diff --git a/analyze_run.py b/analyze_run.py
index e15c5d3..c438770 100644
--- a/analyze_run.py
+++ b/analyze_run.py
@@ -181,7 +181,7 @@
 
 # apply the NTC thresholding to the t13_dataframe to produce a new dataframe with the positive/negative denotation
 # and save the file to your working directory
-ntc_PerAssay, ntc_thresholds_output, t13_hit_output = thresholdr.raw_thresholder(unique_crRNA_assays, assigned_norms['signal_norm_raw'], t13_dataframe_copy1, CLI_arg[1])
+ntc_thresholds_output, t13_hit_output = thresholdr.raw_thresholder(unique_crRNA_assays, assigned_norms['signal_norm_raw'], t13_dataframe_copy1, CLI_arg[1])
 
 # make copies of t13_hit_output csv for downstream summaries and quality control checks
 t13_hit_output_copy1 = pd.DataFrame(t13_hit_output).copy() # make a copy of t13_hit_output # used in ndc qual check
diff --git a/qual_checks.py b/qual_checks.py
index e19e296..a3c4ad1 100644
--- a/qual_checks.py
+++ b/qual_checks.py
@@ -124,7 +124,7 @@ def coinf_check(self, t13_hit_binary_output):
        
         # convert coninf_samples_by_assay into a df for easy output
         coinf_df = pd.DataFrame.from_dict(coinf_samples_by_assay, orient='index')
-        coinf_df.drop('Summary')
+        #coinf_df.drop('Summary')
         
         return coinf_df
 
diff --git a/threshold.py b/threshold.py
index beeb2e6..4572dab 100644
--- a/threshold.py
+++ b/threshold.py
@@ -95,6 +95,6 @@ def raw_thresholder(self, unique_crRNA_assays, assigned_only, t13_df, CLI_thresh
         # Create a new row called 'Summary' at the bottom of the hit output sheet
         t13_df.loc['Summary'] = t13_df.apply(lambda col: col.value_counts().get('positive', 0))
  
-        return ntc_PerAssay, raw_thresholds_df, t13_df
+        return raw_thresholds_df, t13_df
 
     
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