, "problematic genes", "good genes")
p4 <- ggplot(data = finemap_res_gene_ldmismatchnoLD, aes(x= abs(z), y= susie_pip, color = highlight)) +
geom_point() +
scale_color_manual(values = c("problematic genes" = "red", "good genes" = "black")) +
ggtitle("LD mismatch fixed -- remove the snp") +
theme_minimal()
grid::grid.newpage()
gridExtra::grid.arrange(p1,p2,p3,p4,
ncol = 4,
top = paste0(trait,"-nonSNP_PIPs:",threshold_nonSNP_PIPs,"-SNP_p:",threshold_SNP_p))
}else {
n_problematic_gene <- 0
}
# setting <- paste0("thin",thin,".",var_struc,".L",L,".genepip",threshold_nonSNP_PIPs,"_snpp_",threshold_SNP_p)
setting <- paste0("genepip",threshold_nonSNP_PIPs,"_snpp_",threshold_SNP_p)
tmp <- c(setting, n_problematic_snp, n_problematic_gene)
df_summary <- rbind(df_summary,tmp)Thresholds for ld mismatch diagnosis: nonSNP PIP = 0.2, SNP p_diff = 5e-6
threshold_nonSNP_PIPs <- 0.2
threshold_SNP_p <- "5e-06"file_res_ldmm <- paste0(folder_results_susieST,trait,"/",trait,".",st,".thin",thin,".",var_struc,".L",L,".ldmismatch_res_genepip",threshold_nonSNP_PIPs,"_snpp_",threshold_SNP_p,".RDS")
res_ldmm <- readRDS(file_res_ldmm)
n_problematic_snp <- length(res_ldmm$problematic_snps)
file_problematic_genes <- paste0(folder_results_susieST,trait,"/",trait,".",st,".thin",thin,".",var_struc,".L",L,".ldmismatch_problematic_genes_genepip",threshold_nonSNP_PIPs,"_snpp_",threshold_SNP_p,".RDS")
if(file.exists(file_problematic_genes)) {
problematic_genes <- readRDS(paste0(folder_results_susieST,trait,"/",trait,".",st,".thin",thin,".",var_struc,".L",L,".ldmismatch_problematic_genes_genepip",threshold_nonSNP_PIPs,"_snpp_",threshold_SNP_p,".RDS"))
n_problematic_gene <- length(problematic_genes)
finemap_res_gene_origin$highlight <- ifelse(finemap_res_gene_origin$id %in% problematic_genes, "problematic genes", "good genes")
p1 <- ggplot(data = finemap_res_gene_origin, aes(x= abs(z), y= susie_pip, color = highlight)) +
geom_point() +
scale_color_manual(values = c("problematic genes" = "red", "good genes" = "black")) +
ggtitle("Original ctwas results") +
theme_minimal()
finemap_res_gene_regionmerge$highlight <- ifelse(finemap_res_gene_regionmerge$id %in% problematic_genes, "problematic genes", "good genes")
p2 <- ggplot(data = finemap_res_gene_regionmerge, aes(x= abs(z), y= susie_pip, color = highlight)) +
geom_point() +
scale_color_manual(values = c("problematic genes" = "red", "good genes" = "black")) +
ggtitle("After region merge") +
theme_minimal()
ctwas_res_ldmismatch <- readRDS(paste0(folder_results_susieST,trait,"/",trait,".",st,".thin",thin,".",var_struc,".L",L,".ldmismatch_finemap_regions_res_genepip",threshold_nonSNP_PIPs,"_snpp_",threshold_SNP_p,".RDS"))
finemap_res_ldmismatch <- ctwas_res_ldmismatch$finemap_res
finemap_res_gene_ldmismatch <- finemap_res_ldmismatch[finemap_res_ldmismatch$type != "SNP",]
finemap_res_gene_ldmismatch$highlight <- ifelse(finemap_res_gene_ldmismatch$id %in% problematic_genes, "problematic genes", "good genes")
p3 <- ggplot(data = finemap_res_gene_ldmismatch, aes(x= abs(z), y= susie_pip, color = highlight)) +
geom_point() +
scale_color_manual(values = c("problematic genes" = "red", "good genes" = "black")) +
ggtitle("LD mismatch fixed -- remove the snp") +
theme_minimal()
ctwas_res_ldmismatchnoLD <- readRDS(paste0(folder_results_susieST,trait,"/",trait,".",st,".thin",thin,".",var_struc,".L",L,".ldmismatch_noLD_finemap_regions_res_genepip",threshold_nonSNP_PIPs,"_snpp_",threshold_SNP_p,".RDS"))
finemap_res_ldmismatchnoLD <- ctwas_res_ldmismatchnoLD$finemap_res
finemap_res_gene_ldmismatchnoLD <- finemap_res_ldmismatchnoLD[finemap_res_ldmismatchnoLD$type != "SNP",]
finemap_res_gene_ldmismatchnoLD$highlight <- ifelse(finemap_res_gene_ldmismatchnoLD$id %in% problematic_genes, "problematic genes", "good genes")
p4 <- ggplot(data = finemap_res_gene_ldmismatchnoLD, aes(x= abs(z), y= susie_pip, color = highlight)) +
geom_point() +
scale_color_manual(values = c("problematic genes" = "red", "good genes" = "black")) +
ggtitle("LD mismatch fixed -- remove the snp") +
theme_minimal()
grid::grid.newpage()
gridExtra::grid.arrange(p1,p2,p3,p4,
ncol = 4,
top = paste0(trait,"-nonSNP_PIPs:",threshold_nonSNP_PIPs,"-SNP_p:",threshold_SNP_p))
}else {
n_problematic_gene <- 0
}
# setting <- paste0("thin",thin,".",var_struc,".L",L,".genepip",threshold_nonSNP_PIPs,"_snpp_",threshold_SNP_p)
setting <- paste0("genepip",threshold_nonSNP_PIPs,"_snpp_",threshold_SNP_p)
tmp <- c(setting, n_problematic_snp, n_problematic_gene)
df_summary <- rbind(df_summary,tmp)Thresholds for ld mismatch diagnosis: nonSNP PIP = 0.2, SNP p_diff = 5e-4
threshold_nonSNP_PIPs <- 0.2
threshold_SNP_p <- "5e-04"file_res_ldmm <- paste0(folder_results_susieST,trait,"/",trait,".",st,".thin",thin,".",var_struc,".L",L,".ldmismatch_res_genepip",threshold_nonSNP_PIPs,"_snpp_",threshold_SNP_p,".RDS")
res_ldmm <- readRDS(file_res_ldmm)
n_problematic_snp <- length(res_ldmm$problematic_snps)
file_problematic_genes <- paste0(folder_results_susieST,trait,"/",trait,".",st,".thin",thin,".",var_struc,".L",L,".ldmismatch_problematic_genes_genepip",threshold_nonSNP_PIPs,"_snpp_",threshold_SNP_p,".RDS")
if(file.exists(file_problematic_genes)) {
problematic_genes <- readRDS(paste0(folder_results_susieST,trait,"/",trait,".",st,".thin",thin,".",var_struc,".L",L,".ldmismatch_problematic_genes_genepip",threshold_nonSNP_PIPs,"_snpp_",threshold_SNP_p,".RDS"))
n_problematic_gene <- length(problematic_genes)
finemap_res_gene_origin$highlight <- ifelse(finemap_res_gene_origin$id %in% problematic_genes, "problematic genes", "good genes")
p1 <- ggplot(data = finemap_res_gene_origin, aes(x= abs(z), y= susie_pip, color = highlight)) +
geom_point() +
scale_color_manual(values = c("problematic genes" = "red", "good genes" = "black")) +
ggtitle("Original ctwas results") +
theme_minimal()
finemap_res_gene_regionmerge$highlight <- ifelse(finemap_res_gene_regionmerge$id %in% problematic_genes, "problematic genes", "good genes")
p2 <- ggplot(data = finemap_res_gene_regionmerge, aes(x= abs(z), y= susie_pip, color = highlight)) +
geom_point() +
scale_color_manual(values = c("problematic genes" = "red", "good genes" = "black")) +
ggtitle("After region merge") +
theme_minimal()
ctwas_res_ldmismatch <- readRDS(paste0(folder_results_susieST,trait,"/",trait,".",st,".thin",thin,".",var_struc,".L",L,".ldmismatch_finemap_regions_res_genepip",threshold_nonSNP_PIPs,"_snpp_",threshold_SNP_p,".RDS"))
finemap_res_ldmismatch <- ctwas_res_ldmismatch$finemap_res
finemap_res_gene_ldmismatch <- finemap_res_ldmismatch[finemap_res_ldmismatch$type != "SNP",]
finemap_res_gene_ldmismatch$highlight <- ifelse(finemap_res_gene_ldmismatch$id %in% problematic_genes, "problematic genes", "good genes")
p3 <- ggplot(data = finemap_res_gene_ldmismatch, aes(x= abs(z), y= susie_pip, color = highlight)) +
geom_point() +
scale_color_manual(values = c("problematic genes" = "red", "good genes" = "black")) +
ggtitle("LD mismatch fixed -- remove the snp") +
theme_minimal()
ctwas_res_ldmismatchnoLD <- readRDS(paste0(folder_results_susieST,trait,"/",trait,".",st,".thin",thin,".",var_struc,".L",L,".ldmismatch_noLD_finemap_regions_res_genepip",threshold_nonSNP_PIPs,"_snpp_",threshold_SNP_p,".RDS"))
finemap_res_ldmismatchnoLD <- ctwas_res_ldmismatchnoLD$finemap_res
finemap_res_gene_ldmismatchnoLD <- finemap_res_ldmismatchnoLD[finemap_res_ldmismatchnoLD$type != "SNP",]
finemap_res_gene_ldmismatchnoLD$highlight <- ifelse(finemap_res_gene_ldmismatchnoLD$id %in% problematic_genes, "problematic genes", "good genes")
p4 <- ggplot(data = finemap_res_gene_ldmismatchnoLD, aes(x= abs(z), y= susie_pip, color = highlight)) +
geom_point() +
scale_color_manual(values = c("problematic genes" = "red", "good genes" = "black")) +
ggtitle("LD mismatch fixed -- remove the snp") +
theme_minimal()
grid::grid.newpage()
gridExtra::grid.arrange(p1,p2,p3,p4,
ncol = 4,
top = paste0(trait,"-nonSNP_PIPs:",threshold_nonSNP_PIPs,"-SNP_p:",threshold_SNP_p))
}else {
n_problematic_gene <- 0
}
# setting <- paste0("thin",thin,".",var_struc,".L",L,".genepip",threshold_nonSNP_PIPs,"_snpp_",threshold_SNP_p)
setting <- paste0("genepip",threshold_nonSNP_PIPs,"_snpp_",threshold_SNP_p)
tmp <- c(setting, n_problematic_snp, n_problematic_gene)
df_summary <- rbind(df_summary,tmp)colnames(df_summary) <- c("threshold","num_problematic_snp","num_problematic_gene")
rownames(df_summary) <- NULL
DT::datatable(df_summary,caption = htmltools::tags$caption( style = 'caption-side: left; text-align: left; color:black; font-size:150% ;','Number of problematci genes & snps'),options = list(pageLength = 10) )
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] stats graphics grDevices utils datasets methods base
other attached packages:
[1] dplyr_1.1.2 ctwas_0.5.5.9001 ggplot2_3.4.2 workflowr_1.7.1
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] GenomicRanges_1.50.2 base64enc_0.1-3
[9] fs_1.5.2 rstudioapi_0.14
[11] farver_2.1.0 DT_0.22
[13] ggrepel_0.9.3 bit64_4.0.5
[15] AnnotationDbi_1.60.2 fansi_1.0.3
[17] xml2_1.3.3 logging_0.10-108
[19] codetools_0.2-18 cachem_1.0.6
[21] knitr_1.42 jsonlite_1.8.9
[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 Biobase_2.58.0
[43] jquerylib_0.1.4 vctrs_0.6.1
[45] Biostrings_2.66.0 rtracklayer_1.58.0
[47] crosstalk_1.2.0 xfun_0.38
[49] stringr_1.5.0 ps_1.7.0
[51] irlba_2.3.5 lifecycle_1.0.4
[53] restfulr_0.0.15 ensembldb_2.22.0
[55] XML_3.99-0.9 getPass_0.2-2
[57] zlibbioc_1.44.0 scales_1.2.0
[59] gggrid_0.2-0 hms_1.1.3
[61] promises_1.2.0.1 MatrixGenerics_1.10.0
[63] ProtGenerics_1.30.0 parallel_4.2.0
[65] SummarizedExperiment_1.28.0 AnnotationFilter_1.22.0
[67] LDlinkR_1.3.0 yaml_2.3.5
[69] curl_4.3.2 gridExtra_2.3
[71] memoise_2.0.1 sass_0.4.1
[73] biomaRt_2.54.1 stringi_1.7.6
[75] RSQLite_2.3.1 highr_0.9
[77] S4Vectors_0.36.2 BiocIO_1.8.0
[79] GenomicFeatures_1.50.4 BiocGenerics_0.44.0
[81] filelock_1.0.2 BiocParallel_1.32.6
[83] repr_1.1.4 GenomeInfoDb_1.34.9
[85] rlang_1.1.2 pkgconfig_2.0.3
[87] matrixStats_1.2.0 bitops_1.0-7
[89] evaluate_0.15 lattice_0.20-45
[91] purrr_1.0.1 labeling_0.4.2
[93] GenomicAlignments_1.34.1 htmlwidgets_1.6.2
[95] cowplot_1.1.1 bit_4.0.4
[97] processx_3.5.3 tidyselect_1.2.0
[99] magrittr_2.0.3 AMR_2.1.1
[101] R6_2.5.1 IRanges_2.32.0
[103] generics_0.1.3 DelayedArray_0.24.0
[105] DBI_1.1.2 pgenlibr_0.3.6
[107] pillar_1.9.0 whisker_0.4
[109] withr_2.5.0 mixsqp_0.3-48
[111] KEGGREST_1.38.0 RCurl_1.98-1.12
[113] tibble_3.2.1 crayon_1.5.1
[115] utf8_1.2.2 BiocFileCache_2.6.1
[117] plotly_4.10.0 tzdb_0.3.0
[119] rmarkdown_2.21 progress_1.2.2
[121] grid_4.2.0 data.table_1.14.4
[123] blob_1.2.3 callr_3.7.0
[125] git2r_0.30.1 digest_0.6.29
[127] tidyr_1.3.0 httpuv_1.6.5
[129] stats4_4.2.0 munsell_0.5.0
[131] viridisLite_0.4.0 skimr_2.1.4
[133] bslib_0.3.1