Fix y~x predictor column names

qhtcp-workflow/apps/r/calculate_interaction_zscores.R

@@ -217,7 +217,7 @@ calculate_interaction_scores <- function(df, df_bg, type, overlap_threshold = 2)
     group_vars <- c("OrfRep", "Gene", "Drug")
   }
 
-  perform_lm_simple <- function(x, y, max_conc) {
+  perform_lm <- function(x, y, max_conc) {
     if (all(is.na(x)) || all(is.na(y)) || length(x[!is.na(x)]) == 0 || length(y[!is.na(y)]) == 0) {
       return(list(intercept = NA, slope = NA, r_squared = NA, score = NA))
     } else {
@@ -316,10 +316,10 @@ calculate_interaction_scores <- function(df, df_bg, type, overlap_threshold = 2)
     group_by(across(all_of(group_vars))) %>%
     mutate(
       # Apply the simple LM function for each variable
-      lm_L = list(perform_lm_simple(Delta_L, conc_num_factor, max_conc)),
-      lm_K = list(perform_lm_simple(Delta_K, conc_num_factor, max_conc)),
-      lm_r = list(perform_lm_simple(Delta_r, conc_num_factor, max_conc)),
-      lm_AUC = list(perform_lm_simple(Delta_AUC, conc_num_factor, max_conc)),
+      lm_L = list(perform_lm(Delta_L, conc_num_factor, max_conc)),
+      lm_K = list(perform_lm(Delta_K, conc_num_factor, max_conc)),
+      lm_r = list(perform_lm(Delta_r, conc_num_factor, max_conc)),
+      lm_AUC = list(perform_lm(Delta_AUC, conc_num_factor, max_conc)),
 
       # Extract coefficients and statistics for each model
       lm_intercept_L = lm_L[[1]]$intercept,
@@ -480,19 +480,19 @@ calculate_interaction_scores <- function(df, df_bg, type, overlap_threshold = 2)
           Z_lm_L <= -overlap_threshold & Avg_Zscore_L >= overlap_threshold ~ "Deletion Suppressor lm, Deletion Enhancer Avg Zscore",
           TRUE ~ "No Effect"
         ),
-        # Apply the perform_lm_simple function for each variable pair
-        lm_L = list(perform_lm_simple(Z_lm_L, Avg_Zscore_L, max_conc)),
-        lm_K = list(perform_lm_simple(Z_lm_K, Avg_Zscore_K, max_conc)),
-        lm_r = list(perform_lm_simple(Z_lm_r, Avg_Zscore_r, max_conc)),
-        lm_AUC = list(perform_lm_simple(Z_lm_AUC, Avg_Zscore_AUC, max_conc)),
+        # Apply the perform_lm function for each variable pair
+        lm_L = list(perform_lm(Z_lm_L, Avg_Zscore_L, max_conc)),
+        lm_K = list(perform_lm(Z_lm_K, Avg_Zscore_K, max_conc)),
+        lm_r = list(perform_lm(Z_lm_r, Avg_Zscore_r, max_conc)),
+        lm_AUC = list(perform_lm(Z_lm_AUC, Avg_Zscore_AUC, max_conc)),
 
         # Correlation models for various pairs
-        Z_lm_K_L = list(perform_lm_simple(Z_lm_K, Z_lm_L, max_conc)),
-        Z_lm_r_L = list(perform_lm_simple(Z_lm_r, Z_lm_L, max_conc)),
-        Z_lm_R_AUC_L = list(perform_lm_simple(Z_lm_AUC, Z_lm_L, max_conc)),
-        Z_lm_R_r_K = list(perform_lm_simple(Z_lm_r, Z_lm_K, max_conc)),
-        Z_lm_R_AUC_K = list(perform_lm_simple(Z_lm_AUC, Z_lm_K, max_conc)),
-        Z_lm_R_AUC_r = list(perform_lm_simple(Z_lm_AUC, Z_lm_r, max_conc)),
+        Z_lm_K_L = list(perform_lm(Z_lm_K, Z_lm_L, max_conc)),
+        Z_lm_r_L = list(perform_lm(Z_lm_r, Z_lm_L, max_conc)),
+        Z_lm_AUC_L = list(perform_lm(Z_lm_AUC, Z_lm_L, max_conc)),
+        Z_lm_r_K = list(perform_lm(Z_lm_r, Z_lm_K, max_conc)),
+        Z_lm_AUC_K = list(perform_lm(Z_lm_AUC, Z_lm_K, max_conc)),
+        Z_lm_AUC_r = list(perform_lm(Z_lm_AUC, Z_lm_r, max_conc)),
 
         # Extract coefficients and statistics for each model
         lm_rank_intercept_L = lm_L[[1]]$intercept,
@@ -525,29 +525,29 @@ calculate_interaction_scores <- function(df, df_bg, type, overlap_threshold = 2)
         Z_lm_R_squared_r_L = Z_lm_r_L[[1]]$r_squared,
         Z_lm_Score_r_L = Z_lm_r_L[[1]]$score,
 
-        Z_lm_intercept_R_AUC_L = Z_lm_R_AUC_L[[1]]$intercept,
-        Z_lm_slope_R_AUC_L = Z_lm_R_AUC_L[[1]]$slope,
-        Z_lm_R_squared_R_AUC_L = Z_lm_R_AUC_L[[1]]$r_squared,
-        Z_lm_Score_R_AUC_L = Z_lm_R_AUC_L[[1]]$score,
-
-        Z_lm_intercept_R_r_K = Z_lm_R_r_K[[1]]$intercept,
-        Z_lm_slope_R_r_K = Z_lm_R_r_K[[1]]$slope,
-        Z_lm_R_squared_R_r_K = Z_lm_R_r_K[[1]]$r_squared,
-        Z_lm_Score_R_r_K = Z_lm_R_r_K[[1]]$score,
-
-        Z_lm_intercept_R_AUC_K = Z_lm_R_AUC_K[[1]]$intercept,
-        Z_lm_slope_R_AUC_K = Z_lm_R_AUC_K[[1]]$slope,
-        Z_lm_R_squared_R_AUC_K = Z_lm_R_AUC_K[[1]]$r_squared,
-        Z_lm_Score_R_AUC_K = Z_lm_R_AUC_K[[1]]$score,
-
-        Z_lm_intercept_R_AUC_r = Z_lm_R_AUC_r[[1]]$intercept,
-        Z_lm_slope_R_AUC_r = Z_lm_R_AUC_r[[1]]$slope,
-        Z_lm_R_squared_R_AUC_r = Z_lm_R_AUC_r[[1]]$r_squared,
-        Z_lm_Score_R_AUC_r = Z_lm_R_AUC_r[[1]]$score
+        Z_lm_intercept_AUC_L = Z_lm_AUC_L[[1]]$intercept,
+        Z_lm_slope_AUC_L = Z_lm_AUC_L[[1]]$slope,
+        Z_lm_R_squared_AUC_L = Z_lm_AUC_L[[1]]$r_squared,
+        Z_lm_Score_AUC_L = Z_lm_AUC_L[[1]]$score,
+
+        Z_lm_intercept_r_K = Z_lm_r_K[[1]]$intercept,
+        Z_lm_slope_r_K = Z_lm_r_K[[1]]$slope,
+        Z_lm_R_squared_r_K = Z_lm_r_K[[1]]$r_squared,
+        Z_lm_Score_r_K = Z_lm_r_K[[1]]$score,
+
+        Z_lm_intercept_AUC_K = Z_lm_AUC_K[[1]]$intercept,
+        Z_lm_slope_AUC_K = Z_lm_AUC_K[[1]]$slope,
+        Z_lm_R_squared_AUC_K = Z_lm_AUC_K[[1]]$r_squared,
+        Z_lm_Score_AUC_K = Z_lm_AUC_K[[1]]$score,
+
+        Z_lm_intercept_AUC_r = Z_lm_AUC_r[[1]]$intercept,
+        Z_lm_slope_AUC_r = Z_lm_AUC_r[[1]]$slope,
+        Z_lm_R_squared_AUC_r = Z_lm_AUC_r[[1]]$r_squared,
+        Z_lm_Score_AUC_r = Z_lm_AUC_r[[1]]$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)
+        -lm_L, -lm_K, -lm_r, -lm_AUC,
+        -Z_lm_K_L, -Z_lm_r_L, -Z_lm_AUC_L, -Z_lm_r_K, -Z_lm_AUC_K, -Z_lm_AUC_r)
   } # end deletion-specific block
 
   # Create the final calculations and interactions dataframes with only required columns for csv output
@@ -573,15 +573,27 @@ calculate_interaction_scores <- function(df, df_bg, type, overlap_threshold = 2)
 
   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
+      any_of(c(
+        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",
+        "Z_lm_intercept_L", "Z_lm_slope_L", "Z_lm_R_squared_L", "Z_lm_Score_L",
+        "Z_lm_intercept_K", "Z_lm_slope_K", "Z_lm_R_squared_K", "Z_lm_Score_K",
+        "Z_lm_intercept_r", "Z_lm_slope_r", "Z_lm_R_squared_r", "Z_lm_Score_r",
+        "Z_lm_intercept_AUC", "Z_lm_slope_AUC", "Z_lm_R_squared_AUC", "Z_lm_Score_AUC",
+        "Z_lm_intercept_K_L", "Z_lm_slope_K_L", "Z_lm_R_squared_K_L", "Z_lm_Score_K_L",
+        "Z_lm_intercept_r_L", "Z_lm_slope_r_L", "Z_lm_R_squared_r_L", "Z_lm_Score_r_L",
+        "Z_lm_intercept_AUC_L", "Z_lm_slope_AUC_L", "Z_lm_R_squared_AUC_L", "Z_lm_Score_AUC_L",
+        "Z_lm_intercept_r_K", "Z_lm_slope_r_K", "Z_lm_R_squared_r_K", "Z_lm_Score_r_K",
+        "Z_lm_intercept_AUC_K", "Z_lm_slope_AUC_K", "Z_lm_R_squared_AUC_K", "Z_lm_Score_AUC_K",
+        "Z_lm_intercept_AUC_r", "Z_lm_slope_AUC_r", "Z_lm_R_squared_AUC_r", "Z_lm_Score_AUC_r"
+      ))
     ) %>%
       rename(NG = NG_sum_int, DB = DB_sum_int, SM = SM_sum_int)
 
@@ -1296,21 +1308,28 @@ generate_correlation_plot_configs <- function(df, df_reference) {
       x_var <- paste0("Z_lm_", rel$x) # predictor
       y_var <- paste0("Z_lm_", rel$y) # response
 
-      # Skip this plot if there's no valid data
-      # if (all(is.na(df[[x_var]])) || all(is.na(df_reference[[x_var]])) ||
-      #   all(is.na(df[[y_var]])) || all(is.na(df_reference[[y_var]]))) {
-      #   next
-      # }
-
-      # 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)
+      print(paste("rel$x:", rel$x))
+      print(paste("rel$y:", rel$y))
+      print(paste("Generating correlation plot for response(y):", y_var, "and predictor(x):", x_var))
+      print(paste("Relationship suffix:", rel$y, "_", rel$x))
 
       # 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]
 
+      print(paste("intercept:", intercept))
+      print(paste("slope:", slope))
+      print(paste("r_squared:", r_squared))
+
+      r_squared_rounded <- round(r_squared, 4)
+      r_squared_label <- paste("R-squared =", r_squared_rounded)
+      print(paste("r_squared_label:", r_squared_label))
+
+      # Find the max and min of both dataframes for printing linear regression line
+      xmin <- min(c(min(df[[x_var]]), min(df_reference[[x_var]])))
+      xmax <- max(c(max(df[[x_var]]), max(df_reference[[x_var]])))
+
       # Generate the label for the plot
       plot_label <- paste("Interaction", rel$x, "vs.", rel$y)
 
@@ -1326,7 +1345,7 @@ generate_correlation_plot_configs <- function(df, df_reference) {
           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))
+            label = r_squared_label
           )
         ),
         lm_line = list(