Add more output to main() to aid debug

workflow/apps/r/calculate_interaction_zscores5.R

@@ -564,7 +564,16 @@ main <- function() {
       )
     df_no_zeros <- df_na %>% filter(L > 0)
 
+    # Generate QC PDFs and HTMLs
+    message("Generating QC plots")
+    variables <- c("L", "K", "r", "AUC", "delta_bg")
+    generate_and_save_plots(df, out_dir_qc, "Before_QC", variables, include_qc = TRUE)
+    generate_and_save_plots(df_above_tolerance, out_dir_qc, "Raw_L_vs_K_above_delta_bg_threshold", variables, include_qc = TRUE)
+    generate_and_save_plots(df_na, out_dir_qc, "After_QC", variables)
+    generate_and_save_plots(df_no_zeros, out_dir_qc, "No_Zeros", variables)
+
     # Calculate summary statistics
+    message("Calculating summary statistics for all strains")
     variables <- c("L", "K", "r", "AUC")
     summary_stats <- calculate_summary_stats(df_na, variables, group_vars = c("conc_num", "conc_num_factor"))
     write.csv(summary_stats, file = file.path(out_dir, "SummaryStats_ALLSTRAINS.csv"), row.names = FALSE)
@@ -575,17 +584,12 @@ main <- function() {
     stats_by_r <- summary_stats %>% select(starts_with("r_"), "OrfRep", "conc_num", "conc_num_factor")
     stats_by_auc <- summary_stats %>% select(starts_with("AUC_"), "OrfRep", "conc_num", "conc_num_factor")
 
-    # Generate QC PDFs and HTMLs
-    variables <- c("L", "K", "r", "AUC", "delta_bg")
-    generate_and_save_plots(df, out_dir_qc, "Before_QC", variables, include_qc = TRUE)
-    generate_and_save_plots(df_above_tolerance, out_dir_qc, "Raw_L_vs_K_above_delta_bg_threshold", variables, include_qc = TRUE)
-    generate_and_save_plots(df_na, out_dir_qc, "After_QC", variables)
-    generate_and_save_plots(df_no_zeros, out_dir_qc, "No_Zeros", variables)
-    
     # Process background strains
     background_strains <- c("YDL227C")
     lapply(background_strains, function(strain) {
 
+      message("Processing background strain: ", strain)
+      
       # Handle missing data by setting zero values to NA
       # and then removing any rows with NA in L col
       df_background <- df_na %>%
@@ -599,73 +603,72 @@ main <- function() {
         filter(!is.na(L))
 
       # Recalculate summary statistics for the background strain
+      message("Calculating summary statistics for background strain")
       stats_background <- calculate_summary_stats(df_background, variables, group_vars = c("OrfRep", "Gene", "conc_num", "conc_num_factor"))
       stats_by_l_background <- stats_background %>% select(starts_with("L_"), "OrfRep", "conc_num", "conc_num_factor")
       stats_by_k_background <- stats_background %>% select(starts_with("K_"), "OrfRep", "conc_num", "conc_num_factor")
       stats_by_r_background <- stats_background %>% select(starts_with("r_"), "OrfRep", "conc_num", "conc_num_factor")
       stats_by_auc_background <- stats_background %>% select(starts_with("AUC_"), "OrfRep", "conc_num", "conc_num_factor")
-
-      # Backup in case previous block explodes
-      # Combine all summary statistics into one dataframe
-      # df_stats_background <- stats_l %>%
-      # left_join(stats_k, by = c("OrfRep", "conc_num", "conc_num_factor")) %>%
-      # left_join(stats_r, by = c("OrfRep", "conc_num", "conc_num_factor")) %>%
-      # left_join(stats_auc, by = c("OrfRep", "conc_num", "conc_num_factor"))
-  
-      # Save the summary statistics for the background strain
       write.csv(stats_background,
         file = file.path(output_dir, paste0("SummaryStats_BackgroundStrains_", strain, ".csv")),
         row.names = FALSE)
 
-      # Calculate L values within and outside 2SD of K
+      # Filter L values within and outside 2SD of K
       results_2sd <- calculate_l_2sd_of_k(df_background, stats_by_k_background)
       within_2sd_k <- results_2sd$within_2sd_k
       outside_2sd_k <- results_2sd$outside_2sd_k
 
       # Calculate summary statistics for L within and outside 2SD of K
+      message("Calculating summary statistics for for L within 2SD of K")
       l_within_2sd_k <- calculate_summary_stats(within_2sd_k, "L", group_vars = c("conc_num", "conc_num_factor"))
       write.csv(l_within_2sd_k,
         file = file.path(out_dir, "Max_Observed_L_Vals_for_spots_within_2sd_k.csv"),
         row.names = FALSE)
-      
+      message("Calculating summary statistics for for L outside 2SD of K")
       l_outside_2sd_k <- calculate_summary_stats(outside_2sd_k, "L", group_vars = c("conc_num", "conc_num_factor"))
       write.csv(l_outside_2sd_k,
         file = file.path(out_dir, "Max_Observed_L_Vals_for_spots_outside_2sd_k.csv"),
         row.names = FALSE)
 
+      message("Generating Raw_L_vs_K_for_strains_outside_2sd_k plots")
       generate_and_save_plots(outside_2sd_k, out_dir, "Raw_L_vs_K_for_strains_outside_2sd_k")
 
+      message("Calculating background means")
       background_means <- calculate_background_means(stats_by_l_background,
         stats_by_k_background, stats_by_r_background, stats_by_auc_background)
 
-      # Formerly X2_RF
+      # Filter reference and deletion strains
+      # Formerly X2_RF (reference strain)
       df_reference <- df_na %>%
         filter(OrfRep == strain) %>%
         mutate(SM = 0)
 
-      # Formerly X2
+      # Formerly X2 (deletion strains)
       df_deletion <- df_na %>%
         filter(OrfRep != strain) %>%
         mutate(SM = 0)
       
-      df_reference_strains <- process_strains(df_reference, l_within_2sd_k, strain, out_dir)
+      message("Processing reference strain")
-      df_deletion_strains <- process_strains(df_deletion, l_within_2sd_k, strain, out_dir)
+      reference_strain <- process_strains(df_reference, l_within_2sd_k, strain, out_dir)
-
+      message("Processing deletion strains")
-      variables <- c("L", "K", "r", "AUC")
+      deletion_strains <- process_strains(df_deletion, l_within_2sd_k, strain, out_dir)
 
       # Deprecated
       # Change OrfRep to include the reference strain, gene, and Num so each RF gets its own score
-      # df_reference_strains <- df_reference_strains %>%
+      # reference_strain <- reference_strain %>%
       #   mutate(OrfRep = paste(OrfRep, Gene, num, sep = "_"))
       # We are leaving OrfRep unchanged and using group_by(OrfRep, Gene, num) by default
       # This is synonymous with the legacy OrfRep mutation
       # Use group_by in functions in lieu of mutating OrfRep
       # default_group_vars <- c("OrfRep", "Gene", "num")
 
-      reference_results <- calculate_interaction_scores(df_reference_strains, stats_by_l,
+      # Calculate interactions
+      variables <- c("L", "K", "r", "AUC")
+      message("Calculating reference interaction scores")
+      reference_results <- calculate_interaction_scores(reference_strain, stats_by_l,
         stats_by_k, stats_by_r, stats_by_auc, background_means, max_conc, variables)
-
+      message("Calculating deletion interaction scores")
-      deletion_results <- calculate_interaction_scores(df_deletion_strains, stats_by_l,
+      deletion_results <- calculate_interaction_scores(deletion_strains, stats_by_l,
         stats_by_k, stats_by_r, stats_by_auc, background_means, max_conc, variables)
 
       zscores_calculations_reference <- reference_results$zscores_calculations
@@ -673,12 +676,12 @@ main <- function() {
       zscores_calculations <- deletion_results$zscores_calculations
       zscores_interactions <- deletion_results$zscores_interactions
  
-
       # TODO: I don't know if we need this?
       zscores_interactions <- zscores_interactions %>%
         arrange(desc(Z_lm_L)) %>%
         arrange(desc(NG))
 
+      message("Writing zscores csv files")
       write.csv(zscores_calculations_reference, file = file.path(out_dir, "RF_ZScores_Calculations.csv"), row.names = FALSE)
       write.csv(zscores_calculations, file = file.path(out_dir, "ZScores_Calculations.csv"), row.names = FALSE)
       write.csv(zscores_interactions_reference, file = file.path(out_dir, "RF_ZScores_Interaction.csv"), row.names = FALSE)
@@ -697,6 +700,7 @@ main <- function() {
       suppressors_K <- zscores_interactions[suppressor_condition_K, ]
 
       # Save enhancers and suppressors
+      message("Writing enhancer/suppressor csv files")
       write.csv(enhancers_L, file = file.path(out_dir, "ZScores_Interaction_DeletionEnhancers_L.csv"), row.names = FALSE)
       write.csv(suppressors_L, file = file.path(out_dir, "ZScores_Interaction_DeletionSuppressors_L.csv"), row.names = FALSE)
       write.csv(enhancers_K, file = file.path(out_dir, "ZScores_Interaction_DeletionEnhancers_K.csv"), row.names = FALSE)
@@ -719,6 +723,7 @@ main <- function() {
       suppressors_lm_K <- zscores_interactions[zscores_interactions$Z_lm_K <= -2, ]
 
       # Save linear model based enhancers and suppressors
+      message("Writing linear model enhancer/suppressor csv files")
       write.csv(enhancers_lm_L,
         file = file.path(out_dir, "ZScores_Interaction_DeletionEnhancers_L_lm.csv"), row.names = FALSE)
       write.csv(suppressors_lm_L,