Refactor interaction plot configs

qhtcp-workflow/apps/r/calculate_interaction_zscores.R

@@ -578,102 +578,99 @@ generate_interaction_plot_configs <- function(df, variables) {
     r = c(-0.65, 0.65),
     AUC = c(-6500, 6500)
   )
-  
-  # Define functions to generate annotation labels
-  annotation_labels <- list(
-    ZShift = function(df, var) {
-      val <- df[[paste0("Z_Shift_", var)]]
-      paste("ZShift =", round(val, 2))
-    },
-    lm_ZScore = function(df, var) {
-      val <- df[[paste0("Z_lm_", var)]]
-      paste("lm ZScore =", round(val, 2))
-    },
-    NG = function(df, var) paste("NG =", df$NG),
-    DB = function(df, var) paste("DB =", df$DB),
-    SM = function(df, var) paste("SM =", df$SM)
-  )
 
-  results <- filter_data_for_plots(df, variables, limits_map)
+  df_filtered <- filter_data_for_plots(df, variables, limits_map)$filtered_data
-  df_filtered <- results$df_filtered
+
-  lm_lines <- filtered_results$lm_lines
+  # Define annotation label functions
-  
+  generate_annotation_labels <- function(df, var, annotation_name) {
-  # Iterate over each variable to create plot configurations
+    switch(annotation_name,
-  for (variable in variables) {
+      ZShift = paste("ZShift =", round(df[[paste0("Z_Shift_", var)]], 2)),
-    
+      lm_ZScore = paste("lm ZScore =", round(df[[paste0("Z_lm_", var)]], 2)),
-    # Calculate x and y positions for annotations based on filtered data
+      NG = paste("NG =", df$NG),
-    x_levels <- levels(df_filtered$conc_num_factor)
+      DB = paste("DB =", df$DB),
-    num_levels <- length(x_levels)
+      SM = paste("SM =", df$SM),
-    x_pos <- (1 + num_levels) / 2  # Midpoint of x-axis positions
+      NULL  # Default case for unrecognized annotation names
-    
+    )
-    y_range <- limits_map[[variable]]
+  }
+
+  # Define annotation positions relative to the y-axis range
+  calculate_annotation_positions <- function(y_range) {
     y_min <- min(y_range)
     y_max <- max(y_range)
     y_span <- y_max - y_min
-    
+
-    # Adjust y positions as fractions of y-span
+    list(
-    annotation_positions <- list(
       ZShift = y_max - 0.1 * y_span,
       lm_ZScore = y_max - 0.2 * y_span,
       NG = y_min + 0.2 * y_span,
       DB = y_min + 0.1 * y_span,
       SM = y_min + 0.05 * y_span
     )
-    
+  }
+
+  # Create configurations for each variable
+  for (variable in variables) {
+
+    y_range <- limits_map[[variable]]
+    annotation_positions <- calculate_annotation_positions(y_range)
+    lm_line <- list(
+      intercept = df_filtered[[paste0("lm_intercept_", variable)]],
+      slope = df_filtered[[paste0("lm_slope_", variable)]]
+    )
+
+    # Determine x-axis midpoint
+    num_levels <- length(levels(df_filtered$conc_num_factor))
+    x_pos <- (1 + num_levels) / 2  # Midpoint of x-axis
+
     # Generate annotations
     annotations <- lapply(names(annotation_positions), function(annotation_name) {
+      label <- generate_annotation_labels(df_filtered, variable, annotation_name)
       y_pos <- annotation_positions[[annotation_name]]
-      label_func <- annotation_labels[[annotation_name]]
+      if (!is.null(label)) {
-      if (!is.null(label_func)) {
-        label <- label_func(df_filtered, variable)
         list(x = x_pos, y = y_pos, label = label)
       } else {
-        message(paste("Warning: No annotation function found for", annotation_name))
+        message(paste("Warning: No annotation found for", annotation_name))
         NULL
       }
     })
-    
+
     # Remove NULL annotations
     annotations <- Filter(Negate(is.null), annotations)
-    
+
-    # Create scatter plot config
+    # Shared plot settings
-    configs[[length(configs) + 1]] <- list(
+    plot_settings <- list(
       df = df_filtered,
       x_var = "conc_num_factor",
       y_var = variable,
-      plot_type = "scatter",
-      title = sprintf("%s      %s", df_filtered$OrfRep[1], df_filteredGene[1]),
       ylim_vals = y_range,
       annotations = annotations,
-      lm_line = lm_lines[[variable]],
+      lm_line = lm_line,
-      error_bar = TRUE,
-      x_breaks = levels(df_filtered$conc_num_factor),
-      x_labels = levels(df_filtered$conc_num_factor),
-      x_label = unique(df$Drug[1]),
-      position = "jitter",
-      coord_cartesian = y_range  # Use the actual y-limits
-    )
-    
-    # Create box plot config
-    configs[[length(configs) + 1]] <- list(
-      df = df_filtered,
-      x_var = "conc_num_factor",
-      y_var = variable,
-      plot_type = "box",
-      title = sprintf("%s      %s (Boxplot)", df_filtered$OrfRep[1], df_filtered$Gene[1]),
-      ylim_vals = y_range,
-      annotations = annotations,
-      error_bar = FALSE,
       x_breaks = levels(df_filtered$conc_num_factor),
       x_labels = levels(df_filtered$conc_num_factor),
       x_label = unique(df_filtered$Drug[1]),
-      coord_cartesian = y_range
+      coord_cartesian = y_range  # Use the actual y-limits
     )
+
+    # Scatter plot config
+    configs[[length(configs) + 1]] <- modifyList(plot_settings, list(
+      plot_type = "scatter",
+      title = sprintf("%s      %s", df_filtered$OrfRep[1], df_filtered$Gene[1]),
+      error_bar = TRUE,
+      position = "jitter"
+    ))
+
+    # Box plot config
+    configs[[length(configs) + 1]] <- modifyList(plot_settings, list(
+      plot_type = "box",
+      title = sprintf("%s      %s (Boxplot)", df_filtered$OrfRep[1], df_filtered$Gene[1]),
+      error_bar = FALSE
+    ))
   }
-  
+
   return(configs)
 }
 
+
 generate_rank_plot_configs <- function(df, interaction_vars, rank_vars = c("L", "K"), is_lm = FALSE, adjust = FALSE) {
 
   for (var in interaction_vars) {
@@ -789,7 +786,7 @@ filter_and_print_non_finite <- function(df, vars_to_check, print_vars) {
   df %>% filter(if_all(all_of(vars_to_check), is.finite))
 }
 
-filter_data_for_plots <- function(df, variables, limits_map) {
+filter_data_for_plots <- function(df, variables, limits_map = NULL) {
 
   # Initialize lists to store lm lines and filtered data
   lm_lines <- list()
@@ -830,11 +827,7 @@ filter_data_for_plots <- function(df, variables, limits_map) {
   df_filtered <- df %>% filter(across(all_of(variables), ~ !is.na(.))) %>%
     filter(across(all_of(variables), ~ between(., limits_map[[cur_column()]][1], limits_map[[cur_column()]][2]), .names = "filter_{col}"))
 
-  # Return the filtered dataframe and lm lines
+  return(df_filtered)
-  return(list(
-    df_filtered = df_filtered,
-    lm_lines = lm_lines
-  ))
 }
 
 main <- function() {