Left join background data to calculations df

qhtcp-workflow/apps/r/calculate_interaction_zscores.R

@@ -216,7 +216,16 @@ calculate_summary_stats <- function(df, variables, group_vars) {
   return(list(summary_stats = summary_stats, df_with_stats = df_joined))
 }
 
-calculate_interaction_scores <- function(df, bg_stats, group_vars, overlap_threshold = 2) {
+calculate_interaction_scores <- function(df, bg_df, group_vars, overlap_threshold = 2) {
+
+  bg_df_selected <- bg_df %>%
+    select(OrfRep, conc_num, conc_num_factor, conc_num_factor_factor,
+      mean_L, mean_K, mean_r, mean_AUC, sd_L, sd_K, sd_r, sd_AUC
+    )
+
+  df <- df %>%
+    left_join(bg_df_selected, by = c("OrfRep", "conc_num", "conc_num_factor", "conc_num_factor_factor"),
+      suffix = c("", "_bg"))
 
   # Calculate total concentration variables
   total_conc_num <- length(unique(df$conc_num))
@@ -230,15 +239,25 @@ calculate_interaction_scores <- function(df, bg_stats, group_vars, overlap_thres
       SM = sum(SM, na.rm = TRUE),
       num_non_removed_concs = total_conc_num - sum(DB, na.rm = TRUE) - 1,
 
+      # Assign WT values from the background data
+      WT_L = mean_L_bg,
+      WT_K = mean_K_bg,
+      WT_r = mean_r_bg,
+      WT_AUC = mean_AUC_bg,
+      WT_sd_L = sd_L_bg,
+      WT_sd_K = sd_K_bg,
+      WT_sd_r = sd_r_bg,
+      WT_sd_AUC = sd_AUC_bg,
+      
       # Calculate raw data
-      Raw_Shift_L = first(mean_L) - bg_stats$mean_L,
+      Raw_Shift_L = first(mean_L) - first(mean_L_bg),
-      Raw_Shift_K = first(mean_K) - bg_stats$mean_K,
+      Raw_Shift_K = first(mean_K) - first(mean_K_bg),
-      Raw_Shift_r = first(mean_r) - bg_stats$mean_r,
+      Raw_Shift_r = first(mean_r) - first(mean_r_bg),
-      Raw_Shift_AUC = first(mean_AUC) - bg_stats$mean_AUC,
+      Raw_Shift_AUC = first(mean_AUC) - first(mean_AUC_bg),
-      Z_Shift_L = Raw_Shift_L / bg_stats$sd_L,
+      Z_Shift_L = Raw_Shift_L / first(sd_L_bg),
-      Z_Shift_K = Raw_Shift_K / bg_stats$sd_K,
+      Z_Shift_K = Raw_Shift_K / first(sd_K_bg),
-      Z_Shift_r = Raw_Shift_r / bg_stats$sd_r,
+      Z_Shift_r = Raw_Shift_r / first(sd_r_bg),
-      Z_Shift_AUC = Raw_Shift_AUC / bg_stats$sd_AUC,
+      Z_Shift_AUC = Raw_Shift_AUC / first(sd_AUC_bg),
       
       # Expected values
       Exp_L = WT_L + Raw_Shift_L,
@@ -1073,59 +1092,12 @@ main <- function() {
     dir.create(out_dir, recursive = TRUE, showWarnings = FALSE)
     dir.create(out_dir_qc, recursive = TRUE, showWarnings = FALSE)
     
+    # Each list of plots corresponds to a separate file
     message("Loading and filtering data for experiment: ", exp_name)
     df <- load_and_filter_data(args$easy_results_file, sd = exp_sd) %>%
       update_gene_names(args$sgd_gene_list) %>%
       as_tibble()
 
-    message("Calculating summary statistics before quality control")
-    df_stats <- calculate_summary_stats(
-      df = df,
-      variables = c("L", "K", "r", "AUC", "delta_bg"),
-      group_vars = c("conc_num", "conc_num_factor", "conc_num_factor_factor"))$df_with_stats
-   
-    message("Calculating summary statistics after quality control")
-    df_na <- df %>% mutate(across(all_of(c("L", "K", "r", "AUC", "delta_bg")), ~ ifelse(DB == 1, NA, .))) # formerly X
-    ss <- calculate_summary_stats(
-      df = df_na,
-      variables = c("L", "K", "r", "AUC", "delta_bg"),
-      group_vars = c("conc_num", "conc_num_factor", "conc_num_factor_factor"))
-    df_na_ss <- ss$summary_stats
-    df_na_stats <- ss$df_with_stats # formerly X_stats_ALL
-    write.csv(df_na_ss, file = file.path(out_dir, "summary_stats_all_strains.csv"), row.names = FALSE)
-    # For plotting (ggplot warns on NAs)
-    df_na_stats_filtered <- df_na_stats %>% filter(if_all(all_of(c("L", "K", "r", "AUC", "delta_bg")), is.finite))
-
-    message("Calculating summary statistics after quality control excluding zero values")
-    df_no_zeros <- df_na %>% filter(L > 0) # formerly X_noZero
-    df_no_zeros_stats <- calculate_summary_stats(
-      df = df_no_zeros,
-      variables = c("L", "K", "r", "AUC", "delta_bg"),
-      group_vars = c("conc_num", "conc_num_factor", "conc_num_factor_factor")
-    )$df_with_stats
-
-    message("Filtering by 2SD of K")
-    df_na_within_2sd_k <- df_na_stats %>%
-      filter(K >= (mean_K - 2 * sd_K) & K <= (mean_K + 2 * sd_K))
-    df_na_outside_2sd_k <- df_na_stats %>%
-      filter(K < (mean_K - 2 * sd_K) | K > (mean_K + 2 * sd_K))
-
-    message("Calculating summary statistics for L within 2SD of K")
-    # TODO We're omitting the original z_max calculation, not sure if needed?
-    ss <- calculate_summary_stats(df_na_within_2sd_k, "L",
-      group_vars = c("conc_num", "conc_num_factor", "conc_num_factor_factor"))$summary_stats
-    write.csv(ss,
-      file = file.path(out_dir_qc, "max_observed_L_vals_for_spots_within_2sd_K.csv"),
-      row.names = FALSE)
-    
-    message("Calculating summary statistics for L outside 2SD of K")
-    ss <- calculate_summary_stats(df_na_outside_2sd_k, "L", group_vars = c("conc_num", "conc_num_factor", "conc_num_factor_factor"))
-    df_na_l_outside_2sd_k_stats <- ss$df_with_stats
-    write.csv(ss$summary_stats,
-      file = file.path(out_dir, "max_observed_L_vals_for_spots_outside_2sd_K.csv"),
-      row.names = FALSE)
-      
-    # Each list of plots corresponds to a file
     l_vs_k_plot_configs <- list(
       plots = list(
         list(
@@ -1142,6 +1114,12 @@ main <- function() {
       )
     )
 
+    message("Calculating summary statistics before quality control")
+    df_stats <- calculate_summary_stats(
+      df = df,
+      variables = c("L", "K", "r", "AUC", "delta_bg"),
+      group_vars = c("conc_num", "conc_num_factor", "conc_num_factor_factor"))$df_with_stats
+
     frequency_delta_bg_plot_configs <- list(
       plots = list(
         list(
@@ -1171,7 +1149,9 @@ main <- function() {
       )
     )
 
+    message("Filtering rows above delta background tolerance for plotting")
     df_above_tolerance <- df %>% filter(DB == 1)
+
     above_threshold_plot_configs <- list(
       plots = list(
         list(
@@ -1197,6 +1177,49 @@ main <- function() {
       )
     )
    
+    message("Setting rows above delta background tolerance to NA")
+    df_na <- df %>% mutate(across(all_of(c("L", "K", "r", "AUC", "delta_bg")), ~ ifelse(DB == 1, NA, .))) # formerly X
+    
+    message("Calculating summary statistics across all strains")
+    ss <- calculate_summary_stats(
+      df = df_na,
+      variables = c("L", "K", "r", "AUC", "delta_bg"),
+      group_vars = c("conc_num", "conc_num_factor", "conc_num_factor_factor"))
+    df_na_ss <- ss$summary_stats
+    df_na_stats <- ss$df_with_stats # formerly X_stats_ALL
+    write.csv(df_na_ss, file = file.path(out_dir, "summary_stats_all_strains.csv"), row.names = FALSE)
+    # This can help bypass missing values ggplot warnings during testing
+    df_na_stats_filtered <- df_na_stats %>% filter(if_all(all_of(c("L", "K", "r", "AUC", "delta_bg")), is.finite))
+
+    message("Calculating summary statistics excluding zero values")
+    df_no_zeros <- df_na %>% filter(L > 0) # formerly X_noZero
+    df_no_zeros_stats <- calculate_summary_stats(
+      df = df_no_zeros,
+      variables = c("L", "K", "r", "AUC", "delta_bg"),
+      group_vars = c("conc_num", "conc_num_factor", "conc_num_factor_factor")
+    )$df_with_stats
+
+    message("Filtering by 2SD of K")
+    df_na_within_2sd_k <- df_na_stats %>%
+      filter(K >= (mean_K - 2 * sd_K) & K <= (mean_K + 2 * sd_K))
+    df_na_outside_2sd_k <- df_na_stats %>%
+      filter(K < (mean_K - 2 * sd_K) | K > (mean_K + 2 * sd_K))
+
+    message("Calculating summary statistics for L within 2SD of K")
+    # TODO We're omitting the original z_max calculation, not sure if needed?
+    ss <- calculate_summary_stats(df_na_within_2sd_k, "L",
+      group_vars = c("conc_num", "conc_num_factor", "conc_num_factor_factor"))$summary_stats
+    write.csv(ss,
+      file = file.path(out_dir_qc, "max_observed_L_vals_for_spots_within_2sd_K.csv"),
+      row.names = FALSE)
+    
+    message("Calculating summary statistics for L outside 2SD of K")
+    ss <- calculate_summary_stats(df_na_outside_2sd_k, "L", group_vars = c("conc_num", "conc_num_factor", "conc_num_factor_factor"))
+    df_na_l_outside_2sd_k_stats <- ss$df_with_stats
+    write.csv(ss$summary_stats,
+      file = file.path(out_dir, "max_observed_L_vals_for_spots_outside_2sd_K.csv"),
+      row.names = FALSE)
+
     plate_analysis_plot_configs <- generate_plate_analysis_plot_configs(
       variables = c("L", "K", "r", "AUC", "delta_bg"),
       df_before = df_stats,
@@ -1303,7 +1326,6 @@ main <- function() {
 
     bg_strains <- c("YDL227C")
     lapply(bg_strains, function(strain) {
-      
       message("Processing background strain: ", strain)
       
       # Handle missing data by setting zero values to NA
@@ -1318,19 +1340,18 @@ main <- function() {
         ) %>%
         filter(!is.na(L))
       
-      # Recalculate summary statistics for the background strain
       message("Calculating summary statistics for background strain")
       ss_bg <- calculate_summary_stats(df_bg, c("L", "K", "r", "AUC", "delta_bg"),
         group_vars = c("OrfRep", "conc_num", "conc_num_factor", "conc_num_factor_factor"))
       summary_stats_bg <- ss_bg$summary_stats
-      ss_bg_stats <- ss_bg$df_with_stats
+      df_bg_stats <- ss_bg$df_with_stats
-      write.csv(summary_stats_bg,
+      write.csv(
+        summary_stats_bg,
         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
+      message("Setting missing reference values to the highest theoretical value at each drug conc for L")
-      # Leave other values as 0 for the max/min
+      df_reference <- df_na_stats %>% # formerly X2_RF
-      df_reference <- df_bg_stats %>% # formerly X2_RF
         filter(OrfRep == strain) %>%
         filter(!is.na(L)) %>%
         group_by(conc_num, conc_num_factor, conc_num_factor_factor) %>%
@@ -1341,11 +1362,21 @@ main <- function() {
           L = ifelse(L >= max_l_theoretical & !is.na(L) & conc_num > 0, max_l_theoretical, L)) %>%
         ungroup()
 
-      # Ditto for deletion strains
+      message("Calculating reference strain interaction scores")
+      df_reference_stats <- calculate_summary_stats(
+        df = df_reference,
+        variables = c("L", "K", "r", "AUC"),
+        group_vars = c("OrfRep", "Gene", "num", "conc_num", "conc_num_factor")
+        )$df_with_stats
+      reference_results <- calculate_interaction_scores(df_reference_stats, df_bg_stats, group_vars = c("OrfRep", "Gene", "num"))
+      zscore_calculations_reference <- reference_results$calculations
+      zscore_interactions_reference <- reference_results$interactions
+      zscore_interactions_reference_joined <- reference_results$full_data
+
+      message("Setting missing deletion values to the highest theoretical value at each drug conc for L")
       df_deletion <- df_na_stats %>% # formerly X2
         filter(OrfRep != strain) %>%
         filter(!is.na(L)) %>%
-        mutate(SM = 0) %>%
         group_by(conc_num, conc_num_factor, conc_num_factor_factor) %>%
         mutate(
           max_l_theoretical = max(max_L, na.rm = TRUE),
@@ -1354,24 +1385,13 @@ main <- function() {
           L = ifelse(L >= max_l_theoretical & !is.na(L) & conc_num > 0, max_l_theoretical, L)) %>%
         ungroup()
 
-      message("Calculating reference strain interaction scores")
-      df_reference_stats <- calculate_summary_stats(
-        df = df_reference,
-        variables = c("L", "K", "r", "AUC"),
-        group_vars = c("OrfRep", "Gene", "num", "conc_num", "conc_num_factor")
-        )$df_with_stats
-      reference_results <- calculate_interaction_scores(df_reference_stats, bg_stats, group_vars = c("OrfRep", "Gene", "num"))
-      zscore_calculations_reference <- reference_results$calculations
-      zscore_interactions_reference <- reference_results$interactions
-      zscore_interactions_reference_joined <- reference_results$full_data
-
       message("Calculating deletion strain(s) interactions scores")
       df_deletion_stats <- calculate_summary_stats(
         df = df_deletion,
         variables = c("L", "K", "r", "AUC"),
         group_vars = c("OrfRep", "Gene", "conc_num", "conc_num_factor")
         )$df_with_stats
-      deletion_results <- calculate_interaction_scores(df_deletion_stats, bg_stats, group_vars = c("OrfRep", "Gene"))
+      deletion_results <- calculate_interaction_scores(df_deletion_stats, df_bg_stats, group_vars = c("OrfRep", "Gene"))
       zscore_calculations <- deletion_results$calculations
       zscore_interactions <- deletion_results$interactions
       zscore_interactions_joined <- deletion_results$full_data