Add more plotting nuances

qhtcp-workflow/apps/r/calculate_interaction_zscores.R

@@ -356,39 +356,52 @@ generate_and_save_plots <- function(output_dir, file_name, plot_configs, grid_la
     
     # Plot type handling
     plot <- switch(config$plot_type,
-      
       "scatter" = {
-        plot + geom_point(aes(ORF = ORF, Gene = Gene, !!sym(config$x_var) := !!sym(config$x_var)),
+        plot <- if (!is.null(config$delta_bg_point) && config$delta_bg_point) {
-          shape = config$shape %||% 3, size = config$size %||% 0.6) +
+          plot + geom_point(aes(ORF = ORF, Gene = Gene, delta_bg = delta_bg), config$shape %||% 3)
-          (if (!is.null(config$add_smooth) && config$add_smooth)
+        } else if (!is.null(config$gene_point) && config$gene_point) {
-            geom_smooth(method = "lm", se = FALSE)
+          plot + geom_point(aes(ORF = ORF, Gene = Gene, Gene = Gene), shape = config$shape %||% 3, position = "jitter")
-          else NULL) +
+        } else if (!is.null(config$position) && config$position == "jitter") {
-          (if (!is.null(config$position) && config$position == "jitter")
+          plot + geom_point(shape = config$shape %||% 3, size = config$size %||% 0.2, position = "jitter")
-            geom_point(position = "jitter")
+        } else {
-          else NULL) +
+          plot + geom_point(shape = config$shape %||% 3, size = config$size %||% 0.2)
-          # Use precalculated mean and sd for error bars
+        }
+
+        if (!is.null(config$add_smooth) && config$add_smooth) {
+          plot <- plot + geom_smooth(method = "lm", se = FALSE)
+        }
+        
+        plot <- plot +
           geom_errorbar(aes(
             ymin = !!sym(paste0("mean_", config$y_var)) - !!sym(paste0("sd_", config$y_var)),
-            ymax = !!sym(paste0("mean_", config$y_var)) + !!sym(paste0("sd_", config$y_var))), width = 0.1) +
+            ymax = !!sym(paste0("mean_", config$y_var)) + !!sym(paste0("sd_", config$y_var))),
+            width = 0.1) +
           geom_point(aes(y = !!sym(paste0("mean_", config$y_var))), size = 0.6)
+        
+        plot
       },
       
       "rank" = {
-        plot + geom_point(size = config$size %||% 0.1, shape = config$shape %||% 3) +
+        plot <- plot + geom_point(size = config$size %||% 0.1, shape = config$shape %||% 3)
-          (if (!is.null(config$sd_band))
+        
-            Reduce(`+`, lapply(seq_len(config$sd_band), function(i) {
+        if (!is.null(config$sd_band)) {
-              list(
+          for (i in seq_len(config$sd_band)) {
-                annotate("rect", xmin = -Inf, xmax = Inf, ymin = i, ymax = Inf, fill = "#542788", alpha = 0.3),
+            plot <- plot +
-                annotate("rect", xmin = -Inf, xmax = Inf, ymin = -i, ymax = -Inf, fill = "orange", alpha = 0.3),
+              annotate("rect", xmin = -Inf, xmax = Inf, ymin = i, ymax = Inf, fill = "#542788", alpha = 0.3) +
-                geom_hline(yintercept = c(-i, i), color = "gray")
+              annotate("rect", xmin = -Inf, xmax = Inf, ymin = -i, ymax = -Inf, fill = "orange", alpha = 0.3) +
-              )
+              geom_hline(yintercept = c(-i, i), color = "gray")
-            })) else NULL) +
+          }
-          (if (!is.null(config$enhancer_label))
+        }
-            annotate("text", x = config$enhancer_label$x, y = config$enhancer_label$y, label = config$enhancer_label$label)
+        
-          else NULL) +
+        if (!is.null(config$enhancer_label)) {
-          (if (!is.null(config$suppressor_label))
+          plot <- plot + annotate("text", x = config$enhancer_label$x, y = config$enhancer_label$y, label = config$enhancer_label$label)
-            annotate("text", x = config$suppressor_label$x, y = config$suppressor_label$y, label = config$suppressor_label$label)
+        }
-          else NULL)
+        
+        if (!is.null(config$suppressor_label)) {
+          plot <- plot + annotate("text", x = config$suppressor_label$x, y = config$suppressor_label$y, label = config$suppressor_label$label)
+        }
+        
+        plot
       },
       
       "correlation" = plot + geom_point(shape = config$shape %||% 3, color = "gray70") +
@@ -397,36 +410,10 @@ generate_and_save_plots <- function(output_dir, file_name, plot_configs, grid_la
       
       "box" = plot + geom_boxplot(),
       "density" = plot + geom_density(),
-      "bar" = plot + geom_bar(),
+      "bar" = plot + geom_bar()
-      
-      # Default case (scatter with smooth line)
-      plot + geom_point(shape = config$shape %||% 3) + geom_smooth(method = "lm", se = FALSE)
     )
     
-    # Error bars using pre-calculated mean and sd columns
+    plot
-    if (!is.null(config$error_bar) && config$error_bar) {
-      y_mean_col <- paste0("mean_", config$y_var)
-      y_sd_col <- paste0("sd_", config$y_var)
-      plot <- plot + geom_errorbar(aes(
-        ymin = !!sym(y_mean_col) - !!sym(y_sd_col),
-        ymax = !!sym(y_mean_col) + !!sym(y_sd_col)), width = 0.1) +
-        geom_point(aes(y = !!sym(y_mean_col)), size = 0.6)
-    }
-    
-    # Y-limits and labels
-    plot <- plot + (if (!is.null(config$ylim_vals)) coord_cartesian(ylim = config$ylim_vals) else NULL) +
-      ggtitle(config$title) +
-      theme_publication(legend_position = config$legend_position %||% "bottom") +
-      xlab(config$x_label %||% "") + ylab(config$y_label %||% "")
-    
-    # Annotations
-    if (!is.null(config$annotations)) {
-      for (annotation in config$annotations) {
-        plot <- plot + geom_text(aes(x = annotation$x, y = annotation$y, label = annotation$label))
-      }
-    }
-    
-    return(plot)
   })
   
   # Save plots to PDF and HTML
@@ -442,43 +429,39 @@ generate_and_save_plots <- function(output_dir, file_name, plot_configs, grid_la
 generate_interaction_plot_configs <- function(df, variables) {
   configs <- list()
 
-  for (variable in variables) {
+  # Predefine y-limits and annotation y-values for each variable
-    # Define the y-limits based on the variable being plotted
+  variable_properties <- list(
-    ylim_vals <- switch(variable,
+    "L" = list(ylim = c(-65, 65), annotations_y = c(45, 25, -25, -35, -45)),
-      "L" = c(-65, 65),
+    "K" = list(ylim = c(-65, 65), annotations_y = c(45, 25, -25, -35, -45)),
-      "K" = c(-65, 65),
+    "r" = list(ylim = c(-0.65, 0.65), annotations_y = c(0.45, 0.25, -0.25, -0.35, -0.45)),
-      "r" = c(-0.65, 0.65),
+    "AUC" = list(ylim = c(-6500, 6500), annotations_y = c(4500, 2500, -2500, -3500, -4500))
-      "AUC" = c(-6500, 6500)
+  )
-    )
 
-    # Dynamically generate the column names for standard deviation and delta
+  for (variable in variables) {
+    props <- variable_properties[[variable]]
+    
+    # Dynamically generate column names
     wt_sd_col <- paste0("WT_sd_", variable)
     delta_var <- paste0("Delta_", variable)
     z_shift <- paste0("Z_Shift_", variable)
     z_lm <- paste0("Z_lm_", variable)
+    lm_score <- paste0("lm_Score_", variable)  # Precomputed lm score
+    r_squared <- paste0("r_squared_", variable)  # Precomputed R^2
 
-    # Set annotations for ZShift, Z lm Score, NG, DB, SM
+    # Create annotation list
-    annotations <- list(
+    annotation_labels <- c("ZShift =", "lm ZScore =", "NG =", "DB =", "SM =")
-      list(x = 1, y = ifelse(variable == "L", 45, ifelse(variable == "K", 45,
+    annotations <- lapply(seq_along(annotation_labels), function(i) {
-        ifelse(variable == "r", 0.45, 4500))), label = paste("ZShift =", round(df[[z_shift]], 2))),
+      list(x = 1, y = props$annotations_y[i], label = paste(annotation_labels[i], round(df[[c(z_shift, z_lm, "NG", "DB", "SM")[i]]], 2)))
-      list(x = 1, y = ifelse(variable == "L", 25, ifelse(variable == "K", 25,
+    })
-        ifelse(variable == "r", 0.25, 2500))), label = paste("lm ZScore =", round(df[[z_lm]], 2))),
-      list(x = 1, y = ifelse(variable == "L", -25, ifelse(variable == "K", -25,
-        ifelse(variable == "r", -0.25, -2500))), label = paste("NG =", df$NG)),
-      list(x = 1, y = ifelse(variable == "L", -35, ifelse(variable == "K", -35,
-        ifelse(variable == "r", -0.35, -3500))), label = paste("DB =", df$DB)),
-      list(x = 1, y = ifelse(variable == "L", -45, ifelse(variable == "K", -45,
-        ifelse(variable == "r", -0.45, -4500))), label = paste("SM =", df$SM))
-    )
 
-    # Add scatter plot configuration for this variable
+    # Create scatter plot configuration using precomputed lm scores
-    configs[[length(configs) + 1]] <- list(
+    scatter_config <- list(
       df = df,
       x_var = "conc_num_factor",
       y_var = delta_var,
       plot_type = "scatter",
       title = sprintf("%s      %s", df$OrfRep[1], df$Gene[1]),
-      ylim_vals = ylim_vals,
+      ylim_vals = props$ylim,
       annotations = annotations,
       error_bar = list(
         ymin = 0 - (2 * df[[wt_sd_col]][1]),
@@ -489,28 +472,36 @@ generate_interaction_plot_configs <- function(df, variables) {
       x_label = unique(df$Drug[1]),
       shape = 3,
       size = 0.6,
-      position = "jitter"
+      position = "jitter",
+      lm_line = list(
+        intercept = coef(lm(df[[delta_var]] ~ df$conc_num_factor))[1],  # Intercept from lm model
+        slope = coef(lm(df[[delta_var]] ~ df$conc_num_factor))[2]      # Slope from lm model
+      )
     )
 
-    # Add box plot configuration for this variable
+    # Create box plot configuration for this variable
-    configs[[length(configs) + 1]] <- list(
+    box_config <- 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 = ylim_vals,
+      ylim_vals = props$ylim,
       annotations = annotations,
-      error_bar = FALSE,  # Boxplots typically don't need error bars
+      error_bar = FALSE,
       x_breaks = unique(df$conc_num_factor),
       x_labels = unique(as.character(df$conc_num)),
       x_label = unique(df$Drug[1])
     )
+
+    # Append both scatter and box plot configurations
+    configs <- append(configs, list(scatter_config, box_config))
   }
 
   return(configs)
 }
 
+
 # Adjust missing values and calculate ranks
 adjust_missing_and_rank <- function(df, variables) {
 
@@ -706,9 +697,10 @@ main <- function() {
         x_var = "L",
         y_var = "K",
         plot_type = "scatter",
+        delta_bg_point = TRUE,
         title = "Raw L vs K before quality control",
         color_var = "conc_num",
-        position = "jitter",
+        error_bar = FALSE,
         legend_position = "right"
       )
     )
@@ -744,6 +736,7 @@ main <- function() {
         x_var = "L",
         y_var = "K",
         plot_type = "scatter",
+        delta_bg_point = TRUE,
         title = paste("Raw L vs K for strains above Delta Background threshold of",
           df_above_tolerance$delta_bg_tolerance[[1]], "or above"),
         color_var = "conc_num",
@@ -804,7 +797,6 @@ main <- function() {
 
     plate_analysis_no_zeros_plots <- list()
     for (var in summary_vars) {
-
       config <- list(
         df = df_no_zeros_filtered_stats,
         x_var = "scan",
@@ -820,9 +812,6 @@ main <- function() {
 
     plate_analysis_no_zeros_boxplots <- list()
     for (var in summary_vars) {
-      
-      
-      # Create the plot configuration
       config <- list(
         df = df_no_zeros_filtered_stats,
         x_var = "scan",
@@ -841,6 +830,7 @@ main <- function() {
         x_var = "L",
         y_var = "K",
         plot_type = "scatter",
+        delta_bg_point = TRUE,
         title = "Raw L vs K for strains falling outside 2SD of the K mean at each Conc",
         color_var = "conc_num",
         position = "jitter",
@@ -854,6 +844,7 @@ main <- function() {
         x_var = "delta_bg",
         y_var = "K",
         plot_type = "scatter",
+        gene_point = TRUE,
         title = "Delta Background vs K for strains falling outside 2SD of the K mean at each Conc",
         color_var = "conc_num",
         position = "jitter",