Redo interaction score groupings

qhtcp-workflow/apps/r/calculate_interaction_zscores.R

@@ -163,41 +163,26 @@ update_gene_names <- function(df, sgd_gene_list) {
   return(df)
 }
 
-calculate_summary_stats <- function(df, variables, group_vars, no_sd = FALSE) {
+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), 
+        all_of(variables),
         list(
           mean = ~mean(., na.rm = TRUE),
           median = ~median(., na.rm = TRUE),
-          max = ~ ifelse(all(is.na(.)), NA, max(., na.rm = TRUE)),
-          min = ~ ifelse(all(is.na(.)), NA, min(., na.rm = TRUE))
+          max = ~ifelse(all(is.na(.)), NA, max(., na.rm = TRUE)),
+          min = ~ifelse(all(is.na(.)), NA, min(., na.rm = TRUE)),
+          sd = ~sd(., na.rm = TRUE),
+          se = ~sd(., na.rm = TRUE) / sqrt(N - 1) # TODO non-standard SE, needs explanation
         ),
         .names = "{.fn}_{.col}"
       ),
       .groups = "drop"
     )
-  
-  if (!no_sd) {
-    sd_se_stats <- df %>%
-      group_by(across(all_of(group_vars))) %>%
-      summarise(
-        across(
-          all_of(variables),
-          list(
-            sd = ~sd(., na.rm = TRUE),
-            se = ~sd(., na.rm = TRUE) / sqrt(N - 1)
-          ),
-          .names = "{.fn}_{.col}"
-        ),
-        .groups = "drop"
-      )
-    
-    summary_stats <- left_join(summary_stats, sd_se_stats, by = group_vars)
-  }
 
   # Create a cleaned version of df that doesn't overlap with summary_stats
   cleaned_df <- df %>%
@@ -232,21 +217,18 @@ calculate_interaction_scores <- function(df, max_conc, variables = c("L", "K", "
   calculations <- calculate_summary_stats(
     df = df,
     variables = variables,
-    group_vars = c("OrfRep", "Gene", "num", "conc_num", "conc_num_factor"),
-    no_sd = TRUE
+    group_vars = c("OrfRep", "Gene", "num", "conc_num", "conc_num_factor")
     )$df_with_stats
 
   calculations <- calculations %>%
     group_by(across(all_of(group_vars))) %>%
     mutate(
-      sd_L = df$sd_L,
-      sd_K = df$sd_K,
-      sd_r = df$sd_r,
-      sd_AUC = df$sd_AUC,
-      se_L = df$se_L,
-      se_K = df$se_K,
-      se_r = df$se_r,
-      se_AUC = df$se_AUC,
+      NG = sum(NG, na.rm = TRUE),
+      DB = sum(DB, na.rm = TRUE),
+      SM = sum(SM, na.rm = TRUE),
+      num_non_removed_concs = total_conc_num - sum(DB, na.rm = TRUE) - 1,
+
+      # Store the background data
       WT_L = bg_means$L,
       WT_K = bg_means$K,
       WT_r = bg_means$r,
@@ -275,70 +257,84 @@ calculate_interaction_scores <- function(df, max_conc, variables = c("L", "K", "
       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)
-    )
+      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,
+
+      # Fit linear models and store in list-columns
+      gene_lm_L = list(lm(Delta_L ~ conc_num_factor, data = cur_data())),
+      gene_lm_K = list(lm(Delta_K ~ conc_num_factor, data = cur_data())),
+      gene_lm_r = list(lm(Delta_r ~ conc_num_factor, data = cur_data())),
+      gene_lm_AUC = list(lm(Delta_AUC ~ conc_num_factor, data = cur_data())),
+
+      # Extract coefficients using purrr::map_dbl
+      lm_intercept_L = map_dbl(gene_lm_L, ~ coef(.x)[1]),
+      lm_slope_L = map_dbl(gene_lm_L, ~ coef(.x)[2]),
+      lm_intercept_K = map_dbl(gene_lm_K, ~ coef(.x)[1]),
+      lm_slope_K = map_dbl(gene_lm_K, ~ coef(.x)[2]),
+      lm_intercept_r = map_dbl(gene_lm_r, ~ coef(.x)[1]),
+      lm_slope_r = map_dbl(gene_lm_r, ~ coef(.x)[2]),
+      lm_intercept_AUC = map_dbl(gene_lm_AUC, ~ coef(.x)[1]),
+      lm_slope_AUC = map_dbl(gene_lm_AUC, ~ coef(.x)[2]),
+
+      # Calculate lm_Score_* based on coefficients
+      lm_Score_L = max_conc * lm_slope_L + lm_intercept_L,
+      lm_Score_K = max_conc * lm_slope_K + lm_intercept_K,
+      lm_Score_r = max_conc * lm_slope_r + lm_intercept_r,
+      lm_Score_AUC = max_conc * lm_slope_AUC + lm_intercept_AUC,
+
+      # Calculate R-squared values
+      R_Squared_L = map_dbl(gene_lm_L, ~ summary(.x)$r.squared),
+      R_Squared_K = map_dbl(gene_lm_K, ~ summary(.x)$r.squared),
+      R_Squared_r = map_dbl(gene_lm_r, ~ summary(.x)$r.squared),
+      R_Squared_AUC = map_dbl(gene_lm_AUC, ~ summary(.x)$r.squared),
+
+      # Calculate Z_lm_* Scores
+      Z_lm_L = (lm_Score_L - mean(lm_Score_L, na.rm = TRUE)) / sd(lm_Score_L, na.rm = TRUE),
+      Z_lm_K = (lm_Score_K - mean(lm_Score_K, na.rm = TRUE)) / sd(lm_Score_K, na.rm = TRUE),
+      Z_lm_r = (lm_Score_r - mean(lm_Score_r, na.rm = TRUE)) / sd(lm_Score_r, na.rm = TRUE),
+      Z_lm_AUC = (lm_Score_AUC - mean(lm_Score_AUC, na.rm = TRUE)) / sd(lm_Score_AUC, na.rm = TRUE)
+    ) %>%
+    ungroup()
+
+  # Summarize some of the stats
   interactions <- calculations %>%
     group_by(across(all_of(group_vars))) %>%
     mutate(
       OrfRep = first(OrfRep),
       Gene = first(Gene),
       num = first(num),
-      conc_num = first(conc_num),
-      conc_num_factor = first(conc_num_factor),
-      N = n(),
+
+      # Calculate 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),
+
+      # Calculate 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),
-      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,
+
+      # Sum Z-scores
       Sum_Zscore_L = sum(Zscore_L, na.rm = TRUE),
       Sum_Zscore_K = sum(Zscore_K, na.rm = TRUE),
       Sum_Zscore_r = sum(Zscore_r, na.rm = TRUE),
       Sum_Zscore_AUC = sum(Zscore_AUC, na.rm = TRUE),
+      
+      # Calculate Average Z-scores
       Avg_Zscore_L = Sum_Zscore_L / num_non_removed_concs,
       Avg_Zscore_K = Sum_Zscore_K / num_non_removed_concs,
       Avg_Zscore_r = Sum_Zscore_r / (total_conc_num - 1),
-      Avg_Zscore_AUC = Sum_Zscore_AUC / (total_conc_num - 1),
-      gene_lm_L = lm(formula = Delta_L ~ conc_num_factor, data = df),
-      gene_lm_K = lm(formula = Delta_K ~ conc_num_factor, data = df),
-      gene_lm_r = lm(formula = Delta_r ~ conc_num_factor, data = df),
-      gene_lm_AUC = lm(formula = Delta_AUC ~ conc_num_factor, data = df),
-      lm_Score_L = max_conc * (gene_lm_L$coefficients[2]) + gene_lm_L$coefficients[1],
-      lm_Score_K = max_conc * (gene_lm_K$coefficients[2]) + gene_lm_K$coefficients[1],
-      lm_Score_r = max_conc * (gene_lm_r$coefficients[2]) + gene_lm_r$coefficients[1],
-      lm_Score_AUC = max_conc * (gene_lm_AUC$coefficients[2]) + gene_lm_AUC$coefficients[1],
-      R_Squared_L = summary(gene_lm_L)$r.squared,
-      R_Squared_K = summary(gene_lm_K)$r.squared,
-      R_Squared_r = summary(gene_lm_r)$r.squared,
-      R_Squared_AUC = summary(gene_lm_AUC)$r.squared,
-
-      lm_intercept_L = coef(lm(Zscore_L ~ conc_num_factor))[1],
-      lm_slope_L = coef(lm(Zscore_L ~ conc_num))[2],
-      lm_intercept_K = coef(lm(Zscore_K ~ conc_num))[1],
-      lm_slope_K = coef(lm(Zscore_K ~ conc_num))[2],
-      lm_intercept_r = coef(lm(Zscore_r ~ conc_num))[1],
-      lm_slope_r = coef(lm(Zscore_r ~ conc_num))[2],
-      lm_intercept_AUC = coef(lm(Zscore_AUC ~ conc_num))[1],
-      lm_slope_AUC = coef(lm(Zscore_AUC ~ conc_num))[2],
-      
-      Z_lm_L = (lm_Score_L - mean_L) / sd(lm_Score_L, na.rm = TRUE),
-      Z_lm_K = (lm_Score_K - mean_K) / sd(lm_Score_K, na.rm = TRUE),
-      Z_lm_r = (lm_Score_r - mean_r) / sd(lm_Score_r, na.rm = TRUE),
-      Z_lm_AUC = (lm_Score_AUC - mean_AUC) / sd(lm_Score_AUC, na.rm = TRUE),
-      NG = sum(NG, na.rm = TRUE),
-      DB = sum(DB, na.rm = TRUE),
-      SM = sum(SM, na.rm = TRUE),
-      num_non_removed_concs = total_conc_num - sum(DB, na.rm = TRUE) - 1
+      Avg_Zscore_AUC = Sum_Zscore_AUC / (total_conc_num - 1)
     ) %>%
-    arrange(desc(Z_lm_L), desc(NG))
+    arrange(desc(Z_lm_L), desc(NG)) %>%
+    ungroup()
 
   # Declare column order for output
   calculations <- calculations %>%
@@ -356,17 +352,33 @@ calculate_interaction_scores <- function(df, max_conc, variables = c("L", "K", "
       "Delta_L", "Delta_K", "Delta_r", "Delta_AUC",
       "Zscore_L", "Zscore_K", "Zscore_r", "Zscore_AUC",
       "NG", "SM", "DB")
+
+  interactions <- interactions %>%
+    select(
+      "OrfRep", "Gene", "num", "NG", "DB", "SM",
+      "Raw_Shift_L", "Raw_Shift_K", "Raw_Shift_r", "Raw_Shift_AUC",
+      "Z_Shift_L", "Z_Shift_K", "Z_Shift_r", "Z_Shift_AUC",
+      "Sum_Z_Score_L", "Sum_Z_Score_K", "Sum_Z_Score_r", "Sum_Z_Score_AUC",
+      "Avg_Zscore_L", "Avg_Zscore_K", "Avg_Zscore_r", "Avg_Zscore_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",
+      "Z_lm_L", "Z_lm_K", "Z_lm_r", "Z_lm_AUC")
     
-  cleaned_df <- df %>%
-    select(-any_of(setdiff(intersect(names(df), names(calculations)), group_vars)))
-  calculations_joined <- left_join(cleaned_df, calculations, by = c("OrfRep", "Gene", "num", "conc_num", "conc_num_factor"))
+  # cleaned_df <- df %>%
+  #   select(-any_of(setdiff(intersect(names(df), names(calculations)), group_vars)))
+
+  # calculations_joined <- left_join(cleaned_df, calculations, by = c("OrfRep", "Gene", "num", "conc_num", "conc_num_factor"))
 
   cleaned_df <- df %>%
     select(-any_of(setdiff(intersect(names(df), names(interactions)), group_vars)))
+
   interactions_joined <- left_join(cleaned_df, interactions, by = c("OrfRep", "Gene", "num", "conc_num", "conc_num_factor"))
 
-  return(list(calculations = calculations, interactions = interactions, interactions_joined = interactions_joined,
-    calculations_joined = calculations_joined))
+  return(list(
+    calculations = calculations,
+    interactions = interactions,
+    interactions_joined = interactions_joined
+  ))
 }
 
 generate_and_save_plots <- function(out_dir, filename, plot_configs, grid_layout = NULL) {
@@ -380,18 +392,16 @@ generate_and_save_plots <- function(out_dir, filename, plot_configs, grid_layout
     config <- plot_configs[[i]]
     df <- config$df
 
-    aes_mapping <- if (is.null(config$color_var)) {
-      if (is.null(config$y_var)) {
+    aes_mapping <- if (config$plot_type == "bar" || config$plot_type == "density") {
+      if (is.null(config$color_var)) {
         aes(x = .data[[config$x_var]])
       } else {
-        aes(x = .data[[config$x_var]], y = .data[[config$y_var]])
-      }
-    } else {
-      if (is.null(config$y_var)) {
         aes(x = .data[[config$x_var]], color = as.factor(.data[[config$color_var]]))
-      } else {
-        aes(x = .data[[config$x_var]], y = .data[[config$y_var]], color = as.factor(.data[[config$color_var]]))
       }
+    } else if (is.null(config$color_var)) {
+      aes(x = .data[[config$x_var]], y = .data[[config$y_var]])
+    } else {
+      aes(x = .data[[config$x_var]], y = .data[[config$y_var]], color = as.factor(.data[[config$color_var]]))
     }
 
     # Start building the plot with aes_mapping
@@ -1331,14 +1341,14 @@ main <- function() {
       reference_results <- calculate_interaction_scores(reference_strain, max_conc)
       zscores_calculations_reference <- reference_results$calculations
       zscores_interactions_reference <- reference_results$interactions
-      zscores_calculations_reference_joined <- reference_results$calculations_joined
+      # zscores_calculations_reference_joined <- reference_results$calculations_joined
       zscores_interactions_reference_joined <- reference_results$interactions_joined
 
       message("Calculating deletion strain(s) interactions scores")
       deletion_results <- calculate_interaction_scores(deletion_strains, max_conc)
       zscores_calculations <- deletion_results$calculations
       zscores_interactions <- deletion_results$interactions
-      zscores_calculations_joined <- deletion_results$calculations_joined
+      # zscores_calculations_joined <- deletion_results$calculations_joined
       zscores_interactions_joined <- deletion_results$interactions_joined
 
       # Writing Z-Scores to file