Enable QC plots for comparison

qhtcp-workflow/apps/r/calculate_interaction_zscores.R

@@ -1232,9 +1232,9 @@ main <- function() {
     )
 
     # Generating quality control plots in parallel
-    # furrr::future_map(plot_configs, function(config) {
-    #   generate_and_save_plots(config$out_dir, config$filename, config$plot_configs)
-    # }, .options = furrr_options(seed = TRUE))
+    furrr::future_map(plot_configs, function(config) {
+      generate_and_save_plots(config$out_dir, config$filename, config$plot_configs)
+    }, .options = furrr_options(seed = TRUE))
 
     # Process background strains
     bg_strains <- c("YDL227C")
@@ -1259,7 +1259,7 @@ main <- function() {
       ss_bg <- calculate_summary_stats(df_bg, summary_vars, group_vars = c("OrfRep", "conc_num", "conc_num_factor"))
       summary_stats_bg <- ss_bg$summary_stats
       write.csv(summary_stats_bg,
-        file = file.path(out_dir, paste0("SummaryStats_BackgroundStrains_", strain, ".csv")),
+        file = file.path(out_dir, paste0("summary_stats_background_strain_", strain, ".csv")),
         row.names = FALSE)
       
       # Set the missing values to the highest theoretical value at each drug conc for L
@@ -1449,7 +1449,7 @@ main <- function() {
       correlation_plot_configs <- generate_correlation_plot_configs(zscores_interactions_filtered)
       generate_and_save_plots(
         out_dir = out_dir,
-        filename = "Avg_Zscore_vs_lm_NA_rm",
+        filename = "Correlation_CPPs",
         plot_configs = correlation_plot_configs,
         grid_layout = list(ncol = 2, nrow = 2))
     })