diff --git a/workflow/apps/r/calculate_interaction_zscores5.R b/workflow/apps/r/calculate_interaction_zscores5.R
index 4b2424f2..08f431a4 100644
--- a/workflow/apps/r/calculate_interaction_zscores5.R
+++ b/workflow/apps/r/calculate_interaction_zscores5.R
@@ -306,7 +306,7 @@ calculate_bg_means <- function(df_stats_by_l, df_stats_by_k, df_stats_by_r, df_s
 }
 
 # Process strains (deletion and reference)
-process_strains <- function(df, l_within_2sd_k, strain, output_dir) {
+process_strains <- function(df, l_within_2sd_k, strain) {
   df_strains <- data.frame()  # Initialize an empty dataframe to store results
   
   for (concentration in unique(df$conc_num)) {
@@ -627,7 +627,7 @@ main <- function() {
       stats_by_r_bg <- stats_bg %>% select(starts_with("r_"), "OrfRep", "conc_num", "conc_num_factor")
       stats_by_auc_bg <- stats_bg %>% select(starts_with("AUC_"), "OrfRep", "conc_num", "conc_num_factor")
       write.csv(stats_bg,
-        file = file.path(output_dir, paste0("SummaryStats_BackgroundStrains_", strain, ".csv")),
+        file = file.path(out_dir, paste0("SummaryStats_BackgroundStrains_", strain, ".csv")),
         row.names = FALSE)
       stats_bg_joined <- left_join(df_bg, stats_bg, by = c("conc_num", "conc_num_factor"))
 
@@ -666,9 +666,9 @@ main <- function() {
         mutate(SM = 0)
       
       message("Processing reference strain")
-      reference_strain <- process_strains(df_reference, l_within_2sd_k, strain, out_dir)
+      reference_strain <- process_strains(df_reference, l_within_2sd_k, strain)
       message("Processing deletion strains")
-      deletion_strains <- process_strains(df_deletion, l_within_2sd_k, strain, out_dir)
+      deletion_strains <- process_strains(df_deletion, l_within_2sd_k, strain)
       
       # Deprecated
       # Change OrfRep to include the reference strain, gene, and Num so each RF gets its own score