server-scripts/qhtcp-workflow/apps/r/calculate_interaction_zscores.R

suppressMessages({
  library("ggplot2")
  library("plotly")
  library("htmlwidgets")
  library("htmltools")
  library("dplyr")
  library("rlang")
  library("ggthemes")
  library("data.table")
  library("grid")
  library("gridExtra")
  library("future")
  library("furrr")
  library("purrr")
})

# These parallelization libraries are very noisy
suppressPackageStartupMessages({
  library("future")
  library("furrr")
  library("purrr")
})

# Turn all warnings into errors for development
options(warn = 2)

parse_arguments <- function() {
  args <- if (interactive()) {
    c(
      "/home/bryan/documents/develop/hartmanlab/qhtcp-workflow/out/20240116_jhartman2_DoxoHLD/20240116_jhartman2_DoxoHLD",
      "/home/bryan/documents/develop/hartmanlab/qhtcp-workflow/apps/r/SGD_features.tab",
      "/home/bryan/documents/develop/hartmanlab/qhtcp-workflow/out/20240116_jhartman2_DoxoHLD/easy/20240116_jhartman2_DoxoHLD/results_std.txt",
      "/home/bryan/documents/develop/hartmanlab/qhtcp-workflow/out/20240116_jhartman2_DoxoHLD/20240822_jhartman2_DoxoHLD/exp1",
      "Experiment 1: Doxo versus HLD",
      3,
      "/home/bryan/documents/develop/hartmanlab/qhtcp-workflow/out/20240116_jhartman2_DoxoHLD/20240822_jhartman2_DoxoHLD/exp2",
      "Experiment 2: HLD versus Doxo",
      3
    )
  } else {
    commandArgs(trailingOnly = TRUE)
  }

  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.")
  }

  # Extract the experiments into a list
  experiments <- list()
  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])
    )
  }

  # Extract the trailing number from each path
  trailing_numbers <- sapply(experiments, function(x) {
    path <- x$path
    nums <- gsub("[^0-9]", "", basename(path))
    as.integer(nums)
  })

  # Sort the experiments based on the trailing numbers
  sorted_experiments <- experiments[order(trailing_numbers)]

  list(
    out_dir = out_dir,
    sgd_gene_list = sgd_gene_list,
    easy_results_file = easy_results_file,
    experiments = sorted_experiments
  )
}

args <- parse_arguments()

# Should we keep output in exp dirs or combine in the study output dir?
# dir.create(file.path(args$out_dir, "zscores"), showWarnings = FALSE)
# dir.create(file.path(args$out_dir, "zscores", "qc"), showWarnings = FALSE)

theme_publication <- function(base_size = 14, base_family = "sans", legend_position = NULL) {
  # Ensure that legend_position has a valid value or default to "none"
  legend_position <- if (is.null(legend_position) || length(legend_position) == 0) "none" else legend_position
  
  theme_foundation <- ggthemes::theme_foundation(base_size = base_size, base_family = base_family)
  
  theme_foundation %+replace%
    theme(
      plot.title = element_text(face = "bold", size = rel(1.6), hjust = 0.5),
      text = element_text(),
      panel.background = element_blank(),
      plot.background = element_blank(),
      panel.border = element_blank(),
      axis.title = element_text(face = "bold", size = rel(1.4)),
      axis.title.y = element_text(angle = 90, vjust = 2),
      axis.text = element_text(size = rel(1.2)),
      axis.line = element_line(colour = "black"),
      panel.grid.major = element_line(colour = "#f0f0f0"),
      panel.grid.minor = element_blank(),
      legend.key = element_rect(colour = NA),
      legend.position = legend_position,
      legend.direction =
        if (legend_position == "right") {
          "vertical"
        } else if (legend_position == "bottom") {
          "horizontal"
        } else {
          NULL  # No legend direction if position is "none" or other values
        },
      legend.spacing = unit(0, "cm"),
      legend.title = element_text(face = "italic", size = rel(1.3)),
      legend.text = element_text(size = rel(1.2)),
      plot.margin = unit(c(10, 5, 5, 5), "mm")
    )
}

scale_fill_publication <- function(...) {
  discrete_scale("fill", "Publication", manual_pal(values = c(
    "#386cb0", "#fdb462", "#7fc97f", "#ef3b2c", "#662506",
    "#a6cee3", "#fb9a99", "#984ea3", "#ffff33"
  )), ...)
}

scale_colour_publication <- function(...) {
  discrete_scale("colour", "Publication", manual_pal(values = c(
    "#386cb0", "#fdb462", "#7fc97f", "#ef3b2c", "#662506",
    "#a6cee3", "#fb9a99", "#984ea3", "#ffff33"
  )), ...)
}

# Load the initial dataframe from the easy_results_file
load_and_filter_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 <- 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?
      conc_num = as.numeric(gsub("[^0-9\\.]", "", Conc)),
      conc_num_factor = as.numeric(as.factor(conc_num)) - 1, # for legacy purposes
      conc_num_factor_factor = as.factor(conc_num)
    )

  return(df)
}

update_gene_names <- function(df, sgd_gene_list) {
  genes <- read.delim(file = sgd_gene_list, quote = "", header = FALSE,
    colClasses = c(rep("NULL", 3), rep("character", 2), rep("NULL", 11)))
  
  gene_map <- setNames(genes$V5, genes$V4) # ORF to GeneName mapping

  df <- df %>%
    mutate(
      mapped_genes = gene_map[ORF],
      Gene = if_else(is.na(mapped_genes) | OrfRep == "YDL227C", Gene, mapped_genes),
      Gene = if_else(Gene == "" | Gene == "OCT1", OrfRep, Gene) # Handle invalid names
    )

  return(df)
}

calculate_summary_stats <- function(df, variables, group_vars) {
  summary_stats <- df %>%
    group_by(across(all_of(group_vars))) %>%
    summarise(
      N = n(),
      across(all_of(variables),
        list(
          mean = ~ mean(.x, na.rm = TRUE),
          median = ~ median(.x, na.rm = TRUE),
          max = ~ ifelse(all(is.na(.x)), NA, max(.x, na.rm = TRUE)),
          min = ~ ifelse(all(is.na(.x)), NA, min(.x, na.rm = TRUE)),
          sd = ~ sd(.x, na.rm = TRUE),
          se = ~ sd(.x, na.rm = TRUE) / sqrt(n() - 1)
        ),
        .names = "{.fn}_{.col}"
      ),
      .groups = "drop"
    )
  
  # Create a cleaned version of df that doesn't overlap with summary_stats
  df_cleaned <- df %>%
    select(-any_of(setdiff(intersect(names(df), names(summary_stats)), group_vars)))
  
  df_joined <- left_join(df_cleaned, summary_stats, by = group_vars)

  return(list(summary_stats = summary_stats, df_with_stats = df_joined))
}

calculate_interaction_scores <- function(df, df_bg, type, overlap_threshold = 2) {

  max_conc <- max(as.numeric(df$conc_num_factor), na.rm = TRUE)
  total_conc_num <- length(unique(df$conc_num))

  if (type == "reference") {
    bg_group_vars <- c("OrfRep", "Gene", "num", "Drug", "conc_num", "conc_num_factor", "conc_num_factor_factor")
    group_vars <- c("OrfRep", "Gene", "num", "Drug")
  } else if (type == "deletion") {
    bg_group_vars <- c("Drug", "conc_num", "conc_num_factor", "conc_num_factor_factor")
    group_vars <- c("OrfRep", "Gene", "Drug")
  }

  # Calculate WT statistics from df_bg
  wt_stats <- df_bg %>%
    group_by(across(all_of(bg_group_vars))) %>%
    summarise(
      WT_L = mean(mean_L, na.rm = TRUE),
      WT_sd_L = mean(sd_L, na.rm = TRUE),
      WT_K = mean(mean_K, na.rm = TRUE),
      WT_sd_K = mean(sd_K, na.rm = TRUE),
      WT_r = mean(mean_r, na.rm = TRUE),
      WT_sd_r = mean(sd_r, na.rm = TRUE),
      WT_AUC = mean(mean_AUC, na.rm = TRUE),
      WT_sd_AUC = mean(sd_AUC, na.rm = TRUE),
      .groups = "drop"
    )

  # Join WT statistics to df
  df <- df %>%
    left_join(wt_stats, by = bg_group_vars)

  # Compute mean values at zero concentration
  mean_zeroes <- df %>%
    filter(conc_num == 0) %>%
    group_by(across(all_of(group_vars))) %>%
    summarise(
      mean_L_zero = mean(mean_L, na.rm = TRUE),
      mean_K_zero = mean(mean_K, na.rm = TRUE),
      mean_r_zero = mean(mean_r, na.rm = TRUE),
      mean_AUC_zero = mean(mean_AUC, na.rm = TRUE),
      .groups = "drop"
    )

  df <- df %>%
    left_join(mean_zeroes, by = c(group_vars))
    
  # Calculate Raw Shifts and Z Shifts
  df <- df %>%
    mutate(
      Raw_Shift_L = mean_L_zero - WT_L,
      Raw_Shift_K = mean_K_zero - WT_K,
      Raw_Shift_r = mean_r_zero - WT_r,
      Raw_Shift_AUC = mean_AUC_zero - WT_AUC,
      Z_Shift_L = Raw_Shift_L / WT_sd_L,
      Z_Shift_K = Raw_Shift_K / WT_sd_K,
      Z_Shift_r = Raw_Shift_r / WT_sd_r,
      Z_Shift_AUC = Raw_Shift_AUC / WT_sd_AUC
    )
  
  calculations <- df %>%
    group_by(across(all_of(c(group_vars, "conc_num", "conc_num_factor", "conc_num_factor_factor")))) %>%
    mutate(
      NG_sum = sum(NG, na.rm = TRUE),
      DB_sum = sum(DB, na.rm = TRUE),
      SM_sum = sum(SM, na.rm = TRUE),

      # Expected values
      Exp_L = WT_L + Raw_Shift_L,
      Exp_K = WT_K + Raw_Shift_K,
      Exp_r = WT_r + Raw_Shift_r,
      Exp_AUC = WT_AUC + Raw_Shift_AUC,
      
      # Deltas
      Delta_L = mean_L - Exp_L,
      Delta_K = mean_K - Exp_K,
      Delta_r = mean_r - Exp_r,
      Delta_AUC = mean_AUC - Exp_AUC,

      # Adjust deltas for NG and SM
      Delta_L = if_else(NG == 1, mean_L - WT_L, Delta_L),
      Delta_K = if_else(NG == 1, mean_K - WT_K, Delta_K),
      Delta_r = if_else(NG == 1, mean_r - WT_r, Delta_r),
      Delta_AUC = if_else(NG == 1, mean_AUC - WT_AUC, Delta_AUC),
      Delta_L = if_else(SM == 1, mean_L - WT_L, Delta_L),

      # Calculate Z-scores
      Zscore_L = Delta_L / WT_sd_L,
      Zscore_K = Delta_K / WT_sd_K,
      Zscore_r = Delta_r / WT_sd_r,
      Zscore_AUC = Delta_AUC / WT_sd_AUC
    ) %>%
    ungroup()
    
  calculations <- calculations %>%
    group_by(across(all_of(group_vars))) %>%
    mutate(
      lm_L = map2(.x = Delta_L, .y = conc_num_factor, ~ if (all(is.na(.x)) || all(is.na(.y)) || length(.x[!is.na(.x)]) == 0) {
        list(intercept = NA, slope = NA, r_squared = NA, score = NA)
      } else {
        lm(.x ~ .y) %>% {
          list(
            intercept = coef(.)[1],
            slope = coef(.)[2],
            r_squared = summary(.)$r.squared,
            score = max_conc * coef(.)[2] + coef(.)[1]
          )
        }
      }),
      lm_K = map2(.x = Delta_K, .y = conc_num_factor, ~ if (all(is.na(.x)) || all(is.na(.y)) || length(.x[!is.na(.x)]) == 0) {
        list(intercept = NA, slope = NA, r_squared = NA, score = NA)
      } else {
        lm(.x ~ .y) %>% {
          list(
            intercept = coef(.)[1],
            slope = coef(.)[2],
            r_squared = summary(.)$r.squared,
            score = max_conc * coef(.)[2] + coef(.)[1]
          )
        }
      }),
      lm_r = map2(.x = Delta_r, .y = conc_num_factor, ~ if (all(is.na(.x)) || all(is.na(.y)) || length(.x[!is.na(.x)]) == 0) {
        list(intercept = NA, slope = NA, r_squared = NA, score = NA)
      } else {
        lm(.x ~ .y) %>% {
          list(
            intercept = coef(.)[1],
            slope = coef(.)[2],
            r_squared = summary(.)$r.squared,
            score = max_conc * coef(.)[2] + coef(.)[1]
          )
        }
      }),
      lm_AUC = map2(.x = Delta_AUC, .y = conc_num_factor, ~ if (all(is.na(.x)) || all(is.na(.y)) || length(.x[!is.na(.x)]) == 0) {
        list(intercept = NA, slope = NA, r_squared = NA, score = NA)
      } else {
        lm(.x ~ .y) %>% {
          list(
            intercept = coef(.)[1],
            slope = coef(.)[2],
            r_squared = summary(.)$r.squared,
            score = max_conc * coef(.)[2] + coef(.)[1]
          )
        }
      }),

      # Extract coefficients and statistics for each model
      lm_intercept_L = map_dbl(lm_L, "intercept"),
      lm_slope_L = map_dbl(lm_L, "slope"),
      R_Squared_L = map_dbl(lm_L, "r_squared"),
      lm_Score_L = map_dbl(lm_L, "score"),

      lm_intercept_K = map_dbl(lm_K, "intercept"),
      lm_slope_K = map_dbl(lm_K, "slope"),
      R_Squared_K = map_dbl(lm_K, "r_squared"),
      lm_Score_K = map_dbl(lm_K, "score"),

      lm_intercept_r = map_dbl(lm_r, "intercept"),
      lm_slope_r = map_dbl(lm_r, "slope"),
      R_Squared_r = map_dbl(lm_r, "r_squared"),
      lm_Score_r = map_dbl(lm_r, "score"),

      lm_intercept_AUC = map_dbl(lm_AUC, "intercept"),
      lm_slope_AUC = map_dbl(lm_AUC, "slope"),
      R_Squared_AUC = map_dbl(lm_AUC, "r_squared"),
      lm_Score_AUC = map_dbl(lm_AUC, "score")
) %>%
    select(-lm_L, -lm_K, -lm_r, -lm_AUC) %>%
    ungroup()

  # For interaction plot error bars
  delta_means_sds <- calculations %>%
    group_by(across(all_of(group_vars))) %>%
    summarise(
      mean_Delta_L = mean(Delta_L, na.rm = TRUE),
      mean_Delta_K = mean(Delta_K, na.rm = TRUE),
      mean_Delta_r = mean(Delta_r, na.rm = TRUE),
      mean_Delta_AUC = mean(Delta_AUC, na.rm = TRUE),
      sd_Delta_L = sd(Delta_L, na.rm = TRUE),
      sd_Delta_K = sd(Delta_K, na.rm = TRUE),
      sd_Delta_r = sd(Delta_r, na.rm = TRUE),
      sd_Delta_AUC = sd(Delta_AUC, na.rm = TRUE),
      .groups = "drop"
    )

  calculations <- calculations %>%
    left_join(delta_means_sds, by = group_vars)

  # Summary statistics for lm scores
  calculations <- calculations %>%
    mutate(
      lm_mean_L = mean(lm_Score_L, na.rm = TRUE),
      lm_sd_L = sd(lm_Score_L, na.rm = TRUE),
      lm_mean_K = mean(lm_Score_K, na.rm = TRUE),
      lm_sd_K = sd(lm_Score_K, na.rm = TRUE),
      lm_mean_r = mean(lm_Score_r, na.rm = TRUE),
      lm_sd_r = sd(lm_Score_r, na.rm = TRUE),
      lm_mean_AUC = mean(lm_Score_AUC, na.rm = TRUE),
      lm_sd_AUC = sd(lm_Score_AUC, na.rm = TRUE),
      Z_lm_L = (lm_Score_L - lm_mean_L) / lm_sd_L,
      Z_lm_K = (lm_Score_K - lm_mean_K) / lm_sd_K,
      Z_lm_r = (lm_Score_r - lm_mean_r) / lm_sd_r,
      Z_lm_AUC = (lm_Score_AUC - lm_mean_AUC) / lm_sd_AUC
    )

  # Build summary stats (interactions)
  interactions <- calculations %>%
    group_by(across(all_of(group_vars))) %>%
    summarise(

      num_non_removed_concs = total_conc_num - sum(DB, na.rm = TRUE) - 1,

      Sum_Z_Score_L = sum(Zscore_L, na.rm = TRUE),
      Sum_Z_Score_K = sum(Zscore_K, na.rm = TRUE),
      Sum_Z_Score_r = sum(Zscore_r, na.rm = TRUE),
      Sum_Z_Score_AUC = sum(Zscore_AUC, na.rm = TRUE),

      Avg_Zscore_L = Sum_Z_Score_L / first(num_non_removed_concs),
      Avg_Zscore_K = Sum_Z_Score_K / first(num_non_removed_concs),
      Avg_Zscore_r = Sum_Z_Score_r / first(num_non_removed_concs),
      Avg_Zscore_AUC = Sum_Z_Score_AUC / first(num_non_removed_concs),

      # R_Squared
      R_Squared_L = first(R_Squared_L),
      R_Squared_K = first(R_Squared_K),
      R_Squared_r = first(R_Squared_r),
      R_Squared_AUC = first(R_Squared_AUC),
      
      # Interaction Z-scores
      Z_lm_L = first(Z_lm_L),
      Z_lm_K = first(Z_lm_K),
      Z_lm_r = first(Z_lm_r),
      Z_lm_AUC = first(Z_lm_AUC),
      
      # Raw Shifts
      Raw_Shift_L = first(Raw_Shift_L),
      Raw_Shift_K = first(Raw_Shift_K),
      Raw_Shift_r = first(Raw_Shift_r),
      Raw_Shift_AUC = first(Raw_Shift_AUC),
      
      # Z Shifts
      Z_Shift_L = first(Z_Shift_L),
      Z_Shift_K = first(Z_Shift_K),
      Z_Shift_r = first(Z_Shift_r),
      Z_Shift_AUC = first(Z_Shift_AUC),

      # Gene-Gene Interaction
      lm_Score_L = first(lm_Score_L),
      lm_Score_K = first(lm_Score_K),
      lm_Score_r = first(lm_Score_r),
      lm_Score_AUC = first(lm_Score_AUC),

      # NG, DB, SM values
      NG_sum_int = sum(NG),
      DB_sum_int = sum(DB),
      SM_sum_int = sum(SM),
      .groups = "drop"
    ) %>%
    arrange(desc(Z_lm_L), desc(NG_sum_int))

  # Deletion data ranking and linear modeling
  if (type == "deletion") {
    interactions <- interactions %>%
      mutate(
        Avg_Zscore_L_adjusted = ifelse(is.na(Avg_Zscore_L), 0.001, Avg_Zscore_L),
        Avg_Zscore_K_adjusted = ifelse(is.na(Avg_Zscore_K), 0.001, Avg_Zscore_K),
        Avg_Zscore_r_adjusted = ifelse(is.na(Avg_Zscore_r), 0.001, Avg_Zscore_r),
        Avg_Zscore_AUC_adjusted = ifelse(is.na(Avg_Zscore_AUC), 0.001, Avg_Zscore_AUC),
        Z_lm_L_adjusted = ifelse(is.na(Z_lm_L), 0.001, Z_lm_L),
        Z_lm_K_adjusted = ifelse(is.na(Z_lm_K), 0.001, Z_lm_K),
        Z_lm_r_adjusted = ifelse(is.na(Z_lm_r), 0.001, Z_lm_r),
        Z_lm_AUC_adjusted = ifelse(is.na(Z_lm_AUC), 0.001, Z_lm_AUC)
      ) %>%
      mutate(
        Rank_L = rank(Avg_Zscore_L_adjusted),
        Rank_K = rank(Avg_Zscore_K_adjusted),
        Rank_r = rank(Avg_Zscore_r_adjusted),
        Rank_AUC = rank(Avg_Zscore_AUC_adjusted),
        Rank_lm_L = rank(Z_lm_L_adjusted),
        Rank_lm_K = rank(Z_lm_K_adjusted),
        Rank_lm_r = rank(Z_lm_r_adjusted),
        Rank_lm_AUC = rank(Z_lm_AUC_adjusted)
      ) %>%
      mutate(
        lm_R_squared_rank_L = summary(lm(Rank_lm_L ~ Rank_L, data = .))$r.squared,
        lm_R_squared_rank_K = summary(lm(Rank_lm_K ~ Rank_K, data = .))$r.squared,
        lm_R_squared_rank_r = summary(lm(Rank_lm_r ~ Rank_r, data = .))$r.squared,
        lm_R_squared_rank_AUC = summary(lm(Rank_lm_AUC ~ Rank_AUC, data = .))$r.squared
      )

    # Add overlap threshold categories based on Z-lm and Avg-Z scores
    interactions <- interactions %>%
      filter(!is.na(Z_lm_L) | !is.na(Avg_Zscore_L)) %>%
      mutate(
        Overlap = case_when(
          Z_lm_L >= overlap_threshold & Avg_Zscore_L >= overlap_threshold ~ "Deletion Enhancer Both",
          Z_lm_L <= -overlap_threshold & Avg_Zscore_L <= -overlap_threshold ~ "Deletion Suppressor Both",
          Z_lm_L >= overlap_threshold & Avg_Zscore_L <= overlap_threshold ~ "Deletion Enhancer lm only",
          Z_lm_L <= overlap_threshold & Avg_Zscore_L >= overlap_threshold ~ "Deletion Enhancer Avg Zscore only",
          Z_lm_L <= -overlap_threshold & Avg_Zscore_L >= -overlap_threshold ~ "Deletion Suppressor lm only",
          Z_lm_L >= -overlap_threshold & Avg_Zscore_L <= -overlap_threshold ~ "Deletion Suppressor Avg Zscore only",
          Z_lm_L >= overlap_threshold & Avg_Zscore_L <= -overlap_threshold ~ "Deletion Enhancer lm, Deletion Suppressor Avg Zscore",
          Z_lm_L <= -overlap_threshold & Avg_Zscore_L >= overlap_threshold ~ "Deletion Suppressor lm, Deletion Enhancer Avg Zscore",
          TRUE ~ "No Effect"
        ),
        lm_L = map2(.x = Z_lm_L, .y = Avg_Zscore_L, ~ if (all(is.na(.x)) || all(is.na(.y)) || length(.x[!is.na(.x)]) == 0) {
          list(intercept = NA, slope = NA, r_squared = NA, score = NA)
        } else {
          lm(.y ~ .x) %>% {
            list(
              intercept = coef(.)[1],
              slope = coef(.)[2],
              r_squared = summary(.)$r.squared,
              score = max_conc * coef(.)[2] + coef(.)[1]
            )
          }
        }),
        lm_K = map2(.x = Z_lm_K, .y = Avg_Zscore_K, ~ if (all(is.na(.x)) || all(is.na(.y)) || length(.x[!is.na(.x)]) == 0) {
          list(intercept = NA, slope = NA, r_squared = NA, score = NA)
        } else {
          lm(.y ~ .x) %>% {
            list(
              intercept = coef(.)[1],
              slope = coef(.)[2],
              r_squared = summary(.)$r.squared,
              score = max_conc * coef(.)[2] + coef(.)[1]
            )
          }
        }),
        lm_r = map2(.x = Z_lm_r, .y = Avg_Zscore_r, ~ if (all(is.na(.x)) || all(is.na(.y)) || length(.x[!is.na(.x)]) == 0) {
          list(intercept = NA, slope = NA, r_squared = NA, score = NA)
        } else {
          lm(.y ~ .x) %>% {
            list(
              intercept = coef(.)[1],
              slope = coef(.)[2],
              r_squared = summary(.)$r.squared,
              score = max_conc * coef(.)[2] + coef(.)[1]
            )
          }
        }),
        lm_AUC = map2(.x = Z_lm_AUC, .y = Avg_Zscore_AUC, ~ if (all(is.na(.x)) || all(is.na(.y)) || length(.x[!is.na(.x)]) == 0) {
          list(intercept = NA, slope = NA, r_squared = NA, score = NA)
        } else {
          lm(.y ~ .x) %>% {
            list(
              intercept = coef(.)[1],
              slope = coef(.)[2],
              r_squared = summary(.)$r.squared,
              score = max_conc * coef(.)[2] + coef(.)[1]
            )
          }
        }),

        # For correlation plots
        Z_lm_K_L = map2(.x = Z_lm_K, .y = Z_lm_L, ~ if (all(is.na(.x)) || all(is.na(.y)) || length(.x[!is.na(.x)]) == 0) {
          list(intercept = NA, slope = NA, r_squared = NA, score = NA)
        } else {
          lm(.y ~ .x) %>% {
            list(
              intercept = coef(.)[1],
              slope = coef(.)[2],
              r_squared = summary(.)$r.squared,
              score = max_conc * coef(.)[2] + coef(.)[1]
            )
          }
        }),
        Z_lm_r_L = map2(.x = Z_lm_r, .y = Z_lm_L, ~ if (all(is.na(.x)) || all(is.na(.y)) || length(.x[!is.na(.x)]) == 0) {
          list(intercept = NA, slope = NA, r_squared = NA, score = NA)
        } else {
          lm(.y ~ .x) %>% {
            list(
              intercept = coef(.)[1],
              slope = coef(.)[2],
              r_squared = summary(.)$r.squared,
              score = max_conc * coef(.)[2] + coef(.)[1]
            )
          }
        }),
        Z_lm_R_AUC_L = map2(.x = Z_lm_AUC, .y = Z_lm_L, ~ if (all(is.na(.x)) || all(is.na(.y)) || length(.x[!is.na(.x)]) == 0) {
          list(intercept = NA, slope = NA, r_squared = NA, score = NA)
        } else {
          lm(.y ~ .x) %>% {
            list(
              intercept = coef(.)[1],
              slope = coef(.)[2],
              r_squared = summary(.)$r.squared,
              score = max_conc * coef(.)[2] + coef(.)[1]
            )
          }
        }),
        Z_lm_R_r_K = map2(.x = Z_lm_r, .y = Z_lm_K, ~ if (all(is.na(.x)) || all(is.na(.y)) || length(.x[!is.na(.x)]) == 0) {
          list(intercept = NA, slope = NA, r_squared = NA, score = NA)
        } else {
          lm(.y ~ .x) %>% {
            list(
              intercept = coef(.)[1],
              slope = coef(.)[2],
              r_squared = summary(.)$r.squared,
              score = max_conc * coef(.)[2] + coef(.)[1]
            )
          }
        }),
        Z_lm_R_AUC_K = map2(.x = Z_lm_AUC, .y = Z_lm_K, ~ if (all(is.na(.x)) || all(is.na(.y)) || length(.x[!is.na(.x)]) == 0) {
          list(intercept = NA, slope = NA, r_squared = NA, score = NA)
        } else {
          lm(.y ~ .x) %>% {
            list(
              intercept = coef(.)[1],
              slope = coef(.)[2],
              r_squared = summary(.)$r.squared,
              score = max_conc * coef(.)[2] + coef(.)[1]
            )
          }
        }),
        Z_lm_R_AUC_r = map2(.x = Z_lm_AUC, .y = Z_lm_r, ~ if (all(is.na(.x)) || all(is.na(.y)) || length(.x[!is.na(.x)]) == 0) {
          list(intercept = NA, slope = NA, r_squared = NA, score = NA)
        } else {
          lm(.y ~ .x) %>% {
            list(
              intercept = coef(.)[1],
              slope = coef(.)[2],
              r_squared = summary(.)$r.squared,
              score = max_conc * coef(.)[2] + coef(.)[1]
            )
          }
        }),

        # Extract coefficients and statistics for each model
        lm_rank_intercept_L = map_dbl(lm_L, "intercept"),
        lm_rank_slope_L = map_dbl(lm_L, "slope"),
        R_Squared_L = map_dbl(lm_L, "r_squared"),
        lm_Score_L = map_dbl(lm_L, "score"),

        lm_intercept_K = map_dbl(lm_K, "intercept"),
        lm_slope_K = map_dbl(lm_K, "slope"),
        R_Squared_K = map_dbl(lm_K, "r_squared"),
        lm_Score_K = map_dbl(lm_K, "score"),

        lm_intercept_r = map_dbl(lm_r, "intercept"),
        lm_slope_r = map_dbl(lm_r, "slope"),
        R_Squared_r = map_dbl(lm_r, "r_squared"),
        lm_Score_r = map_dbl(lm_r, "score"),

        lm_intercept_AUC = map_dbl(lm_AUC, "intercept"),
        lm_slope_AUC = map_dbl(lm_AUC, "slope"),
        R_Squared_AUC = map_dbl(lm_AUC, "r_squared"),
        lm_Score_AUC = map_dbl(lm_AUC, "score"),

        Z_lm_intercept_K_L = map_dbl(Z_lm_K_L, "intercept"),
        Z_lm_slope_K_L = map_dbl(Z_lm_K_L, "slope"),
        Z_lm_R_squared_K_L = map_dbl(Z_lm_K_L, "r_squared"),
        Z_lm_Score_K_L = map_dbl(Z_lm_K_L, "score"),

        Z_lm_intercept_r_L = map_dbl(Z_lm_r_L, "intercept"),
        Z_lm_slope_r_L = map_dbl(Z_lm_r_L, "slope"),
        Z_lm_R_squared_r_L = map_dbl(Z_lm_r_L, "r_squared"),
        Z_lm_Score_r_L = map_dbl(Z_lm_r_L, "score"),

        Z_lm_intercept_R_AUC_L = map_dbl(Z_lm_R_AUC_L, "intercept"),
        Z_lm_slope_R_AUC_L = map_dbl(Z_lm_R_AUC_L, "slope"),
        Z_lm_R_squared_R_AUC_L = map_dbl(Z_lm_R_AUC_L, "r_squared"),
        Z_lm_Score_R_AUC_L = map_dbl(Z_lm_R_AUC_L, "score"),

        Z_lm_intercept_R_r_K = map_dbl(Z_lm_R_r_K, "intercept"),
        Z_lm_slope_R_r_K = map_dbl(Z_lm_R_r_K, "slope"),
        Z_lm_R_squared_R_r_K = map_dbl(Z_lm_R_r_K, "r_squared"),
        Z_lm_Score_R_r_K = map_dbl(Z_lm_R_r_K, "score"),

        Z_lm_intercept_R_AUC_K = map_dbl(Z_lm_R_AUC_K, "intercept"),
        Z_lm_slope_R_AUC_K = map_dbl(Z_lm_R_AUC_K, "slope"),
        Z_lm_R_squared_R_AUC_K = map_dbl(Z_lm_R_AUC_K, "r_squared"),
        Z_lm_Score_R_AUC_K = map_dbl(Z_lm_R_AUC_K, "score"),

        Z_lm_intercept_R_AUC_r = map_dbl(Z_lm_R_AUC_r, "intercept"),
        Z_lm_slope_R_AUC_r = map_dbl(Z_lm_R_AUC_r, "slope"),
        Z_lm_R_squared_R_AUC_r = map_dbl(Z_lm_R_AUC_r, "r_squared"),
        Z_lm_Score_R_AUC_r = map_dbl(Z_lm_R_AUC_r, "score")
      ) %>%
      select(
        -lm_L, -lm_K, -lm_r, -lm_AUC, -Z_lm_K_L, -Z_lm_r_L, -Z_lm_R_AUC_L,
        -Z_lm_R_r_K, -Z_lm_R_AUC_K, -Z_lm_R_AUC_r)
  } # end deletion-specific block

  # Create the final calculations and interactions dataframes with only required columns for csv output
  df_calculations <- calculations %>%
    select(
      all_of(group_vars),
      conc_num, conc_num_factor, conc_num_factor_factor, N,
      mean_L, median_L, sd_L, se_L,
      mean_K, median_K, sd_K, se_K,
      mean_r, median_r, sd_r, se_r,
      mean_AUC, median_AUC, sd_AUC, se_AUC,
      Raw_Shift_L, Raw_Shift_K, Raw_Shift_r, Raw_Shift_AUC,
      Z_Shift_L, Z_Shift_K, Z_Shift_r, Z_Shift_AUC,
      WT_L, WT_K, WT_r, WT_AUC,
      WT_sd_L, WT_sd_K, WT_sd_r, WT_sd_AUC,
      Exp_L, Exp_K, Exp_r, Exp_AUC,
      Delta_L, Delta_K, Delta_r, Delta_AUC,
      mean_Delta_L, mean_Delta_K, mean_Delta_r, mean_Delta_AUC,
      Zscore_L, Zscore_K, Zscore_r, Zscore_AUC,
      NG_sum, DB_sum, SM_sum
    ) %>%
      rename(NG = NG_sum, DB = DB_sum, SM = SM_sum)

  df_interactions <- interactions %>%
    select(
      all_of(group_vars),
      Avg_Zscore_L, Avg_Zscore_K, Avg_Zscore_r, Avg_Zscore_AUC,
      Sum_Z_Score_L, Sum_Z_Score_K, Sum_Z_Score_r, Sum_Z_Score_AUC,
      Z_lm_L, Z_lm_K, Z_lm_r, Z_lm_AUC,
      Raw_Shift_L, Raw_Shift_K, Raw_Shift_r, Raw_Shift_AUC,
      Z_Shift_L, Z_Shift_K, Z_Shift_r, Z_Shift_AUC,
      lm_Score_L, lm_Score_K, lm_Score_r, lm_Score_AUC,
      R_Squared_L, R_Squared_K, R_Squared_r, R_Squared_AUC,
      NG_sum_int, DB_sum_int, SM_sum_int
    ) %>%
      rename(NG = NG_sum_int, DB = DB_sum_int, SM = SM_sum_int)

  # Avoid column collision on left join for overlapping variables
  calculations_no_overlap <- calculations %>%
    select(-any_of(c("DB", "NG", "SM",
      # Don't need these anywhere so easier to remove
      "Raw_Shift_L", "Raw_Shift_K", "Raw_Shift_r", "Raw_Shift_AUC",
      "R_Squared_L", "R_Squared_K", "R_Squared_r", "R_Squared_AUC",
      # we need these for the interactions but the original code has the same names in both datasets
      "Z_Shift_L", "Z_Shift_K", "Z_Shift_r", "Z_Shift_AUC",
      "Z_lm_L", "Z_lm_K", "Z_lm_r", "Z_lm_AUC"
    )))

  full_data <- calculations_no_overlap %>%
    left_join(interactions, by = group_vars)

  # Return final dataframes
  return(list(
    calculations = df_calculations,
    interactions = df_interactions,
    full_data = full_data
  ))
}

generate_and_save_plots <- function(out_dir, filename, plot_configs, page_width = 12, page_height = 8) {
  message("Generating ", filename, ".pdf and ", filename, ".html")

  plot_groups <- if ("plots" %in% names(plot_configs)) {
    list(plot_configs)  # Single group
  } else {
    plot_configs  # Multiple groups
  }

  pdf(file.path(out_dir, paste0(filename, ".pdf")), width = page_width, height = page_height)

  for (group in plot_groups) {
    static_plots <- list()
    plotly_plots <- list()

    for (i in seq_along(group$plots)) {
      config <- group$plots[[i]]
      df <- config$df

      # Filter NAs
      if (!is.null(config$filter_na) && config$filter_na) {
        df <- df %>%
          filter(!is.na(!!sym(config$y_var)))
      }

      # TODO for now skip all NA plots NA data
      # Eventually add to own or filter_na block so we can handle selectively
      if (nrow(df) == 0) {
        message("Insufficient data for plot:", config$title)
        next  # skip plot if insufficient data is available
      }

      # Create initial aes mappings for all plot types
      aes_mapping <- aes(x = !!sym(config$x_var))  # required
      if (!is.null(config$y_var)) {
        aes_mapping <- modifyList(aes_mapping, aes(y = !!sym(config$y_var))) # optional for density/bar plots
      }
      if (!is.null(config$color_var)) {
        aes_mapping <- modifyList(aes_mapping, aes(color = !!sym(config$color_var))) # dynamic color_var
      } else if (!is.null(config$color)) {
        aes_mapping <- modifyList(aes_mapping, aes(color = config$color)) # static color
      }
      if (config$plot_type == "bar" && !is.null(config$color_var)) {
        aes_mapping <- modifyList(aes_mapping, aes(fill = !!sym(config$color_var))) # only fill bar plots
      }

      # Begin plot generation
      plot <- ggplot(df, aes_mapping) + theme_publication(legend_position = config$legend_position)

      plot <- switch(config$plot_type,
        "scatter" = generate_scatter_plot(plot, config),
        "box" = generate_boxplot(plot, config),
        "density" = plot + geom_density(),
        "bar" = plot + geom_bar(),
        plot # default
      )

      if (!is.null(config$title)) {
        plot <- plot + ggtitle(config$title)
        if (!is.null(config$title_size)) {
          plot <- plot + theme(plot.title = element_text(size = config$title_size))
        }
      }
      if (!is.null(config$x_label)) plot <- plot + xlab(config$x_label)
      if (!is.null(config$y_label)) plot <- plot + ylab(config$y_label)
      if (!is.null(config$coord_cartesian)) plot <- plot + coord_cartesian(ylim = config$coord_cartesian)

      #plotly_plot <- suppressWarnings(plotly::ggplotly(plot))

      static_plots[[i]] <- plot
      #plotly_plots[[i]] <- plotly_plot
    }

    grid_layout <- group$grid_layout
    if (!is.null(grid_layout)) {
      if (is.null(grid_layout$ncol)) {
        grid_layout$ncol <- 1
      }

      if (!is.null(grid_layout$ncol) && is.null(grid_layout$nrow)) {
        num_plots <- length(static_plots)
        grid_layout$nrow <- ceiling(num_plots / grid_layout$ncol)
      }
      
      # total_spots <- grid_layout$nrow * grid_layout$ncol
      # num_plots <- length(static_plots)

      # if (num_plots < total_spots) {
      #   message("Filling ", total_spots - num_plots, " empty spots with nullGrob()")
      #   static_plots <- c(static_plots, replicate(total_spots - num_plots, nullGrob(), simplify = FALSE))
      # }

      grid.arrange(
        grobs = static_plots,
        ncol = grid_layout$ncol,
        nrow = grid_layout$nrow
      )
      
    } else {
      for (plot in static_plots) {
        print(plot)
      }
    }
  }

  dev.off()

  # out_html_file <- file.path(out_dir, paste0(filename, ".html"))
  # message("Saving combined HTML file: ", out_html_file)
  # htmltools::save_html(
  #   htmltools::tagList(plotly_plots),
  #   file = out_html_file
  # )
}

generate_scatter_plot <- function(plot, config) {

  # Define the points
  shape <- if (!is.null(config$shape)) config$shape else 3
  size <- if (!is.null(config$size)) config$size else 1.5
  position <-
    if (!is.null(config$position) && config$position == "jitter") {
      position_jitter(width = 0.4, height = 0.1)
    } else {
      "identity"
    }

  plot <- plot + geom_point(
    shape = shape,
    size = size,
    position = position
  )

  # Add a cyan point for the reference data for correlation plots
  if (!is.null(config$cyan_points) && config$cyan_points) {
    plot <- plot + geom_point(
      aes(x = !!sym(config$x_var), y = !!sym(config$y_var)),
      data = config$df_reference,
      color = "cyan",
      shape = 3,
      size = 0.5
    )
  }

  # Add error bars if specified
  if (!is.null(config$error_bar) && config$error_bar) {
    # Check if custom columns are provided for y_mean and y_sd, or use the defaults
    y_mean_col <- if (!is.null(config$error_bar_params$y_mean_col)) {
      config$error_bar_params$y_mean_col
    } else {
      paste0("mean_", config$y_var)
    }

    y_sd_col <- if (!is.null(config$error_bar_params$y_sd_col)) {
      config$error_bar_params$y_sd_col
    } else {
      paste0("sd_", config$y_var)
    }

    # Use rlang to handle custom error bar calculations
    if (!is.null(config$error_bar_params$custom_error_bar)) {
      custom_ymin_expr <- rlang::parse_expr(config$error_bar_params$custom_error_bar$ymin)
      custom_ymax_expr <- rlang::parse_expr(config$error_bar_params$custom_error_bar$ymax)

      plot <- plot + geom_errorbar(
        aes(
          ymin = !!custom_ymin_expr,
          ymax = !!custom_ymax_expr
        ),
        color = config$error_bar_params$color,
        linewidth = ifelse(is.null(config$error_bar_params$linewidth), 0.1, config$error_bar_params$linewidth)
      )
    } else {
      # If no custom error bar formula, use the default or dynamic ones
      if (!is.null(config$color_var) && config$color_var %in% colnames(config$df)) {
        # Only use color_var if it's present in the dataframe
        plot <- plot + geom_errorbar(
          aes(
            ymin = !!sym(y_mean_col) - !!sym(y_sd_col),
            ymax = !!sym(y_mean_col) + !!sym(y_sd_col),
            color = !!sym(config$color_var)
          ),
          linewidth = 0.1
        )
      } else {
        # If color_var is missing, fall back to a default color or none
        plot <- plot + geom_errorbar(
          aes(
            ymin = !!sym(y_mean_col) - !!sym(y_sd_col),
            ymax = !!sym(y_mean_col) + !!sym(y_sd_col)
          ),
          color = config$error_bar_params$color, # use the provided color or default
          linewidth = ifelse(is.null(config$error_bar_params$linewidth), 0.1, config$error_bar_params$linewidth)
        )
      }
    }

    # Add the center point if the option is provided
    if (!is.null(config$error_bar_params$mean_point) && config$error_bar_params$mean_point) {
      if (!is.null(config$error_bar_params$color)) {
        plot <- plot + geom_point(
          aes(x = !!sym(config$x_var), y = !!sym(y_mean_col)),
          color = config$error_bar_params$color,
          shape = 16
        )
      } else {
        plot <- plot + geom_point(
          aes(x = !!sym(config$x_var), y = !!sym(y_mean_col)),
          shape = 16
        )
      }
    }
  }
  
  # Add linear regression line if specified
  if (!is.null(config$lm_line)) {
    # Extract necessary values
    intercept <- config$lm_line$intercept # required
    slope <- config$lm_line$slope # required
    xmin <- ifelse(!is.null(config$lm_line$xmin), config$lm_line$xmin, min(as.numeric(config$df[[config$x_var]])))
    xmax <- ifelse(!is.null(config$lm_line$xmax), config$lm_line$xmax, max(as.numeric(config$df[[config$x_var]])))
    color <- ifelse(!is.null(config$lm_line$color), config$lm_line$color, "blue")
    linewidth <- ifelse(!is.null(config$lm_line$linewidth), config$lm_line$linewidth, 1)
    ymin <- intercept + slope * xmin
    ymax <- intercept + slope * xmax
    
    # Ensure y-values are within y-limits (if any)
    if (!is.null(config$ylim_vals)) {
      ymin_within_limits <- ymin >= config$ylim_vals[1] && ymin <= config$ylim_vals[2]
      ymax_within_limits <- ymax >= config$ylim_vals[1] && ymax <= config$ylim_vals[2]

      # Adjust or skip based on whether the values fall within limits
      if (ymin_within_limits && ymax_within_limits) {
        plot <- plot + annotate(
          "segment",
          x = xmin,
          xend = xmax,
          y = ymin,
          yend = ymax,
          color = color,
          linewidth = linewidth,
        )
      } else {
        message("Skipping linear regression line due to y-values outside of limits")
      }
    } else {
      # If no y-limits are provided, proceed with the annotation
      plot <- plot + annotate(
        "segment",
        x = xmin,
        xend = xmax,
        y = ymin,
        yend = ymax,
        color = color,
        linewidth = linewidth
      )
    }
  }

  # Add SD Bands if specified
  if (!is.null(config$sd_band)) {
    plot <- plot +
      annotate(
        "rect",
        xmin = -Inf, xmax = Inf,
        ymin = config$sd_band, ymax = Inf,
        fill = ifelse(!is.null(config$fill_positive), config$fill_positive, "#542788"),
        alpha = ifelse(!is.null(config$alpha_positive), config$alpha_positive, 0.3)
      ) +
      annotate(
        "rect",
        xmin = -Inf, xmax = Inf,
        ymin = -config$sd_band, ymax = -Inf,
        fill = ifelse(!is.null(config$fill_negative), config$fill_negative, "orange"),
        alpha = ifelse(!is.null(config$alpha_negative), config$alpha_negative, 0.3)
      ) +
      geom_hline(
        yintercept = c(-config$sd_band, config$sd_band),
        color = ifelse(!is.null(config$hl_color), config$hl_color, "black")
      )
  }

  # # 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)
  #     )
  #   }
  # }

  # Customize X-axis if specified
  if (!is.null(config$x_breaks) && !is.null(config$x_labels) && !is.null(config$x_label)) {
    # Check if x_var is factor or character (for discrete x-axis)
    if (is.factor(plot$data[[config$x_var]]) || is.character(plot$data[[config$x_var]])) {
      plot <- plot +
        scale_x_discrete(
          name = config$x_label,
          breaks = config$x_breaks,
          labels = config$x_labels
        )
    } else {
      plot <- plot +
        scale_x_continuous(
          name = config$x_label,
          breaks = config$x_breaks,
          labels = config$x_labels
        )
    }
  }

  # Set Y-axis limits if specified
  if (!is.null(config$ylim_vals)) {
    plot <- plot + scale_y_continuous(limits = config$ylim_vals)
  }
  
  return(plot)
}

generate_boxplot <- function(plot, config) {
  # Convert x_var to a factor within aes mapping
  plot <- plot + geom_boxplot(aes(x = factor(!!sym(config$x_var))))

  # Customize X-axis if specified
  if (!is.null(config$x_breaks) && !is.null(config$x_labels) && !is.null(config$x_label)) {
    # Check if x_var is factor or character (for discrete x-axis)
    if (is.factor(plot$data[[config$x_var]]) || is.character(plot$data[[config$x_var]])) {
      plot <- plot +
        scale_x_discrete(
          name = config$x_label,
          breaks = config$x_breaks,
          labels = config$x_labels
        )
    } else {
      plot <- plot +
        scale_x_continuous(
          name = config$x_label,
          breaks = config$x_breaks,
          labels = config$x_labels
        )
    }
  }

  return(plot)
}

generate_plate_analysis_plot_configs <- function(variables, df_before = NULL, df_after = NULL,
  plot_type = "scatter", stages = c("before", "after")) {
  plot_configs <- list()
  
  for (var in variables) {
    for (stage in stages) {
      df_plot <- if (stage == "before") df_before else df_after

      # Check for non-finite values in the y-variable
      # df_plot_filtered <- df_plot %>% filter(is.finite(!!sym(var)))

      # Adjust settings based on plot_type
      plot_config <- list(
        df = df_plot,
        x_var = "scan",
        y_var = var,
        plot_type = plot_type,
        title = paste("Plate analysis by Drug Conc for", var, stage, "quality control"),
        color_var = "conc_num_factor_factor",
        size = 0.2,
        error_bar = (plot_type == "scatter"),
        legend_position = "bottom",
        filter_na = TRUE
      )

      # Add config to plots list
      plot_configs <- append(plot_configs, list(plot_config))
    }
  }

  return(list(plots = plot_configs))
}

generate_interaction_plot_configs <- function(df_summary, df_interactions, type) {

  # Define the y-limits for the plots
  limits_map <- list(
    L = c(0, 130),
    K = c(-20, 160),
    r = c(0, 1),
    AUC = c(0, 12500)
  )
  
  stats_plot_configs <- list()
  stats_boxplot_configs <- list()
  delta_plot_configs <- list()

  # Overall statistics plots
  OrfRep <- first(df_summary$OrfRep) # this should correspond to the reference strain

  for (plot_type in c("scatter", "box")) {

    for (var in names(limits_map)) {
      y_limits <- limits_map[[var]]
      y_span <- y_limits[2] - y_limits[1]

      # Common plot configuration
      plot_config <- list(
        df = df_summary,
        plot_type = plot_type,
        x_var = "conc_num_factor_factor",
        y_var = var,
        shape = 16,
        x_label = paste0("[", unique(df_summary$Drug)[1], "]"),
        coord_cartesian = y_limits,
        x_breaks = unique(df_summary$conc_num_factor_factor),
        x_labels = as.character(unique(df_summary$conc_num))
      )

      # Add specific configurations for scatter and box plots
      if (plot_type == "scatter") {
        plot_config$title <- sprintf("%s Scatter RF for %s with SD", OrfRep, var)
        plot_config$error_bar <- TRUE
        plot_config$error_bar_params <- list(
          color = "red",
          mean_point = TRUE,
          y_mean_col = paste0("mean_mean_", var),
          y_sd_col = paste0("mean_sd_", var)
        )
        plot_config$position <- "jitter"

        annotations <- list(
          list(x = 0.25, y = y_limits[1] + 0.08 * y_span, label = "                NG =", size = 4),
          list(x = 0.25, y = y_limits[1] + 0.04 * y_span, label = "                DB =", size = 4),
          list(x = 0.25, y = y_limits[1], label = "                SM =", size = 4)
        )

        for (x_val in unique(df_summary$conc_num_factor_factor)) {
          current_df <- df_summary %>% filter(!!sym(plot_config$x_var) == x_val)
          annotations <- append(annotations, list(
            list(x = x_val, y = y_limits[1] + 0.08 * y_span, label = first(current_df$NG, default = 0), size = 4),
            list(x = x_val, y = y_limits[1] + 0.04 * y_span, label = first(current_df$DB, default = 0), size = 4),
            list(x = x_val, y = y_limits[1], label = first(current_df$SM, default = 0), size = 4)
          ))
        }

        plot_config$annotations <- annotations
        stats_plot_configs <- append(stats_plot_configs, list(plot_config))

      } else if (plot_type == "box") {
        plot_config$title <- sprintf("%s Box RF for %s with SD", OrfRep, var)
        plot_config$position <- "dodge"

        stats_boxplot_configs <- append(stats_boxplot_configs, list(plot_config))
      }
    }
  }

  # Delta interaction plots
  delta_limits_map <- list(
    L = c(-60, 60),
    K = c(-60, 60),
    r = c(-0.6, 0.6),
    AUC = c(-6000, 6000)
  )

  # Select the data grouping by data type
  if (type == "reference") {
    group_vars <- c("OrfRep", "Gene", "num")
  } else if (type == "deletion") {
    group_vars <- c("OrfRep", "Gene")
  }

  grouped_data <- df_interactions %>%
    group_by(across(all_of(group_vars))) %>%
    group_split()

  for (group_data in grouped_data) {
    # Build the plot title
    OrfRep <- first(group_data$OrfRep)
    Gene <- first(group_data$Gene)
    if (type == "reference") {
      num <- if ("num" %in% names(group_data)) first(group_data$num) else ""
      OrfRepTitle <- paste(OrfRep, Gene, num, sep = "_")
    } else if (type == "deletion") {
      OrfRepTitle <- OrfRep
    }

    for (var in names(delta_limits_map)) {
      y_limits <- delta_limits_map[[var]]
      y_span <- y_limits[2] - y_limits[1]
      y_var_name <- paste0("Delta_", var)

      # Anti-filter to select out-of-bounds rows
      out_of_bounds <- group_data %>%
        filter(is.na(!!sym(y_var_name)) |
        !!sym(y_var_name) < y_limits[1] |
        !!sym(y_var_name) > y_limits[2])
      
      if (nrow(out_of_bounds) > 0) {
        message(sprintf("Filtered %d row(s) from '%s' because %s is outside of y-limits: [%f, %f]",
          nrow(out_of_bounds), OrfRepTitle, y_var_name, y_limits[1], y_limits[2]
        ))
      }

      # Do the actual filtering
      group_data_filtered <- group_data %>%
        filter(!is.na(!!sym(y_var_name)) &
          !!sym(y_var_name) >= y_limits[1] &
          !!sym(y_var_name) <= y_limits[2])

      if (nrow(group_data_filtered) == 0) {
        message("Insufficient data for plot: ", OrfRepTitle, " ", var)
        next  # skip plot if insufficient data is available
      }
      
      WT_sd_value <- first(group_data_filtered[[paste0("WT_sd_", var)]], default = 0)
      Z_Shift_value <- round(first(group_data_filtered[[paste0("Z_Shift_", var)]], default = 0), 2)
      Z_lm_value <- round(first(group_data_filtered[[paste0("Z_lm_", var)]], default = 0), 2)
      R_squared_value <- round(first(group_data_filtered[[paste0("R_Squared_", var)]], default = 0), 2)
      
      NG_value <- first(group_data_filtered$NG, default = 0)
      DB_value <- first(group_data_filtered$DB, default = 0)
      SM_value <- first(group_data_filtered$SM, default = 0)

      lm_intercept_col <- paste0("lm_intercept_", var)
      lm_slope_col <- paste0("lm_slope_", var)
      lm_intercept <- first(group_data_filtered[[lm_intercept_col]], default = 0)
      lm_slope <- first(group_data_filtered[[lm_slope_col]], default = 0)
      
      plot_config <- list(
        df = group_data_filtered,
        plot_type = "scatter",
        x_var = "conc_num_factor_factor",
        y_var = y_var_name,
        x_label = paste0("[", unique(df_summary$Drug)[1], "]"),
        shape = 16,
        title = paste(OrfRepTitle, Gene, sep = "      "),
        title_size = rel(1.4),
        coord_cartesian = y_limits,
        annotations = list(
          list(x = 1, y = y_limits[2] - 0.1 * y_span, label = paste("      ZShift =", round(Z_Shift_value, 2))),
          list(x = 1, y = y_limits[2] - 0.2 * y_span, label = paste("              lm ZScore =", round(Z_lm_value, 2))),
          # list(x = 1, y = y_limits[2] - 0.3 * y_span, label = paste("             R-squared =", round(R_squared_value, 2))),
          list(x = 1, y = y_limits[1] + 0.1 * y_span, label = paste("NG =", NG_value)),
          list(x = 1, y = y_limits[1] + 0.05 * y_span, label = paste("DB =", DB_value)),
          list(x = 1, y = y_limits[1], label = paste("SM =", SM_value))
        ),
        error_bar = TRUE,
        error_bar_params = list(
          custom_error_bar = list(
            ymin = paste0("0 - 2 * WT_sd_", var),
            ymax = paste0("0 + 2 * WT_sd_", var)
          ),
          color = "gray70",
          linewidth = 0.5
        ),
        x_breaks = unique(group_data_filtered$conc_num_factor_factor),
        x_labels = as.character(unique(group_data_filtered$conc_num)),
        ylim_vals = y_limits,
        lm_line = list(
          intercept = lm_intercept,
          slope = lm_slope,
          color = "blue",
          linewidth = 0.8
        )
      )
      delta_plot_configs <- append(delta_plot_configs, list(plot_config))
    }
  }
  
  # Group delta plots in chunks of 12 per page
  chunk_size <- 12
  delta_plot_chunks <- split(delta_plot_configs, ceiling(seq_along(delta_plot_configs) / chunk_size))
  
  return(c(
    list(list(grid_layout = list(ncol = 2), plots = stats_plot_configs)),
    list(list(grid_layout = list(ncol = 2), plots = stats_boxplot_configs)),
    lapply(delta_plot_chunks, function(chunk) list(grid_layout = list(ncol = 4), plots = chunk))
  ))
}

generate_rank_plot_configs <- function(df, is_lm = FALSE, filter_na = FALSE, overlap_color = FALSE) {
  sd_bands <- c(1, 2, 3)
  plot_configs <- list()
  
  variables <- c("L", "K")

  # Helper function to create a rank plot configuration
  create_plot_config <- function(variable, rank_var, zscore_var, y_label, sd_band, filter_na, with_annotations = TRUE) {
    num_enhancers <- sum(df[[zscore_var]] >= sd_band, na.rm = TRUE)
    num_suppressors <- sum(df[[zscore_var]] <= -sd_band, na.rm = TRUE)

    # Default plot config
    plot_config <- list(
      df = df,
      x_var = rank_var,
      y_var = zscore_var,
      x_label = "Rank",
      y_label = y_label,
      plot_type = "scatter",
      title = paste(y_label, "vs. Rank for", variable, "above", sd_band, "SD"),
      sd_band = sd_band,
      fill_positive = "#542788",
      fill_negative = "orange",
      alpha_positive = 0.3,
      alpha_negative = 0.3,
      shape = 3,
      size = 0.1,
      filter_na = filter_na,
      legend_position = "none"
    )
    
    # Selectively add annotations
    if (with_annotations) {
      plot_config$annotations <- list(
        list(
          x = nrow(df) / 2,
          y = 10,
          label = paste("Deletion Enhancers =", num_enhancers)
        ),
        list(
          x = nrow(df) / 2,
          y = -10,
          label = paste("Deletion Suppressors =", num_suppressors)
        )
      )
    }

    return(plot_config)
  }

  # Generate plots for each variable
  for (variable in variables) {
    rank_var <- if (is_lm) paste0("Rank_lm_", variable) else paste0("Rank_", variable)
    zscore_var <- if (is_lm) paste0("Z_lm_", variable) else paste0("Avg_Zscore_", variable)
    y_label <- if (is_lm) paste("Int Z score", variable) else paste("Avg Z score", variable)
    
    # Loop through SD bands
    for (sd_band in sd_bands) {
      # Create plot with annotations
      plot_configs[[length(plot_configs) + 1]] <-
        create_plot_config(variable, rank_var, zscore_var, y_label, sd_band, filter_na, with_annotations = TRUE)
      
      # Create plot without annotations
      plot_configs[[length(plot_configs) + 1]] <-
        create_plot_config(variable, rank_var, zscore_var, y_label, sd_band, filter_na, with_annotations = FALSE)
    }
  }

  # Group delta plots in chunks of 6 per page
  chunk_size <- 6
  plot_chunks <- split(plot_configs, ceiling(seq_along(plot_configs) / chunk_size))

  return(c(
    lapply(plot_chunks, function(chunk) list(grid_layout = list(ncol = 3), plots = chunk))
  ))
}

generate_correlation_plot_configs <- function(df, df_reference) {
  # Define relationships for different-variable correlations
  relationships <- list(
    list(x = "L", y = "K"), # x-var is predictor, y-var is reponse
    list(x = "L", y = "r"),
    list(x = "L", y = "AUC"),
    list(x = "K", y = "r"),
    list(x = "K", y = "AUC"),
    list(x = "r", y = "AUC")
  )

  # Filter both dataframes for missing linear model zscores for plotting
  df <- df %>%
    filter(!is.na(Z_lm_L))
  df_reference <- df_reference %>%
    filter(!is.na(Z_lm_L))

  plot_configs <- list()

  # Iterate over the option to highlight cyan points (TRUE/FALSE)
  highlight_cyan_options <- c(FALSE, TRUE)

  for (highlight_cyan in highlight_cyan_options) {
    for (rel in relationships) {
      # Extract relevant variable names for Z_lm values
      x_var <- paste0("Z_lm_", rel$x) # predictor
      y_var <- paste0("Z_lm_", rel$y) # response

      # Find the max and min of both dataframes for printing linear regression line
      xmin <- min(c(min(df[[x_var]], na.rm = TRUE), min(df_reference[[x_var]], na.rm = TRUE)), na.rm = TRUE)
      xmax <- max(c(max(df[[x_var]], na.rm = TRUE), max(df_reference[[x_var]], na.rm = TRUE)), na.rm = TRUE)

      # Extract the R-squared, intercept, and slope from the df (first value)
      intercept <- df[[paste0("Z_lm_intercept_", rel$y, "_", rel$x)]][1]
      slope <- df[[paste0("Z_lm_slope_", rel$y, "_", rel$x)]][1]
      r_squared <- df[[paste0("Z_lm_R_squared_", rel$y, "_", rel$x)]][1]

      # Generate the label for the plot
      plot_label <- paste("Interaction", rel$x, "vs.", rel$y)

      # Construct plot config
      plot_config <- list(
        df = df,
        df_reference = df_reference,
        x_var = x_var,
        y_var = y_var,
        plot_type = "scatter",
        title = plot_label,
        annotations = list(
          list(
            x = mean(df[[x_var]], na.rm = TRUE),
            y = mean(df[[y_var]], na.rm = TRUE),
            label = paste("R-squared =", round(r_squared, 3))
          )
        ),
        lm_line = list(
          intercept = intercept,
          slope = slope,
          color = "tomato3",
          linewidth = 0.8,
          xmin = xmin,
          xmax = xmax
        ),
        color = "gray70",
        filter_na = TRUE,
        cyan_points = highlight_cyan # include cyan points or not based on the loop
      )

      plot_configs <- append(plot_configs, list(plot_config))
    }
  }

  return(list(plots = plot_configs))
}

main <- function() {
  lapply(names(args$experiments), function(exp_name) {
    exp <- args$experiments[[exp_name]]
    exp_path <- exp$path
    exp_sd <- exp$sd
    out_dir <- file.path(exp_path, "zscores")
    out_dir_qc <- file.path(exp_path, "zscores", "qc")
    dir.create(out_dir, recursive = TRUE, showWarnings = FALSE)
    dir.create(out_dir_qc, recursive = TRUE, showWarnings = FALSE)
    
    # Each list of plots corresponds to a separate file
    message("Loading and filtering data for experiment: ", exp_name)
    df <- load_and_filter_data(args$easy_results_file, sd = exp_sd) %>%
      update_gene_names(args$sgd_gene_list) %>%
      as_tibble()

    l_vs_k_plot_configs <- list(
      plots = list(
        list(
          df = df,
          x_var = "L",
          y_var = "K",
          plot_type = "scatter",
          tooltip_vars = c("OrfRep", "Gene", "delta_bg"),
          title = "Raw L vs K before quality control",
          color_var = "conc_num_factor_factor",
          error_bar = FALSE,
          legend_position = "right"
        )
      )
    )

    message("Calculating summary statistics before quality control")
    df_stats <- calculate_summary_stats( # formerly X_stats_ALL
      df = df,
      variables = c("L", "K", "r", "AUC", "delta_bg"),
      group_vars = c("conc_num", "conc_num_factor_factor"))$df_with_stats

    frequency_delta_bg_plot_configs <- list(
      plots = list(
        list(
          df = df_stats,
          x_var = "delta_bg",
          y_var = NULL,
          plot_type = "density",
          title = "Density plot for Delta Background by [Drug] (All Data)",
          color_var = "conc_num_factor_factor",
          x_label = "Delta Background",
          y_label = "Density",
          error_bar = FALSE,
          legend_position = "right"
        ),
        list(
          df = df_stats,
          x_var = "delta_bg",
          y_var = NULL,
          plot_type = "bar",
          title = "Bar plot for Delta Background by [Drug] (All Data)",
          color_var = "conc_num_factor_factor",
          x_label = "Delta Background",
          y_label = "Count",
          error_bar = FALSE,
          legend_position = "right"
        )
      )
    )

    message("Filtering rows above delta background tolerance for plotting")
    df_above_tolerance <- df %>% filter(DB == 1)

    above_threshold_plot_configs <- list(
      plots = list(
        list(
          df = df_above_tolerance,
          x_var = "L",
          y_var = "K",
          plot_type = "scatter",
          tooltip_vars = c("OrfRep", "Gene", "delta_bg"),
          title = paste("Raw L vs K for strains above Delta Background threshold of",
            round(df_above_tolerance$delta_bg_tolerance[[1]], 3), "or above"),
          color_var = "conc_num_factor_factor",
          position = "jitter",
          annotations = list(
            list(
              x = median(df_above_tolerance$L, na.rm = TRUE) / 2,
              y = median(df_above_tolerance$K, na.rm = TRUE) / 2,
              label = paste("# strains above Delta Background tolerance =", nrow(df_above_tolerance))
            )
          ),
          error_bar = FALSE,
          legend_position = "right"
        )
      )
    )
   
    message("Setting rows above delta background tolerance to NA")
    df_na <- df %>% mutate(across(all_of(c("L", "K", "r", "AUC", "delta_bg")), ~ ifelse(DB == 1, NA, .))) # formerly X
    
    message("Calculating summary statistics across all strains")
    ss <- calculate_summary_stats(
      df = df_na,
      variables = c("L", "K", "r", "AUC", "delta_bg"),
      group_vars = c("conc_num", "conc_num_factor_factor"))
    df_na_ss <- ss$summary_stats
    df_na_stats <- ss$df_with_stats # formerly X_stats_ALL
    write.csv(df_na_ss, file = file.path(out_dir, "summary_stats_all_strains.csv"), row.names = FALSE)
    # This can help bypass missing values ggplot warnings during testing
    df_na_stats_filtered <- df_na_stats %>% filter(if_all(all_of(c("L", "K", "r", "AUC", "delta_bg")), is.finite))

    message("Calculating summary statistics excluding zero values")
    df_no_zeros <- df_na %>% filter(L > 0) # formerly X_noZero
    df_no_zeros_stats <- calculate_summary_stats(
      df = df_no_zeros,
      variables = c("L", "K", "r", "AUC", "delta_bg"),
      group_vars = c("conc_num", "conc_num_factor_factor")
    )$df_with_stats

    message("Filtering by 2SD of K")
    df_na_within_2sd_k <- df_na_stats %>%
      filter(K >= (mean_K - 2 * sd_K) & K <= (mean_K + 2 * sd_K))
    df_na_outside_2sd_k <- df_na_stats %>%
      filter(K < (mean_K - 2 * sd_K) | K > (mean_K + 2 * sd_K))

    message("Calculating summary statistics for L within 2SD of K")
    # TODO We're omitting the original z_max calculation, not sure if needed?
    ss <- calculate_summary_stats(df_na_within_2sd_k, "L", # formerly X_stats_BY_L_within_2SD_K
      group_vars = c("conc_num", "conc_num_factor_factor"))$summary_stats
    write.csv(ss,
      file = file.path(out_dir_qc, "max_observed_L_vals_for_spots_within_2SD_K.csv"),
      row.names = FALSE)
    
    message("Calculating summary statistics for L outside 2SD of K")
    ss <- calculate_summary_stats(df_na_outside_2sd_k, "L", # formerly X_stats_BY_L_outside_2SD_K
      group_vars = c("conc_num", "conc_num_factor_factor"))
    df_na_l_outside_2sd_k_stats <- ss$df_with_stats
    write.csv(ss$summary_stats,
      file = file.path(out_dir, "max_observed_L_vals_for_spots_outside_2SD_K.csv"),
      row.names = FALSE)

    plate_analysis_plot_configs <- generate_plate_analysis_plot_configs(
      variables = c("L", "K", "r", "AUC", "delta_bg"),
      df_before = df_stats,
      df_after = df_na_stats_filtered
    )

    plate_analysis_boxplot_configs <- generate_plate_analysis_plot_configs(
      variables = c("L", "K", "r", "AUC", "delta_bg"),
      df_before = df_stats,
      df_after = df_na_stats_filtered,
      plot_type = "box"
    )

    plate_analysis_no_zeros_plot_configs <- generate_plate_analysis_plot_configs(
      variables = c("L", "K", "r", "AUC", "delta_bg"),
      stages = c("after"),  # Only after QC
      df_after = df_no_zeros_stats
    )

    plate_analysis_no_zeros_boxplot_configs <- generate_plate_analysis_plot_configs(
      variables = c("L", "K", "r", "AUC", "delta_bg"),
      stages = c("after"),  # Only after QC
      df_after = df_no_zeros_stats,
      plot_type = "box"
    )

    l_outside_2sd_k_plot_configs <- list(
      plots = list(
        list(
          df = df_na_l_outside_2sd_k_stats,
          x_var = "L",
          y_var = "K",
          plot_type = "scatter",
          title = "Raw L vs K for strains falling outside 2 SD of the K mean at each Conc",
          color_var = "conc_num_factor_factor",
          position = "jitter",
          tooltip_vars = c("OrfRep", "Gene", "delta_bg"),
          annotations = list(
            list(
              x = median(df_na_l_outside_2sd_k_stats$L, na.rm = TRUE) / 2,
              y = median(df_na_l_outside_2sd_k_stats$K, na.rm = TRUE) / 2,
              label = paste("Total strains:", nrow(df_na_l_outside_2sd_k_stats))
            )
          ),
          error_bar = FALSE,
          legend_position = "right"
        )
      )
    )

    delta_bg_outside_2sd_k_plot_configs <- list(
      plots = list(
        list(
          df = df_na_l_outside_2sd_k_stats,
          x_var = "delta_bg",
          x_label = "Delta Background",
          y_var = "K",
          plot_type = "scatter",
          title = "Delta Background vs K for strains falling outside 2 SD of K",
          color_var = "conc_num_factor_factor",
          position = "jitter",
          tooltip_vars = c("OrfRep", "Gene", "delta_bg"),
          annotations = list(
            list(
              x = 0.05,
              y = 0.95,
              hjust = 0,
              vjust = 1,
              label = paste("Total strains:", nrow(df_na_l_outside_2sd_k_stats)),
              size = 5
            )
          ),
          error_bar = FALSE,
          legend_position = "right"
        )
      )
    )

    message("Generating quality control plots in parallel")
    # future::plan(future::multicore, workers = parallel::detectCores())
    future::plan(future::multisession, workers = 3) # generate 3 plot files in parallel

    plot_configs <- list(
      list(out_dir = out_dir_qc, filename = "L_vs_K_before_quality_control",
        plot_configs = l_vs_k_plot_configs, page_width = 12, page_height = 8),
      list(out_dir = out_dir_qc, filename = "frequency_delta_background",
        plot_configs = frequency_delta_bg_plot_configs, page_width = 12, page_height = 8),
      list(out_dir = out_dir_qc, filename = "L_vs_K_above_threshold",
        plot_configs = above_threshold_plot_configs, page_width = 12, page_height = 8),
      list(out_dir = out_dir_qc, filename = "plate_analysis",
        plot_configs = plate_analysis_plot_configs, page_width = 14, page_height = 9),
      list(out_dir = out_dir_qc, filename = "plate_analysis_boxplots",
        plot_configs = plate_analysis_boxplot_configs, page_width = 18, page_height = 9),
      list(out_dir = out_dir_qc, filename = "plate_analysis_no_zeros",
        plot_configs = plate_analysis_no_zeros_plot_configs, page_width = 14, page_height = 9),
      list(out_dir = out_dir_qc, filename = "plate_analysis_no_zeros_boxplots",
        plot_configs = plate_analysis_no_zeros_boxplot_configs, page_width = 18, page_height = 9),
      list(out_dir = out_dir_qc, filename = "L_vs_K_for_strains_2SD_outside_mean_K",
        plot_configs = l_outside_2sd_k_plot_configs, page_width = 10, page_height = 8),
      list(out_dir = out_dir_qc, filename = "delta_background_vs_K_for_strains_2SD_outside_mean_K",
        plot_configs = delta_bg_outside_2sd_k_plot_configs, page_width = 10, page_height = 8)
    )

    # Parallelize background and quality control plot generation
    # furrr::future_map(plot_configs, function(config) {
    #   generate_and_save_plots(config$out_dir, config$filename, config$plot_configs,
    #     page_width = config$page_width, page_height = config$page_height)
    # }, .options = furrr_options(seed = TRUE))

    # Loop over background strains
    # TODO currently only tested against one strain, if we want to do multiple strains we'll
    # have to rename or group the output files by dir or something so they don't get clobbered
    bg_strains <- c("YDL227C")
    lapply(bg_strains, function(strain) {
      message("Processing background strain: ", strain)
      
      # Handle missing data by setting zero values to NA
      # and then removing any rows with NA in L col
      df_bg <- df_na %>%
        filter(OrfRep == strain) %>%
        mutate(
          L = if_else(L == 0, NA, L),
          K = if_else(K == 0, NA, K),
          r = if_else(r == 0, NA, r),
          AUC = if_else(AUC == 0, NA, AUC)
        ) %>%
        filter(!is.na(L))
      
      message("Calculating background summary statistics")
      ss_bg <- calculate_summary_stats(df_bg, c("L", "K", "r", "AUC", "delta_bg"), # formerly X_stats_BY
        group_vars = c("OrfRep", "Drug", "conc_num", "conc_num_factor_factor"))
      summary_stats_bg <- ss_bg$summary_stats
      df_bg_stats <- ss_bg$df_with_stats
      write.csv(
        summary_stats_bg,
        file = file.path(out_dir, paste0("summary_stats_background_strain_", strain, ".csv")),
        row.names = FALSE)
      
      message("Setting missing reference values to the highest theoretical value at each drug conc for L")
      df_reference <- df_na_stats %>% # formerly X2_RF
        filter(OrfRep == strain) %>%
        filter(!is.na(L)) %>%
        group_by(OrfRep, Drug, conc_num, conc_num_factor_factor) %>%
        mutate(
          max_l_theoretical = max(max_L, na.rm = TRUE),
          L = ifelse(L == 0 & !is.na(L) & conc_num > 0, max_l_theoretical, L),
          SM = ifelse(L >= max_l_theoretical & !is.na(L) & conc_num > 0, 1, 0),
          L = ifelse(L >= max_l_theoretical & !is.na(L) & conc_num > 0, max_l_theoretical, L)) %>%
        ungroup()

      message("Calculating reference strain summary statistics")
      df_reference_summary_stats <- calculate_summary_stats( # formerly X_stats_X2_RF
        df = df_reference,
        variables = c("L", "K", "r", "AUC"),
        group_vars = c("OrfRep", "Drug", "conc_num", "conc_num_factor_factor")
      )$df_with_stats

      # Summarise statistics for error bars
      df_reference_summary_stats <- df_reference_summary_stats %>%
        group_by(OrfRep, Drug, conc_num, conc_num_factor_factor) %>%
        mutate(
          mean_mean_L = first(mean_L),
          mean_sd_L = first(sd_L),
          mean_mean_K = first(mean_K),
          mean_sd_K = first(sd_K),
          mean_mean_r = first(mean_r),
          mean_sd_r = first(sd_r),
          mean_mean_AUC = first(mean_AUC),
          mean_sd_AUC = first(sd_AUC),
          .groups = "drop"
        )

      message("Calculating reference strain interaction summary statistics") # formerly X_stats_interaction
      df_reference_interaction_stats <- calculate_summary_stats(
        df = df_reference,
        variables = c("L", "K", "r", "AUC"),
        group_vars = c("OrfRep", "Gene", "num", "Drug", "conc_num", "conc_num_factor_factor")
        )$df_with_stats
      
      message("Calculating reference strain interaction scores")
      reference_results <- calculate_interaction_scores(df_reference_interaction_stats, df_bg_stats, "reference")
      df_reference_interactions_joined <- reference_results$full_data
      df_reference_interactions <- reference_results$interactions
      write.csv(reference_results$calculations, file = file.path(out_dir, "zscore_calculations_reference.csv"), row.names = FALSE)
      write.csv(df_reference_interactions, file = file.path(out_dir, "zscore_interactions_reference.csv"), row.names = FALSE)

      # message("Generating reference interaction plots")
      # reference_plot_configs <- generate_interaction_plot_configs(df_reference_summary_stats, df_reference_interactions_joined, "reference")
      # generate_and_save_plots(out_dir, "interaction_plots_reference", reference_plot_configs, page_width = 16, page_height = 16)

      message("Setting missing deletion values to the highest theoretical value at each drug conc for L")
      df_deletion <- df_na_stats %>% # formerly X2
        filter(OrfRep != strain) %>%
        filter(!is.na(L)) %>%
        group_by(OrfRep, Gene, conc_num, conc_num_factor_factor) %>%
        mutate(
          max_l_theoretical = max(max_L, na.rm = TRUE),
          L = ifelse(L == 0 & !is.na(L) & conc_num > 0, max_l_theoretical, L),
          SM = ifelse(L >= max_l_theoretical & !is.na(L) & conc_num > 0, 1, SM),
          L = ifelse(L >= max_l_theoretical & !is.na(L) & conc_num > 0, max_l_theoretical, L)) %>%
        ungroup()

      message("Calculating deletion strain(s) interaction summary statistics")
      df_deletion_stats <- calculate_summary_stats(
        df = df_deletion,
        variables = c("L", "K", "r", "AUC"),
        group_vars = c("OrfRep", "Gene", "Drug", "conc_num", "conc_num_factor_factor")
        )$df_with_stats

      message("Calculating deletion strain(s) interactions scores")
      deletion_results <- calculate_interaction_scores(df_deletion_stats, df_bg_stats, "deletion")
      df_interactions <- deletion_results$interactions
      df_interactions_joined <- deletion_results$full_data
      write.csv(deletion_results$calculations, file = file.path(out_dir, "zscore_calculations.csv"), row.names = FALSE)
      write.csv(df_interactions, file = file.path(out_dir, "zscore_interactions.csv"), row.names = FALSE)

      # message("Generating deletion interaction plots")
      # deletion_plot_configs <- generate_interaction_plot_configs(df_reference_summary_stats, df_interactions_joined, "deletion")
      # generate_and_save_plots(out_dir, "interaction_plots", deletion_plot_configs, page_width = 16, page_height = 16)

      # message("Writing enhancer/suppressor csv files")
      # interaction_threshold <- 2  # TODO add to study config?
      # enhancer_condition_L <- df_interactions$Avg_Zscore_L >= interaction_threshold
      # suppressor_condition_L <- df_interactions$Avg_Zscore_L <= -interaction_threshold
      # enhancer_condition_K <- df_interactions$Avg_Zscore_K >= interaction_threshold
      # suppressor_condition_K <- df_interactions$Avg_Zscore_K <= -interaction_threshold
      # enhancers_L <- df_interactions[enhancer_condition_L, ]
      # suppressors_L <- df_interactions[suppressor_condition_L, ]
      # enhancers_K <- df_interactions[enhancer_condition_K, ]
      # suppressors_K <- df_interactions[suppressor_condition_K, ]
      # enhancers_and_suppressors_L <- df_interactions[enhancer_condition_L | suppressor_condition_L, ]
      # enhancers_and_suppressors_K <- df_interactions[enhancer_condition_K | suppressor_condition_K, ]
      # write.csv(enhancers_L, file = file.path(out_dir, "zscore_interactions_deletion_enhancers_L.csv"), row.names = FALSE)
      # write.csv(suppressors_L, file = file.path(out_dir, "zscore_interactions_deletion_suppressors_L.csv"), row.names = FALSE)
      # write.csv(enhancers_K, file = file.path(out_dir, "zscore_interactions_deletion_enhancers_K.csv"), row.names = FALSE)
      # write.csv(suppressors_K, file = file.path(out_dir, "zscore_interactions_deletion_suppressors_K.csv"), row.names = FALSE)
      # write.csv(enhancers_and_suppressors_L,
      #   file = file.path(out_dir, "zscore_interactions_deletion_enhancers_and_suppressors_L.csv"), row.names = FALSE)
      # write.csv(enhancers_and_suppressors_K,
      #   file = file.path(out_dir, "zscore_interaction_deletion_enhancers_and_suppressors_K.csv"), row.names = FALSE)
      
      # message("Writing linear model enhancer/suppressor csv files")
      # lm_interaction_threshold <- 2 # TODO add to study config?
      # enhancers_lm_L <- df_interactions[df_interactions$Z_lm_L >= lm_interaction_threshold, ]
      # suppressors_lm_L <- df_interactions[df_interactions$Z_lm_L <= -lm_interaction_threshold, ]
      # enhancers_lm_K <- df_interactions[df_interactions$Z_lm_K >= lm_interaction_threshold, ]
      # suppressors_lm_K <- df_interactions[df_interactions$Z_lm_K <= -lm_interaction_threshold, ]
      # write.csv(enhancers_lm_L, file = file.path(out_dir, "zscore_interactions_deletion_enhancers_lm_L.csv"), row.names = FALSE)
      # write.csv(suppressors_lm_L, file = file.path(out_dir, "zscore_interactions_deletion_suppressors_lm_L.csv"), row.names = FALSE)
      # write.csv(enhancers_lm_K, file = file.path(out_dir, "zscore_interactions_deletion_enhancers_lm_K.csv"), row.names = FALSE)
      # write.csv(suppressors_lm_K, file = file.path(out_dir, "zscore_interactions_deletion_suppressors_lm_K.csv"), row.names = FALSE)

      # message("Generating rank plots")
      # rank_plot_configs <- generate_rank_plot_configs(
      #   df_interactions,
      #   is_lm = FALSE,
      # )
      # generate_and_save_plots(out_dir, "rank_plots", rank_plot_configs,
      #   page_width = 18, page_height = 12)

      # message("Generating ranked linear model plots")
      # rank_lm_plot_configs <- generate_rank_plot_configs(
      #   df_interactions,
      #   is_lm = TRUE,
      # )
      # generate_and_save_plots(out_dir, "rank_plots_lm", rank_lm_plot_configs,
      #   page_width = 18, page_height = 12)

      # message("Generating overlapped ranked plots")
      # rank_plot_filtered_configs <- generate_rank_plot_configs(
      #   df_interactions,
      #   is_lm = FALSE,
      #   filter_na = TRUE,
      #   overlap_color = TRUE
      # )
      # generate_and_save_plots(out_dir, "rank_plots_na_rm", rank_plot_filtered_configs,
      #   page_width = 18, page_height = 12)

      # message("Generating overlapped ranked linear model plots")
      # rank_plot_lm_filtered_configs <- generate_rank_plot_configs(
      #   df_interactions,
      #   is_lm = TRUE,
      #   filter_na = TRUE,
      #   overlap_color = TRUE
      # )
      # generate_and_save_plots(out_dir, "rank_plots_lm_na_rm", rank_plot_lm_filtered_configs,
      #   page_width = 18, page_height = 12)

      message("Generating correlation curve parameter pair plots")
      correlation_plot_configs <- generate_correlation_plot_configs(
        df_interactions,
        df_reference_interactions
      )
      generate_and_save_plots(out_dir, "correlation_cpps", correlation_plot_configs,
        page_width = 10, page_height = 7)
    })
  })
}
main()