diff --git a/qhtcp-workflow/apps/r/calculate_interaction_zscores.R b/qhtcp-workflow/apps/r/calculate_interaction_zscores.R
index 93e25e03..3c189244 100644
--- a/qhtcp-workflow/apps/r/calculate_interaction_zscores.R
+++ b/qhtcp-workflow/apps/r/calculate_interaction_zscores.R
@@ -229,11 +229,9 @@ calculate_interaction_scores <- function(df, max_conc, variables, group_vars = c
         sd = ~sd(., na.rm = TRUE),
         se = ~ifelse(sum(!is.na(.)) > 1, sd(., na.rm = TRUE) / sqrt(sum(!is.na(.)) - 1), NA)
       ), .names = "{.fn}_{.col}")
-    ) %>%
-    ungroup()
+    )
 
   stats <- stats %>%
-    group_by(across(all_of(group_vars))) %>%
     mutate(
       Raw_Shift_L = mean_L[[1]] - bg_means$L,
       Raw_Shift_K = mean_K[[1]] - bg_means$K,
@@ -272,7 +270,8 @@ calculate_interaction_scores <- function(df, max_conc, variables, group_vars = c
       Zscore_K = Delta_K / WT_sd_K,
       Zscore_r = Delta_r / WT_sd_r,
       Zscore_AUC = Delta_AUC / WT_sd_AUC
-    )
+    ) %>%
+    ungroup()
 
   # Create linear models with error handling for missing/insufficient data
   # This part is a PITA so best to contain it in its own function
@@ -345,7 +344,8 @@ calculate_interaction_scores <- function(df, max_conc, variables, group_vars = c
       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()
 
   # Declare column order for output
   calculations <- stats %>%
@@ -361,9 +361,8 @@ calculate_interaction_scores <- function(df, max_conc, variables, group_vars = c
            "Exp_L", "Exp_K", "Exp_r", "Exp_AUC",
            "Delta_L", "Delta_K", "Delta_r", "Delta_AUC",
            "Zscore_L", "Zscore_K", "Zscore_r", "Zscore_AUC",
-           "NG", "SM", "DB") %>%
-    ungroup()
-
+           "NG", "SM", "DB")
+    
   interactions <- stats %>%
     select("OrfRep", "Gene", "num", "Raw_Shift_L", "Raw_Shift_K", "Raw_Shift_AUC", "Raw_Shift_r",
            "Z_Shift_L", "Z_Shift_K", "Z_Shift_r", "Z_Shift_AUC",
@@ -374,13 +373,12 @@ calculate_interaction_scores <- function(df, max_conc, variables, group_vars = c
            "Z_lm_L", "Z_lm_K", "Z_lm_r", "Z_lm_AUC",
            "NG", "SM", "DB") %>%
     arrange(desc(lm_Score_L)) %>%
-    arrange(desc(NG)) %>%
-    ungroup()
+    arrange(desc(NG))
 
   df <- df %>% select(-any_of(setdiff(names(calculations), group_vars)))
   df <- left_join(df, calculations, by = group_vars)
-  df <- df %>% select(-any_of(setdiff(names(interactions), group_vars)))
-  df <- left_join(df, interactions, by = group_vars)
+  # df <- df %>% select(-any_of(setdiff(names(interactions), group_vars)))
+  # df <- left_join(df, interactions, by = group_vars)
 
   return(list(calculations = calculations, interactions = interactions, joined = df))
 }