Compare different settings: cs index filter or not
XSun
2025-04-28
Last updated: 2025-05-01
Checks: 6 1
Knit directory: multigroup_ctwas_analysis/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | a742ef4 | XSun | 2025-04-30 | update |
| html | a742ef4 | XSun | 2025-04-30 | update |
| Rmd | 3b4edbe | XSun | 2025-04-28 | update |
| html | 3b4edbe | XSun | 2025-04-28 | update |
library(dplyr)
library(scales)
library(tidyr)
library(ggplot2)
library(eulerr)
library(patchwork)
traits_silver <- c("T2D","LDL","BMI","RBC","IBD","SCZ","aFib")
names(traits_silver) <- c("T2D-panukb","LDL-ukb-d-30780_irnt","BMI-panukb","RBC-panukb","IBD-ebi-a-GCST004131","SCZ-ieu-b-5102","aFib-ebi-a-GCST006414")
traits <- c("LDL-ukb-d-30780_irnt","IBD-ebi-a-GCST004131","BMI-panukb","RBC-panukb","SCZ-ieu-b-5102","aFib-ebi-a-GCST006414","T2D-panukb")
db <- "GO_Biological_Process_2023"
folder_results <- "/project/xinhe/xsun/multi_group_ctwas/15.susie_weights/snakemake_outputs/"
create_summary_plot_withTP <- function(df, columns_to_plot, x_var = "setting", x_order = NULL, title = NULL) {
# Reshape data
df_long <- df %>%
pivot_longer(
cols = all_of(columns_to_plot),
names_to = "variable",
values_to = "value"
)
# Convert to factor with specified order if x_order is provided
if (!is.null(x_order)) {
df_long <- df_long %>%
mutate(across(all_of(x_var), ~factor(., levels = x_order)))
}
# Identify the max value for scaling
max_main <- max(df_long$value[df_long$variable != "TP_rate"], na.rm = TRUE)
max_tp_rate <- max(df_long$value[df_long$variable == "TP_rate"], na.rm = TRUE)
if(max_tp_rate ==0) {
max_tp_rate <- 1
}
# Rescale TP_rate
df_long <- df_long %>%
mutate(scaled_value = ifelse(variable == "TP_rate",
value * (max_main / max_tp_rate), value))
# Create plot
ggplot(df_long, aes(x = .data[[x_var]], y = scaled_value, color = variable, shape = variable)) +
#geom_point(size = 3, position = position_jitter(width = 0.2)) +
geom_point(size = 3) +
scale_y_continuous(
name = "Count",
sec.axis = sec_axis(~ . * (max_tp_rate / max_main), name = "TP Rate")
) +
labs(x = "Settings", title = title) +
theme_minimal() +
theme(
axis.text.x = element_text(angle = 45, hjust = 1, size = 10),
legend.position = "right",
legend.title = element_blank()
) +
scale_color_brewer(palette = "Set1")
}
DT::datatable(matrix())thin <- 1
var_struc <- "shared_all"
L <- 5
st <- "with_susieST"The number of genes at PIP > 0.8
setting_names <- c()
num_gene_pip08_all <- c()
num_silver_pip08_all <- c()
num_bystander_pip08_all <- c()
go_num_fdr005 <- c()
for (trait in traits) {
for (cs in c("CS_filtered","CS_NOT_filtered")){
if(cs == "CS_filtered"){
cs_setting <- NULL
}else{
cs_setting <- "_csF"
}
setting_names <- c(setting_names, paste0(traits_silver[trait],"-",cs))
# num_gene_pip08 -- cs filtered
combined_pip_by_group <- readRDS(paste0(folder_results,"/",trait,"/",trait,".",st,".thin",thin,".",var_struc,".L",L, ".combined_pip_bygroup_final",cs_setting,".RDS"))
combined_pip_sig <- combined_pip_by_group[combined_pip_by_group$combined_pip > 0.8,]
num_gene_pip08_all <- c(num_gene_pip08_all, nrow(combined_pip_sig))
# silver_standard genes
known <- readRDS(paste0("/project/xinhe/xsun/multi_group_ctwas/data/silverstandard/known_annotations_",traits_silver[trait],".RDS"))
bystander <- readRDS(paste0("/project/xinhe/xsun/multi_group_ctwas/data/silverstandard/bystanders_",traits_silver[trait],".RDS"))
num_silver_pip08_all <- c(num_silver_pip08_all,sum(combined_pip_sig$gene_name %in% known))
num_bystander_pip08_all <- c(num_bystander_pip08_all,sum(combined_pip_sig$gene_name %in% bystander))
file_go <- paste0(folder_results,trait,"/",trait,".",st,".thin",thin,".",var_struc,".L",L, ".enrichr_",db,"_",cs,".RDS")
if(!file.exists(file_go)){
go_num_fdr005 <- c(go_num_fdr005,0)
}else{
go <- readRDS(paste0(folder_results,trait,"/",trait,".",st,".thin",thin,".",var_struc,".L",L, ".enrichr_",db,"_",cs,".RDS"))
go_num_fdr005 <- c(go_num_fdr005,nrow(go))
}
}
}
df <- data.frame(setting = setting_names,
num_gene_pip08 = num_gene_pip08_all,
num_silver_pip08 = num_silver_pip08_all,
num_bystander_pip08 = num_bystander_pip08_all,
TP_rate = num_silver_pip08_all/(num_silver_pip08_all+num_bystander_pip08_all),
go_num_fdr005 = go_num_fdr005)
create_summary_plot_withTP(df,x_order = setting_names,
columns_to_plot = c("num_gene_pip08","num_silver_pip08","TP_rate"))
| Version | Author | Date |
|---|---|---|
| 3b4edbe | XSun | 2025-04-28 |
create_summary_plot_withTP(df,x_order = setting_names,
columns_to_plot = c("go_num_fdr005"), title = "Number of GO terms at p.adjust < 0.05")
| Version | Author | Date |
|---|---|---|
| a742ef4 | XSun | 2025-04-30 |
DT::datatable(df,caption = htmltools::tags$caption( style = 'caption-side: left; text-align: left; color:black; font-size:150% ;',''),options = list(pageLength = 10) )Number of GO terms at adjusted.p < 0.05
traits <- c("LDL-ukb-d-30780_irnt","IBD-ebi-a-GCST004131")
for (trait in traits) {
file_go_filtered <- paste0(folder_results,trait,"/",trait,".",st,".thin",thin,".",var_struc,".L",L, ".enrichr_",db,"_CS_filtered.RDS")
if(!file.exists(file_go_filtered)){
go_filtered <- NULL
}else{
go_filtered <- readRDS(file_go_filtered)
}
file_go_nofiltered <- paste0(folder_results,trait,"/",trait,".",st,".thin",thin,".",var_struc,".L",L, ".enrichr_",db,"_CS_NOT_filtered.RDS")
if(!file.exists(file_go_nofiltered)){
go_nofiltered <- NULL
}else{
go_nofiltered <- readRDS(file_go_nofiltered)
}
set1 <- unique(go_filtered$Term)
set2 <- unique(go_nofiltered$Term)
venn_input <- list(
Filtered = set1,
NoFiltered = set2
)
fit <- euler(venn_input)
print(plot(fit,
fills = c("skyblue", "orange"),
labels = TRUE,
quantities = TRUE,
main = trait))
}
| Version | Author | Date |
|---|---|---|
| a742ef4 | XSun | 2025-04-30 |
| Version | Author | Date |
|---|---|---|
| a742ef4 | XSun | 2025-04-30 |
traits <- c("LDL-ukb-d-30780_irnt","IBD-ebi-a-GCST004131")
for (trait in traits) {
file_go_filtered <- paste0(folder_results,trait,"/",trait,".",st,".thin",thin,".",var_struc,".L",L, ".enrichr_",db,"_CS_filtered.RDS")
if(!file.exists(file_go_filtered)){
go_filtered <- NULL
}else{
go_filtered <- readRDS(file_go_filtered)
}
file_go_nofiltered <- paste0(folder_results,trait,"/",trait,".",st,".thin",thin,".",var_struc,".L",L, ".enrichr_",db,"_CS_NOT_filtered.RDS")
if(!file.exists(file_go_nofiltered)){
go_nofiltered <- NULL
}else{
go_nofiltered <- readRDS(file_go_nofiltered)
}
cat("\n\n")
cat(knitr::knit_print(DT::datatable(go_filtered[!go_filtered$Term %in% go_nofiltered$Term,], caption = htmltools::tags$caption( style = 'font-size: 150%; caption-side: topleft; text-align = left; color:black;',paste0("Unique GO terms at p.adjust < 0.05 -- ",trait, "--CS_filtered")),options = list(pageLength = 5))))
cat("\n\n")
cat("\n\n")
cat(knitr::knit_print(DT::datatable(go_nofiltered[!go_nofiltered$Term %in% go_filtered$Term,], caption = htmltools::tags$caption( style = 'font-size: 150%; caption-side: topleft; text-align = left; color:black;',paste0("Unique GO terms at p.adjust < 0.05 -- ",trait, "--CS_NOT_filtered")),options = list(pageLength = 5))))
cat("\n\n")
}Comparing PIPs at gene-level
traits <- c("LDL-ukb-d-30780_irnt","IBD-ebi-a-GCST004131","BMI-panukb","RBC-panukb","SCZ-ieu-b-5102","aFib-ebi-a-GCST006414","T2D-panukb")
p <- list()
for (trait in traits) {
combined_pip_filtered <- readRDS(paste0(folder_results,"/",trait,"/",trait,".",st,".thin",thin,".",var_struc,".L",L, ".combined_pip_bygroup_final_csincluded.RDS"))
combined_pip_filtered_plot <- combined_pip_filtered[,c("gene_name","combined_pip")]
colnames(combined_pip_filtered_plot) <- c("gene_name","combined_pip_csfiltered")
combined_pip_NOTfiltered <- readRDS(paste0(folder_results,"/",trait,"/",trait,".",st,".thin",thin,".",var_struc,".L",L, ".combined_pip_bygroup_final_csF.RDS"))
combined_pip_NOTfiltered_plot <- combined_pip_NOTfiltered[,c("gene_name","combined_pip")]
colnames(combined_pip_NOTfiltered_plot) <- c("gene_name","combined_pip_csNOTfiltered")
combined_pip_filtered_sig <- combined_pip_filtered[combined_pip_filtered$combined_pip > 0.8,]
combined_pip_NOTfiltered_sig <- combined_pip_NOTfiltered[combined_pip_NOTfiltered$combined_pip > 0.8,]
combined_pip_NOTfiltered_sig_plot <- combined_pip_NOTfiltered_plot[combined_pip_NOTfiltered_plot$combined_pip > 0.8,]
df_merge <- merge(combined_pip_NOTfiltered_sig_plot, combined_pip_filtered_plot, by = "gene_name", all.x = T)
df_merge$combined_pip_csfiltered[is.na(df_merge$combined_pip_csfiltered)] <- 1.1
p <-ggplot(data = df_merge,aes(x = combined_pip_csfiltered, y = combined_pip_csNOTfiltered)) +
#p[[trait]] <-ggplot(data = df_merge,aes(x = combined_pip_csfiltered, y = combined_pip_csNOTfiltered)) +
geom_point() +
labs(x = "Combined PIP -- CS filtered", y = "Combined PIP -- CS NOT filtered") +
geom_abline(slope = 1, intercept = 0, col = "Red") +
ggtitle(trait) +
theme_minimal()
print("PIP = 1.1 means PIP = NA when filtering CS")
print(p)
# Merge the two data frames by 'gene_name' with suffixes to distinguish columns
merged_df <- merge(combined_pip_NOTfiltered_sig, combined_pip_filtered,
by = "gene_name", suffixes = c("_NOTfiltered", "_filtered"),all.x=T)
# List of columns to process (excluding 'gene_name')
original_cols <- setdiff(names(combined_pip_NOTfiltered_sig), "gene_name")
# Iterate over each column and concatenate values from both data frames
for (col in original_cols) {
notfiltered_col <- paste0(col, "_NOTfiltered")
filtered_col <- paste0(col, "_filtered")
merged_df[[col]] <- paste(round(merged_df[[notfiltered_col]],digits = 5), round(merged_df[[filtered_col]],digits = 5), sep = "-")
}
# Select the relevant columns to match the original structure
df <- merged_df[, c("gene_name", original_cols)]
cat("\n\n")
cat(knitr::knit_print(DT::datatable(df, caption = htmltools::tags$caption( style = 'font-size: 150%; caption-side: topleft; text-align = left; color:black;', trait),options = list(pageLength = 5))))
cat("\n\n")
}[1] “PIP = 1.1 means PIP = NA when filtering CS” 
[1] “PIP = 1.1 means PIP = NA when filtering CS” 
[1] “PIP = 1.1 means PIP = NA when filtering CS” 
[1] “PIP = 1.1 means PIP = NA when filtering CS” 
[1] “PIP = 1.1 means PIP = NA when filtering CS” 
[1] “PIP = 1.1 means PIP = NA when filtering CS” 
[1] “PIP = 1.1 means PIP = NA when filtering CS” 
#wrap_plots(lapply(p, wrap_elements), ncol = 4)
sessionInfo()R version 4.2.0 (2022-04-22)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Red Hat Enterprise Linux 8.4 (Ootpa)
Matrix products: default
BLAS/LAPACK: /software/openblas-0.3.13-el8-x86_64/lib/libopenblas_skylakexp-r0.3.13.so
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] patchwork_1.1.1 eulerr_7.0.2 ggplot2_3.4.2 tidyr_1.3.0
[5] scales_1.2.0 dplyr_1.1.2
loaded via a namespace (and not attached):
[1] tidyselect_1.2.0 xfun_0.38 bslib_0.3.1 purrr_1.0.1
[5] colorspace_2.0-3 vctrs_0.6.1 generics_0.1.3 htmltools_0.5.7
[9] yaml_2.3.5 utf8_1.2.2 rlang_1.1.2 jquerylib_0.1.4
[13] later_1.3.0 pillar_1.9.0 glue_1.6.2 withr_2.5.0
[17] RColorBrewer_1.1-3 lifecycle_1.0.4 stringr_1.5.0 munsell_0.5.0
[21] gtable_0.3.0 workflowr_1.7.1 htmlwidgets_1.6.2 evaluate_0.15
[25] labeling_0.4.2 knitr_1.42 fastmap_1.1.0 httpuv_1.6.5
[29] crosstalk_1.2.0 fansi_1.0.3 highr_0.9 Rcpp_1.0.14
[33] promises_1.2.0.1 DT_0.22 jsonlite_1.8.9 farver_2.1.0
[37] fs_1.5.2 digest_0.6.29 stringi_1.7.6 polyclip_1.10-0
[41] rprojroot_2.0.3 grid_4.2.0 cli_3.6.2 tools_4.2.0
[45] magrittr_2.0.3 sass_0.4.1 tibble_3.2.1 whisker_0.4
[49] pkgconfig_2.0.3 ellipsis_0.3.2 polylabelr_0.3.0 rmarkdown_2.21
[53] rstudioapi_0.14 R6_2.5.1 git2r_0.30.1 compiler_4.2.0