Refactor data loading and add more plotting elements

qhtcp-workflow/apps/r/calculate_interaction_zscores.R

@@ -37,28 +37,30 @@ parse_arguments <- function() {
   } else {
     commandArgs(trailingOnly = TRUE)
   }
-  
-  # Extract paths, names, and standard deviations
-  paths <- args[seq(4, length(args), by = 3)]
-  names <- args[seq(5, length(args), by = 3)]
-  sds <- as.numeric(args[seq(6, length(args), by = 3)])
-  
-  # Normalize paths
-  normalized_paths <- normalizePath(paths, mustWork = FALSE)
-  
-  # Create named list of experiments
+
+  out_dir <- normalizePath(args[1], mustWork = FALSE)
+  sgd_gene_list <- normalizePath(args[2], mustWork = FALSE)
+  easy_results_file <- normalizePath(args[3], mustWork = FALSE)
+
+  # The remaining arguments should be in groups of 3
+  exp_args <- args[-(1:3)]
+  if (length(exp_args) %% 3 != 0) {
+    stop("Experiment arguments should be in groups of 3: path, name, sd.")
+  }
+
   experiments <- list()
-  for (i in seq_along(paths)) {
-    experiments[[names[i]]] <- list(
-      path = normalized_paths[i],
-      sd = sds[i]
+  for (i in seq(1, length(exp_args), by = 3)) {
+    exp_name <- exp_args[i + 1]
+    experiments[[exp_name]] <- list(
+      path = normalizePath(exp_args[i], mustWork = FALSE),
+      sd = as.numeric(exp_args[i + 2])
     )
   }
-  
+
   list(
-    out_dir = normalizePath(args[1], mustWork = FALSE),
-    sgd_gene_list = normalizePath(args[2], mustWork = FALSE),
-    easy_results_file = normalizePath(args[3], mustWork = FALSE),
+    out_dir = out_dir,
+    sgd_gene_list = sgd_gene_list,
+    easy_results_file = easy_results_file,
     experiments = experiments
   )
 }
@@ -81,9 +83,11 @@ theme_publication <- function(base_size = 14, base_family = "sans", legend_posit
       plot.background = element_rect(colour = NA),
       panel.border = element_rect(colour = NA),
       axis.title = element_text(face = "bold", size = rel(1)),
-      axis.title.y = element_text(angle = 90, vjust = 2),
-      axis.title.x = element_text(vjust = -0.2),
+      axis.title.y = element_text(angle = 90, vjust = 2, size = 18),
+      axis.title.x = element_text(vjust = -0.2, size = 18),
       axis.line = element_line(colour = "black"),
+      axis.text.x = element_text(size = 16),
+      axis.text.y = element_text(size = 16),
       panel.grid.major = element_line(colour = "#f0f0f0"),
       panel.grid.minor = element_blank(),
       legend.key = element_rect(colour = NA),
@@ -111,27 +115,42 @@ scale_colour_publication <- function(...) {
 
 # Load the initial dataframe from the easy_results_file
 load_and_process_data <- function(easy_results_file, sd = 3) {
-  df <- read.delim(easy_results_file, skip = 2, as.is = TRUE, row.names = 1, strip.white = TRUE)
-  
+  df <- read.delim(
+    easy_results_file,
+    skip = 2,
+    stringsAsFactors = FALSE,
+    row.names = 1,
+    strip.white = TRUE
+  )
+
+  # Filter and rename columns
+  df <- df %>%
+    filter(!is.na(ORF) & ORF != "") %>%
+    filter(!Gene %in% c("BLANK", "Blank", "blank")) %>%
+    filter(Drug != "BMH21") %>%
+    rename(
+      L = l,
+      num = Num.,
+      AUC = AUC96,
+      scan = Scan,
+      last_bg = LstBackgrd,
+      first_bg = X1stBackgrd
+    ) %>%
+    mutate(across(c(Col, Row, num, L, K, r, scan, AUC, last_bg, first_bg), as.numeric))
+
+  # Calculate delta background and tolerance
   df <- df %>%
-    filter(!(.[[1]] %in% c("", "Scan"))) %>%
-    filter(!is.na(ORF) & ORF != "" & !Gene %in% c("BLANK", "Blank", "blank") & Drug != "BMH21") %>%
-    # Rename columns
-    rename(L = l, num = Num., AUC = AUC96, scan = Scan, last_bg = LstBackgrd, first_bg = X1stBackgrd) %>%
     mutate(
-      across(c(Col, Row, num, L, K, r, scan, AUC, last_bg, first_bg), as.numeric),
       delta_bg = last_bg - first_bg,
       delta_bg_tolerance = mean(delta_bg, na.rm = TRUE) + (sd * sd(delta_bg, na.rm = TRUE)),
       NG = if_else(L == 0 & !is.na(L), 1, 0),
       DB = if_else(delta_bg >= delta_bg_tolerance, 1, 0),
       SM = 0,
-      OrfRep = if_else(ORF == "YDL227C", "YDL227C", OrfRep), # should these be hardcoded?
+      OrfRep = if_else(ORF == "YDL227C", "YDL227C", OrfRep),
       conc_num = as.numeric(gsub("[^0-9\\.]", "", Conc)),
       conc_num_factor = as.factor(conc_num)
-      # conc_num_factor = factor(conc_num, levels = sort(unique(conc_num)))
-      # conc_num_numeric = as.numeric(conc_num_factor) - 1
     )
-  
+
   return(df)
 }
 
@@ -439,34 +458,43 @@ generate_and_save_plots <- function(output_dir, file_name, plot_configs, grid_la
   dev.off()
 
   # Combine and save interactive HTML plots
-  combined_plot <- subplot(plotly_plots, nrows = grid_layout$nrow %||% length(plotly_plots), margin = 0.05)
+  combined_plot <- subplot(plotly_plots,
+    nrows = ifelse(is.null(grid_layout$nrow), length(plotly_plots), grid_layout$nrow),
+    margin = 0.05)
   saveWidget(combined_plot, file = file.path(output_dir, paste0(file_name, ".html")), selfcontained = TRUE)
 }
 
 generate_scatter_plot <- function(plot, config) {
   
-  # 1. Determine Shape, Size, and Position for geom_point
-  shape <- if (!is.null(config$shape)) config$shape else 3
-  size <- if (!is.null(config$size)) {
-    config$size
-  } else {
-    if (!is.null(config$delta_bg_point) && config$delta_bg_point) 0.2
-    else if (!is.null(config$gene_point) && config$gene_point) 0.2
-    else 0.1
+  # Build the aes mapping with color if specified
+  if (!is.null(config$color_var)) {
+    plot <- plot + aes(color = .data[[config$color_var]])
   }
   
-  position <- if (!is.null(config$delta_bg_point) && config$delta_bg_point) {
-    "identity"
-  } else if (!is.null(config$gene_point) && config$gene_point) {
-    "jitter"
-  } else {
-    if (!is.null(config$position) && config$position == "jitter") "jitter" else "identity"
-  }
+  # Determine Shape, Size, and Position for geom_point
+  shape <- if (!is.null(config$shape)) config$shape else 3
+  size <- if (!is.null(config$size)) config$size else 0.1
+  position <- if (!is.null(config$position) && config$position == "jitter") "jitter" else "identity"
   
-  # 2. Add geom_point with determined parameters
-  plot <- plot + geom_point(shape = shape, size = size, position = position)
+  # Add geom_point with determined parameters
+  plot <- plot + geom_point(
+    aes(color = .data[[config$color_var]]),
+    shape = shape,
+    size = size,
+    position = position
+  )
+
+  if (!is.null(config$cyan_points)) {
+    plot <- plot + geom_point(
+      data = subset(config$df, is_cyan_point == TRUE),
+      aes(x = .data[[config$x_var]], y = .data[[config$y_var]]),
+      color = "cyan",
+      shape = 3,
+      size = 0.5
+    )
+  }
   
-  # 3. Add Smooth Line if specified
+  # Add Smooth Line if specified
   if (!is.null(config$add_smooth) && config$add_smooth) {
     if (!is.null(config$lm_line)) {
       plot <- plot +
@@ -485,7 +513,7 @@ generate_scatter_plot <- function(plot, config) {
     }
   }
   
-  # 4. Add SD Bands if specified
+  # Add SD Bands if specified
   if (!is.null(config$sd_band_values)) {
     for (sd_band in config$sd_band_values) {
       plot <- plot +
@@ -509,8 +537,24 @@ generate_scatter_plot <- function(plot, config) {
         )
     }
   }
+
+  # Add Rectangles if specified
+  if (!is.null(config$rectangles)) {
+    for (rect in config$rectangles) {
+      plot <- plot + annotate(
+        "rect",
+        xmin = rect$xmin,
+        xmax = rect$xmax,
+        ymin = rect$ymin,
+        ymax = rect$ymax,
+        fill = ifelse(is.null(rect$fill), NA, rect$fill),
+        color = ifelse(is.null(rect$color), "black", rect$color),
+        alpha = ifelse(is.null(rect$alpha), 0.1, rect$alpha)
+      )
+    }
+  }
   
-  # 5. Add Error Bars if specified
+  # Add Error Bars if specified
   if (!is.null(config$error_bar) && config$error_bar && !is.null(config$y_var)) {
     y_mean_col <- paste0("mean_", config$y_var)
     y_sd_col <- paste0("sd_", config$y_var)
@@ -525,7 +569,7 @@ generate_scatter_plot <- function(plot, config) {
       )
   }
   
-  # 6. Customize X-axis if specified
+  # Customize X-axis if specified
   if (!is.null(config$x_breaks) && !is.null(config$x_labels) && !is.null(config$x_label)) {
     plot <- plot +
       scale_x_discrete(
@@ -535,17 +579,17 @@ generate_scatter_plot <- function(plot, config) {
       )
   }
   
-  # 7. Apply coord_cartesian if specified
+  # Apply coord_cartesian if specified
   if (!is.null(config$coord_cartesian)) {
     plot <- plot + coord_cartesian(ylim = config$coord_cartesian)
   }
   
-  # 8. Set Y-axis limits if specified
+  # Set Y-axis limits if specified
   if (!is.null(config$ylim_vals)) {
     plot <- plot + scale_y_continuous(limits = config$ylim_vals)
   }
   
-  # 9. Add Annotations if specified
+  # Add Annotations if specified
   if (!is.null(config$annotations)) {
     for (annotation in config$annotations) {
       plot <- plot +
@@ -559,12 +603,12 @@ generate_scatter_plot <- function(plot, config) {
     }
   }
   
-  # 10. Add Title if specified
+  # Add Title if specified
   if (!is.null(config$title)) {
     plot <- plot + ggtitle(config$title)
   }
   
-  # 11. Adjust Legend Position if specified
+  # Adjust Legend Position if specified
   if (!is.null(config$legend_position)) {
     plot <- plot + theme(legend.position = config$legend_position)
   }
@@ -692,121 +736,131 @@ generate_interaction_plot_configs <- function(df, variables) {
   return(configs)
 }
 
-generate_rank_plot_configs <- function(df_filtered, variables, is_lm = FALSE, adjust = FALSE) {
-  
-  # Define SD bands
-  sd_bands <- c(1, 2, 3)
-  
-  # Initialize list to store plot configurations
-  configs <- list()
-  
-  # SD-based plots for L and K
-  for (variable in c("L", "K")) {
-    for (sd_band in sd_bands) {
-      
-      # Determine columns based on whether it's lm or not
-      if (is_lm) {
-        rank_var <- paste0(variable, "_Rank_lm")
-        zscore_var <- paste0("Z_lm_", variable)
-        y_label <- paste("Int Z score", variable)
-      } else {
-        rank_var <- paste0(variable, "_Rank")
-        zscore_var <- paste0("Avg_Zscore_", variable)
-        y_label <- paste("Avg Z score", variable)
-      }
-      
-      # Annotated Plot Configuration
-      configs[[length(configs) + 1]] <- list(
-        df = df_filtered,
-        x_var = rank_var,
-        y_var = zscore_var,
-        plot_type = "scatter",
-        title = paste(y_label, "vs. Rank for", variable, "above", sd_band, "SD"),
-        sd_band = sd_band,
-        annotations = list(
-          list(
-            x = median(df_filtered[[rank_var]], na.rm = TRUE),
-            y = 10,
-            label = paste("Deletion Enhancers =", sum(df_filtered[[zscore_var]] >= sd_band, na.rm = TRUE))
-          ),
-          list(
-            x = median(df_filtered[[rank_var]], na.rm = TRUE),
-            y = -10,
-            label = paste("Deletion Suppressors =", sum(df_filtered[[zscore_var]] <= -sd_band, na.rm = TRUE))
-          )
-        ),
-        sd_band_values = sd_band,
-        shape = 3,
-        size = 0.1
-      )
-      
-      # Non-Annotated Plot Configuration
-      configs[[length(configs) + 1]] <- list(
-        df = df_filtered,
-        x_var = rank_var,
-        y_var = zscore_var,
-        plot_type = "scatter",
-        title = paste(y_label, "vs. Rank for", variable, "above", sd_band, "SD No Annotations"),
-        sd_band = sd_band,
-        annotations = NULL,
-        sd_band_values = sd_band,
-        shape = 3,
-        size = 0.1
-      )
+generate_rank_plot_configs <- function(df_filtered, variables, is_lm = FALSE) {
+    # Define SD bands
+    sd_bands <- c(1, 2, 3)
+    
+    # Initialize list to store plot configurations
+    configs <- list()
+    
+    # SD-based plots for L and K
+    for (variable in c("L", "K")) {
+        for (sd_band in sd_bands) {
+            # Determine columns based on whether it's lm or not
+            if (is_lm) {
+                rank_var <- paste0(variable, "_Rank_lm")
+                zscore_var <- paste0("Z_lm_", variable)
+                y_label <- paste("Int Z score", variable)
+            } else {
+                rank_var <- paste0(variable, "_Rank")
+                zscore_var <- paste0("Avg_Zscore_", variable)
+                y_label <- paste("Avg Z score", variable)
+            }
+            
+            # Calculate counts for annotations
+            num_enhancers <- sum(df_filtered[[zscore_var]] >= sd_band, na.rm = TRUE)
+            num_suppressors <- sum(df_filtered[[zscore_var]] <= -sd_band, na.rm = TRUE)
+            
+            # Annotated Plot Configuration
+            configs[[length(configs) + 1]] <- list(
+                df = df_filtered,
+                x_var = rank_var,
+                y_var = zscore_var,
+                plot_type = "scatter",
+                title = paste(y_label, "vs. Rank for", variable, "above", sd_band, "SD"),
+                sd_band = sd_band,
+                annotations = list(
+                    list(
+                        x = median(df_filtered[[rank_var]], na.rm = TRUE),
+                        y = 10,
+                        label = paste("Deletion Enhancers =", num_enhancers)
+                    ),
+                    list(
+                        x = median(df_filtered[[rank_var]], na.rm = TRUE),
+                        y = -10,
+                        label = paste("Deletion Suppressors =", num_suppressors)
+                    )
+                ),
+                sd_band_values = sd_band,
+                shape = 3,
+                size = 0.1,
+                y_label = y_label,
+                x_label = "Rank",
+                legend_position = "none"
+            )
+            
+            # Non-Annotated Plot Configuration
+            configs[[length(configs) + 1]] <- list(
+                df = df_filtered,
+                x_var = rank_var,
+                y_var = zscore_var,
+                plot_type = "scatter",
+                title = paste(y_label, "vs. Rank for", variable, "above", sd_band, "SD No Annotations"),
+                sd_band = sd_band,
+                annotations = NULL,
+                sd_band_values = sd_band,
+                shape = 3,
+                size = 0.1,
+                y_label = y_label,
+                x_label = "Rank",
+                legend_position = "none"
+            )
+        }
     }
-  }
-  
-  # Avg ZScore and Rank Avg ZScore Plots for r, L, K, and AUC
-  for (variable in variables) {
-    for (plot_type in c("Avg_Zscore_vs_lm", "Rank_Avg_Zscore_vs_lm")) {
-      
-      # Define x and y variables based on plot type
-      if (plot_type == "Avg_Zscore_vs_lm") {
-        x_var <- paste0("Avg_Zscore_", variable)
-        y_var <- paste0("Z_lm_", variable)
-        title_suffix <- paste("Avg Zscore vs lm", variable)
-      } else if (plot_type == "Rank_Avg_Zscore_vs_lm") {
-        x_var <- paste0(variable, "_Rank")
-        y_var <- paste0(variable, "_Rank_lm")
-        title_suffix <- paste("Rank Avg Zscore vs lm", variable)
-      }
-      
-      # Determine y-axis label
-      if (plot_type == "Avg_Zscore_vs_lm") {
-        y_label <- paste("Z lm", variable)
-      } else {
-        y_label <- paste("Rank lm", variable)
-      }
-      
-      # Determine correlation text (R-squared)
-      lm_fit <- lm(df_filtered[[y_var]] ~ df_filtered[[x_var]], data = df_filtered)
-      r_squared <- summary(lm_fit)$r.squared
-      
-      # Plot Configuration
-      configs[[length(configs) + 1]] <- list(
-        df = df_filtered,
-        x_var = x_var,
-        y_var = y_var,
-        plot_type = "scatter",
-        title = title_suffix,
-        annotations = list(
-          list(
-            x = 0,
-            y = 0,
-            label = paste("R-squared =", round(r_squared, 2))
-          )
-        ),
-        sd_band_values = NULL,  # Not applicable
-        shape = 3,
-        size = 0.1,
-        add_smooth = TRUE,
-        lm_line = list(intercept = coef(lm_fit)[1], slope = coef(lm_fit)[2]),
-        legend_position = "right"
-      )
+    
+    # Avg ZScore and Rank Avg ZScore Plots for r, L, K, and AUC
+    for (variable in variables) {
+        for (plot_type in c("Avg_Zscore_vs_lm", "Rank_Avg_Zscore_vs_lm")) {
+            # Define x and y variables based on plot type
+            if (plot_type == "Avg_Zscore_vs_lm") {
+                x_var <- paste0("Avg_Zscore_", variable)
+                y_var <- paste0("Z_lm_", variable)
+                title_suffix <- paste("Avg Zscore vs lm", variable)
+            } else {
+                x_var <- paste0(variable, "_Rank")
+                y_var <- paste0(variable, "_Rank_lm")
+                title_suffix <- paste("Rank Avg Zscore vs lm", variable)
+            }
+            
+            # Fit linear model
+            lm_fit <- lm(df_filtered[[y_var]] ~ df_filtered[[x_var]], data = df_filtered)
+            
+            # Check for perfect fit
+            if (summary(lm_fit)$sigma == 0) {
+                next  # Skip this iteration if the fit is perfect
+            }
+            
+            # Calculate R-squared
+            r_squared <- summary(lm_fit)$r.squared
+            
+            # Plot Configuration
+            configs[[length(configs) + 1]] <- list(
+                df = df_filtered,
+                x_var = x_var,
+                y_var = y_var,
+                plot_type = "scatter",
+                title = title_suffix,
+                annotations = list(
+                    list(
+                        x = 0,
+                        y = 0,
+                        label = paste("R-squared =", round(r_squared, 2))
+                    )
+                ),
+                sd_band_values = NULL,  # Not applicable
+                shape = 3,
+                size = 0.1,
+                add_smooth = TRUE,
+                lm_line = list(intercept = coef(lm_fit)[1], slope = coef(lm_fit)[2]),
+                legend_position = "right",
+                color_var = "Overlap",
+                x_label = x_var,
+                y_label = y_var
+            )
+        }
     }
-  }
-  
-  return(configs)
+    
+    return(configs)
 }
 
 generate_correlation_plot_configs <- function(df) {
@@ -831,7 +885,7 @@ generate_correlation_plot_configs <- function(df) {
     config <- list(
       df = df,
       x_var = rel$x,
-      y_var = rel$y,
+      y_var = rel.y,
       plot_type = "scatter",
       title = rel$label,
       x_label = paste("z-score", gsub("Z_lm_", "", rel$x)),
@@ -839,9 +893,19 @@ generate_correlation_plot_configs <- function(df) {
       annotations = list(
         list(x = 0, y = 0, label = paste("R-squared =", round(lm_summary$r.squared, 3)))
       ),
-      add_smooth = TRUE,  # This flags that a geom_smooth layer should be added
-      lm_line = list(intercept = coef(lm_model)[1], slope = coef(lm_model)[2]),  # For direct geom_abline if needed
-      legend_position = "right"
+      add_smooth = TRUE,  # Add regression line
+      lm_line = list(intercept = coef(lm_model)[1], slope = coef(lm_model)[2]),
+      legend_position = "right",
+      shape = 3,
+      size = 0.5,
+      color_var = "Overlap",
+      rectangles = list(
+        list(
+          xmin = -2, xmax = 2, ymin = -2, ymax = 2,
+          fill = NA, color = "grey20", alpha = 0.1
+        )
+      ),
+      cyan_points = TRUE
     )
 
     configs[[length(configs) + 1]] <- config
@@ -850,6 +914,7 @@ generate_correlation_plot_configs <- function(df) {
   return(configs)
 }
 
+
 filter_data <- function(df, variables, nf = FALSE, missing = FALSE, adjust = FALSE,
   rank = FALSE, limits_map = NULL, verbose = TRUE) {
   
@@ -1188,19 +1253,16 @@ main <- function() {
       )
     )
 
-    # message("Generating quality control plots")
-    # generate_and_save_plots(out_dir_qc, "L_vs_K_before_quality_control", l_vs_k_plots)
-    # generate_and_save_plots(out_dir_qc, "frequency_delta_background", frequency_delta_bg_plots)
-    # generate_and_save_plots(out_dir_qc, "L_vs_K_above_threshold", above_threshold_plots)
-    # generate_and_save_plots(out_dir_qc, "plate_analysis", plate_analysis_plots)
-    # generate_and_save_plots(out_dir_qc, "plate_analysis_boxplots", plate_analysis_boxplots)
-    # generate_and_save_plots(out_dir_qc, "plate_analysis_no_zeros", plate_analysis_no_zeros_plots)
-    # generate_and_save_plots(out_dir_qc, "plate_analysis_no_zeros_boxplots", plate_analysis_no_zeros_boxplots)
-    # generate_and_save_plots(out_dir_qc, "L_vs_K_for_strains_2SD_outside_mean_K", l_outside_2sd_k_plots)
-    # generate_and_save_plots(out_dir_qc, "delta_background_vs_K_for_strains_2sd_outside_mean_K", delta_bg_outside_2sd_k_plots)
-
-    # TODO: Originally this filtered L NA's
-    # Let's try to avoid for now since stats have already been calculated
+    message("Generating quality control plots")
+    generate_and_save_plots(out_dir_qc, "L_vs_K_before_quality_control", l_vs_k_plots)
+    generate_and_save_plots(out_dir_qc, "frequency_delta_background", frequency_delta_bg_plots)
+    generate_and_save_plots(out_dir_qc, "L_vs_K_above_threshold", above_threshold_plots)
+    generate_and_save_plots(out_dir_qc, "plate_analysis", plate_analysis_plots)
+    generate_and_save_plots(out_dir_qc, "plate_analysis_boxplots", plate_analysis_boxplots)
+    generate_and_save_plots(out_dir_qc, "plate_analysis_no_zeros", plate_analysis_no_zeros_plots)
+    generate_and_save_plots(out_dir_qc, "plate_analysis_no_zeros_boxplots", plate_analysis_no_zeros_boxplots)
+    generate_and_save_plots(out_dir_qc, "L_vs_K_for_strains_2SD_outside_mean_K", l_outside_2sd_k_plots)
+    generate_and_save_plots(out_dir_qc, "delta_background_vs_K_for_strains_2sd_outside_mean_K", delta_bg_outside_2sd_k_plots)
 
     # Process background strains
     bg_strains <- c("YDL227C")
@@ -1349,8 +1411,7 @@ main <- function() {
       rank_plot_configs <- generate_rank_plot_configs(
         df = zscores_interactions_joined_filtered,
         variables = interaction_vars,
-        is_lm = FALSE,
-        adjust = TRUE
+        is_lm = FALSE
       )
       generate_and_save_plots(output_dir = out_dir, file_name = "RankPlots",
         plot_configs = rank_plot_configs, grid_layout = list(ncol = 3, nrow = 2))
@@ -1359,8 +1420,7 @@ main <- function() {
       rank_lm_plot_configs <- generate_rank_plot_configs(
         df = zscores_interactions_joined_filtered,
         variables = interaction_vars,
-        is_lm = TRUE,
-        adjust = TRUE
+        is_lm = TRUE
       )
       generate_and_save_plots(output_dir = out_dir, file_name = "RankPlots_lm",
         plot_configs = rank_lm_plot_configs, grid_layout = list(ncol = 3, nrow = 2))
@@ -1370,8 +1430,6 @@ main <- function() {
       zscores_interactions_filtered <- zscores_interactions_joined %>%
         group_by(across(all_of(c("OrfRep", "Gene", "num")))) %>%
         filter(!is.na(Z_lm_L) | !is.na(Avg_Zscore_L)) %>%
-        ungroup() %>%
-        rowwise() %>%
         mutate(
           lm_R_squared_L = if (n() > 1) summary(lm(Z_lm_L ~ Avg_Zscore_L))$r.squared else NA,
           lm_R_squared_K = if (n() > 1) summary(lm(Z_lm_K ~ Avg_Zscore_K))$r.squared else NA,
@@ -1387,8 +1445,7 @@ main <- function() {
             Z_lm_L <= -2 & Avg_Zscore_L >= 2 ~ "Deletion Suppressor lm, Deletion Enhancer Avg Z score",
             TRUE ~ "No Effect"
           )
-        ) %>%
-        ungroup()
+        )
 
       # Re-rank
       zscores_interactions_filtered <- filter_data(
@@ -1401,8 +1458,7 @@ main <- function() {
       rank_plot_filtered_configs <- generate_rank_plot_configs(
         df = zscores_interactions_filtered,
         variables = interaction_vars,
-        is_lm = FALSE,
-        adjust = FALSE
+        is_lm = FALSE
       )
 
       message("Generating filtered ranked plots")
@@ -1416,8 +1472,7 @@ main <- function() {
       rank_plot_lm_filtered_configs <- generate_rank_plot_configs(
         df = zscores_interactions_filtered,
         variables = interaction_vars,
-        is_lm = TRUE,
-        adjust = FALSE
+        is_lm = TRUE
       )
       generate_and_save_plots(
         output_dir = out_dir,