Simplify annotation code

qhtcp-workflow/apps/r/calculate_interaction_zscores.R

@@ -619,109 +619,104 @@ adjust_missing_and_rank <- function(df, variables) {
 
 generate_interaction_plot_configs <- function(df, variables) {
   configs <- list()
-
+  
-  # Define common y-limits and other attributes for each variable dynamically
+  # Define common y-limits for each variable
-  limits_map <- list(L = c(-65, 65), K = c(-65, 65), r = c(-0.65, 0.65), AUC = c(-6500, 6500))
+  limits_map <- list(
-
+    L = c(-65, 65),
-  # Define annotation positions based on the variable being plotted
+    K = c(-65, 65),
-  annotation_positions <- list(
+    r = c(-0.65, 0.65),
-    L = list(Z_Shift_L = 45, lm_ZScore = 25, NG = -25, DB = -35, SM = -45),
+    AUC = c(-6500, 6500)
-    K = list(Z_Shift_K = 45, lm_ZScore = 25, NG = -25, DB = -35, SM = -45),
-    r = list(Z_Shift_r = 0.45, lm_ZScore = 0.25, NG = -0.25, DB = -0.35, SM = -0.45),
-    AUC = list(Z_Shift_AUC = 4500, lm_ZScore = 2500, NG = -2500, DB = -3500, SM = -4500)
   )
-
+  
-  # Define which annotations to include for each plot
+  # Define annotation positions and labels
+  annotation_positions <- list(
+    Z_Shift = 45,
+    Z_lm = 25,
+    NG = -25,
+    DB = -35,
+    SM = -45
+  )
+  
+  # Define functions to generate annotation labels
   annotation_labels <- list(
-    ZShift = function(df, var) {
+    Z_Shift = function(df, var) {
       val <- df[[paste0("Z_Shift_", var)]]
-      if (is.numeric(val)) {
+      paste("ZShift =", round(val, 2))
-        paste("ZShift =", round(val, 2))
-      } else {
-        paste("ZShift =", val)
-      }
     },
-    lm_ZScore = function(df, var) {
+    Z_lm = function(df, var) {
       val <- df[[paste0("Z_lm_", var)]]
-      if (is.numeric(val)) {
+      paste("lm ZScore =", round(val, 2))
-        paste("lm ZScore =", round(val, 2))
-      } else {
-        paste("lm ZScore =", val)
-      }
     },
     NG = function(df, var) paste("NG =", df$NG),
     DB = function(df, var) paste("DB =", df$DB),
     SM = function(df, var) paste("SM =", df$SM)
   )
-
+  
   for (variable in variables) {
-    # Dynamically generate the names of the columns
+    # Get y-limits for the variable
-    var_info <- list(
+    ylim_vals <- limits_map[[variable]]
-      ylim = limits_map[[variable]],
+    
-      sd_col = paste0("WT_sd_", variable)
+    # Extract precomputed linear model coefficients
-    )
-
-    # Extract the precomputed linear model coefficients
     lm_line <- list(
       intercept = df[[paste0("lm_intercept_", variable)]],
       slope = df[[paste0("lm_slope_", variable)]]
     )
-
+    
-    annotations <- lapply(names(annotation_positions[[variable]]), function(annotation_name) {
+    # Generate annotations
-      message("Processing annotation: ", annotation_name, " for variable: ", variable)
+    annotations <- lapply(names(annotation_positions), function(annotation_name) {
-      y_pos <- annotation_positions[[variable]][[annotation_name]]
+      y_pos <- annotation_positions[[annotation_name]]
-      
+      label_func <- annotation_labels[[annotation_name]]
-      # Check if the annotation_name exists in annotation_labels
+      if (!is.null(label_func)) {
-      if (!is.null(annotation_labels[[annotation_name]])) {
+        label <- label_func(df, variable)
-        label <- annotation_labels[[annotation_name]](df, variable)
         list(x = 1, y = y_pos, label = label)
       } else {
         message(paste("Warning: No annotation function found for", annotation_name))
         NULL
       }
     })
-
+    
-    # Filter out any NULL annotations
+    # Remove NULL annotations
     annotations <- Filter(Negate(is.null), annotations)
-
+    
-    # Add scatter plot configuration for this variable
+    # Create scatter plot config
     configs[[length(configs) + 1]] <- list(
       df = df,
       x_var = "conc_num_factor",
       y_var = variable,
       plot_type = "scatter",
       title = sprintf("%s      %s", df$OrfRep[1], df$Gene[1]),
-      ylim_vals = var_info$ylim,
+      ylim_vals = ylim_vals,
       annotations = annotations,
-      lm_line = lm_line,  # Precomputed linear model
+      lm_line = lm_line,
       error_bar = TRUE,
       x_breaks = unique(df$conc_num_factor),
       x_labels = unique(as.character(df$conc_num)),
       x_label = unique(df$Drug[1]),
       position = "jitter",
-      coord_cartesian = c(0, max(var_info$ylim))  # You can customize this per plot as needed
+      coord_cartesian = c(min(ylim_vals), max(ylim_vals))
     )
-
+    
-    # Add box plot configuration for this variable
+    # Create box plot config
     configs[[length(configs) + 1]] <- list(
       df = df,
       x_var = "conc_num_factor",
       y_var = variable,
       plot_type = "box",
       title = sprintf("%s      %s (Boxplot)", df$OrfRep[1], df$Gene[1]),
-      ylim_vals = var_info$ylim,
+      ylim_vals = ylim_vals,
       annotations = annotations,
       error_bar = FALSE,
       x_breaks = unique(df$conc_num_factor),
       x_labels = unique(as.character(df$conc_num)),
       x_label = unique(df$Drug[1]),
-      coord_cartesian = c(0, max(var_info$ylim))  # Customize this as needed
+      coord_cartesian = c(min(ylim_vals), max(ylim_vals))
     )
   }
-
+  
   return(configs)
 }
 
+
 generate_rank_plot_configs <- function(df, rank_var, zscore_var, var, is_lm = FALSE) {
   configs <- list()