Merge branch 'master' into adapt_gui

directlfq/configs/intable_config.yaml

@@ -1,31 +1,6 @@
 ---
 #this file determines the parameters used to convert long format tables as e.g. produced by Spectronaut or DIA-NN into a wide table format
 
-alphadia_precursor_protein:
-  format: longtable
-  sample_ID: run
-  quant_ID:
-    precursor: weighted_ms1_intensity
-  protein_cols:
-   - pg_master
-  ion_hierarchy:
-    precursor:
-      order: [SEQ, MOD, CHARGE]
-      mapping:
-        SEQ: 
-         - sequence
-        MOD: 
-         - mods
-        CHARGE: 
-         - charge
-  use_iontree: True
-  ml_level: CHARGE
-  filters:
-    protein_qval:
-      param: pg_qval
-      comparator: "<="
-      value: 0.01
-
 
 alphapept_peptides:
   format: longtable

directlfq/dashboard_parts.py

@@ -337,8 +337,8 @@ def run_pipeline(self, *args):
         min_nonan = self.num_nonan_vals.value
         file_of_proteins_for_normalization = None if self.protein_subset_for_normalization_file.value_input == '' else self.protein_subset_for_normalization_file.value_input
         num_cores = None if self.num_cores_vals.value == -1 else self.num_cores_vals.value
-        yaml_filt_dict_path = None if self.yaml_filt_dict_path.value_input == '' else self.yaml_filt_dict_path.value_input
-        if isinstance(additional_headers, str):  # The user will enter a string with semicolon-separated values
+        yaml_filt_dict_path = None if self.yaml_filt_dict_path.value == '' else self.yaml_filt_dict_path.value
+        if additional_headers is not None: #the user will enter a string with semicolon separated values
             additional_headers = additional_headers.split(';')
 
         lfq_manager.run_lfq(

directlfq/utils.py

@@ -216,7 +216,7 @@ def add_columns_to_lfq_results_table(lfq_results_df, input_file, columns_to_add)
     all_columns = filter_columns_to_existing_columns(all_columns, input_file)
 
     lfq_results_df = lfq_results_df[[x is not None for x in lfq_results_df[config.PROTEIN_ID]]]
-    if len(all_columns) == 1: #if there are no columns to add, return the original dataframe
+    if (len(columns_to_add) == 0) and (len(standard_columns_for_input_type)==0) :
         return lfq_results_df 
     input_df = pd.read_csv(input_file, sep="\t", usecols=all_columns).drop_duplicates(subset=protein_column_input_table)