diff --git a/workflow/apps/r/calculate_interaction_zscores5.R b/workflow/apps/r/calculate_interaction_zscores5.R
index e4efb574..0a91dab2 100644
--- a/workflow/apps/r/calculate_interaction_zscores5.R
+++ b/workflow/apps/r/calculate_interaction_zscores5.R
@@ -205,8 +205,6 @@ calculate_summary_stats <- function(df, variables, group_vars = c("conc_num", "c
   return(summary_stats)
 }
 
-
-
 calculate_interaction_scores <- function(df, max_conc, variables, group_vars = c("OrfRep", "Gene", "num")) {
 
   # Calculate total concentration variables
@@ -329,6 +327,37 @@ calculate_interaction_scores <- function(df, max_conc, variables, group_vars = c
   return(list(zscores_calculations = interaction_scores_all, zscores_interactions = interaction_scores))
 }
 
+interaction_plot_configs <- function(df, variable) {
+  ylim_vals <- switch(variable,
+    "L" = c(-65, 65),
+    "K" = c(-65, 65),
+    "r" = c(-0.65, 0.65),
+    "AUC" = c(-6500, 6500),
+    NULL
+  )
+
+  wt_sd <- paste0("WT_sd_", variable)
+  delta_var <- paste0("Delta_", variable)
+  z_shift <- paste0("Z_Shift_", variable)
+  z_lm <- paste0("Z_lm_", variable)
+
+  list(
+    x_var = "conc_num_factor",
+    y_var = delta_var,
+    plot_type = "scatter",
+    title = paste("Scatter plot for", variable),
+    ylim_vals = ylim_vals,
+    annotations = list(
+      list(x = 1, y = max(ylim_vals) * 0.7, label = paste("ZShift =", round(df[[z_shift]], 2))),
+      list(x = 1, y = max(ylim_vals) * 0.5, label = paste("Z lm Score =", round(df[[z_lm]], 2))),
+      list(x = 1, y = min(ylim_vals) * 0.7, label = paste("NG =", df$NG)),
+      list(x = 1, y = min(ylim_vals) * 0.85, label = paste("DB =", df$DB)),
+      list(x = 1, y = min(ylim_vals) * 1.1, label = paste("SM =", df$SM))
+    )
+  )
+}
+
+
 generate_plot <- function(df, x_var, y_var = NULL, plot_type, color_var = "conc_num",
   title, x_label = NULL, y_label = NULL, ylim_vals = NULL) {
 
@@ -360,7 +389,7 @@ generate_plot <- function(df, x_var, y_var = NULL, plot_type, color_var = "conc_
   return(plot)
 }
 
-generate_and_save_plots <- function(df, output_dir, file_prefix, variables, plot_type = "scatter", include_qc = FALSE, ylim_vals = NULL) {
+generate_and_save_plots <- function(df, output_dir, file_prefix, plot_configs) {
   plots <- list()
   
   if (nrow(df) == 0) {
@@ -370,29 +399,35 @@ generate_and_save_plots <- function(df, output_dir, file_prefix, variables, plot
   
   message("Generating plots for \"", deparse(substitute(df)), "\" dataframe")
   
-  # Create plots for the given variables
-  for (var in variables) {
+  for (config in plot_configs) {
     plot <- generate_plot(
       df = df,
-      x_var = "scan",
-      y_var = var,
-      plot_type = plot_type,
-      title = paste(file_prefix, "Plot for", var),
-      ylim_vals = ylim_vals
+      x_var = config$x_var,
+      y_var = config$y_var,
+      plot_type = config$plot_type,
+      title = config$title,
+      ylim_vals = config$ylim_vals
     )
-    plots[[paste0(var, "_", plot_type)]] <- plot
+    
+    # If custom annotations for interaction plots are required
+    if (!is.null(config$annotations)) {
+      for (annotation in config$annotations) {
+        plot <- plot + 
+          annotate(
+            "text", x = annotation$x, y = annotation$y, label = annotation$label
+          )
+      }
+    }
+    
+    # Store the plot with the variable name and plot type
+    plots[[paste0(config$y_var, "_", config$plot_type)]] <- plot
   }
-  
-  # Include additional QC plots if requested
-  if (include_qc) {
-    plots[["Raw_L_vs_K"]] <- generate_plot(df, "L", "K", "scatter", title = "Raw L vs K before QC")
-    plots[["Delta_bg_Density"]] <- generate_plot(df, "delta_bg", NULL, "density", title = "Density plot for Delta Background")
-    plots[["Delta_bg_Bar"]] <- generate_plot(df, "delta_bg", NULL, "bar", title = "Bar plot for Delta Background")
-  }
-  
+
   save_plots(file_prefix, plots, output_dir)
 }
 
+
+
 # Ensure all plots are saved and printed to PDF
 save_plots <- function(file_name, plot_list, output_dir) {
   # Save to PDF
@@ -613,9 +648,22 @@ main <- function() {
     
     # QC steps and filtering
     df_above_tolerance <- df %>% filter(DB == 1)
-    df_na <- df %>% mutate(across(c(L, r, AUC, K), ~ ifelse(DB == 1, NA, .x)))
+    df_na <- df %>% mutate(across(c(L, r, AUC, K), ~ ifelse(DB == 1, NA, .)))
     df_no_zeros <- df_na %>% filter(L > 0)
-    
+
+    # Calculate the half-medians for `L` and `K` for rows above tolerance
+    L_half_median <- (median(df_above_tolerance$L, na.rm = TRUE)) / 2
+    K_half_median <- (median(df_above_tolerance$K, na.rm = TRUE)) / 2
+
+    # Get the number of rows that are above tolerance
+    rows_to_remove <- nrow(df_above_tolerance)
+
+    # Logging or handling the calculated values, e.g.:
+    message("Half-median for L (above tolerance): ", L_half_median)
+    message("Half-median for K (above tolerance): ", K_half_median)
+    message("Number of rows above tolerance: ", rows_to_remove)
+
+    # Additional filtering for non-finite values in df_na
     df_na_filtered <- df_na %>%
       filter(if_any(c(L), ~ !is.finite(.))) %>%
       {
@@ -626,15 +674,36 @@ main <- function() {
         df_na %>% filter(if_all(c(L), is.finite))
       }
 
-    # Generate and save QC plots using the new generalized function
-    # 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_filtered, out_dir_qc, "After_QC", variables)
-    # generate_and_save_plots(df_no_zeros, out_dir_qc, "No_Zeros", variables)
+    # Define the plot configurations for each QC step
+    qc_plot_configs <- list(
+      list(x_var = "L", y_var = "K", plot_type = "scatter", title = "Raw L vs K before QC", ylim_vals = NULL),
+      list(x_var = "delta_bg", y_var = NULL, plot_type = "density", title = "Density plot for Delta Background", ylim_vals = NULL),
+      list(x_var = "delta_bg", y_var = NULL, plot_type = "bar", title = "Bar plot for Delta Background", ylim_vals = NULL)
+    )
+
+    above_tolerance_plot_configs <- list(
+      list(x_var = "L", y_var = "K", plot_type = "scatter", title = "L vs K for Rows Above Delta Background Threshold", ylim_vals = NULL)
+    )
+
+    after_qc_plot_configs <- list(
+      list(x_var = "L", y_var = "K", plot_type = "scatter", title = "L vs K After QC Filtering", ylim_vals = NULL)
+    )
+
+    no_zeros_plot_configs <- list(
+      list(x_var = "L", y_var = "K", plot_type = "scatter", title = "L vs K for Non-Zero L Values", ylim_vals = NULL)
+    )
+
+    # Generate and save plots for each QC step
+    message("Generating QC plots")
+    generate_and_save_plots(df, out_dir_qc, "Before_QC", qc_plot_configs)
+    generate_and_save_plots(df_above_tolerance, out_dir_qc, "Above_Tolerance", above_tolerance_plot_configs)
+    generate_and_save_plots(df_na_filtered, out_dir_qc, "After_QC", after_qc_plot_configs)
+    generate_and_save_plots(df_no_zeros, out_dir_qc, "No_Zeros", no_zeros_plot_configs)
+
+
+  # Clean up
+  rm(df, df_above_tolerance, df_no_zeros)
 
-    rm(df, df_above_tolerance, df_no_zeros)
     
     # Calculate summary statistics
     message("Calculating summary statistics for all strains")
@@ -790,11 +859,20 @@ main <- function() {
       write.csv(suppressors_lm_K,
         file = file.path(out_dir, "ZScores_Interaction_DeletionSuppressors_K_lm.csv"), row.names = FALSE)
       
-      # Generate plots for interaction scores
-      # generate_rf_plots(zscores_calculations_reference, zscores_interactions_reference, out_dir)
-      # generate_rf_plots(zscores_calculations, zscores_interactions, out_dir)
-      generate_and_save_plots(zscores_calculations_reference, out_dir, "Reference_Calculations", variables)
-      generate_and_save_plots(zscores_calculations, out_dir, "Deletion_Calculations", variables)
+      # Interaction plots for reference strain
+      variables <- c("L", "K", "r", "AUC")
+      reference_plot_configs <- lapply(variables, function(var) {
+        interaction_plot_configs(zscores_calculations_reference, var)
+      })
+
+      generate_and_save_plots(zscores_calculations_reference, out_dir, "RF_InteractionPlots", reference_plot_configs)
+
+      # Interaction plots for deletion strains
+      deletion_plot_configs <- lapply(variables, function(var) {
+        interaction_plot_configs(zscores_calculations, var)
+      })
+
+      generate_and_save_plots(zscores_calculations, out_dir, "InteractionPlots", deletion_plot_configs)
 
       # Apply filtering to remove NA values before further analysis
       zscores_interactions_filtered <- zscores_interactions %>%