added functional relevance to seed_var result table

cami_src/cami_suite.py

@@ -239,27 +239,35 @@ class cami():
                                                                          prefix=f'{result_set}_{self.uid}',
                                                                          file_type='png')
                 
-            sub_validation_results = biodigest.single_validation.single_validation(
-                tar=set(self.result_module_sets[result_set]),
-                tar_id='entrez',
-                mode='subnetwork-set',
-                distance='jaccard',
-                network_data={"network_file":ppi_graph_file,
-                              "prop_name":"name",
-                              "id_type":"entrez"},
-                ref=set(seed_gene_lst),
-                ref_id='entrez'
-                )
-            if sub_validation_results['status'] == 'ok':
-                biodigest.single_validation.save_results(sub_validation_results, f'{result_set}_{self.uid}',
-                                                         self.output_dir)
-                biodigest.evaluation.d_utils.plotting_utils.create_plots(results=sub_validation_results,
-                                                                         mode='subnetwork-set',
-                                                                         tar=set(self.result_module_sets[result_set]),
-                                                                         tar_id='entrez',
-                                                                         out_dir=self.output_dir,
-                                                                         prefix=f'{result_set}_{self.uid}',
-                                                                         file_type='png')
+                with open(os.path.join(self.tmp_dir, f'{result_set}_{self.uid}_relevance_scores.tsv'), 'w') as f:
+                    rel_score_name = list(set_validation_results['input_values']['values'].keys())[0]
+                    f.write(f'value\t{rel_score_name}\n')
+                    val_res_dct = set_validation_results['input_values']['values'][rel_score_name]
+                    for val in val_res_dct:
+                        f.write(f'{val}\t{val_res_dct[val]}\n')
+                
+            # sub_validation_results = biodigest.single_validation.single_validation(
+            #     tar=set(self.result_module_sets[result_set]),
+            #     tar_id='entrez',
+            #     mode='subnetwork-set',
+            #     distance='jaccard',
+            #     network_data={"network_file":ppi_graph_file,
+            #                   "prop_name":"name",
+            #                   "id_type":"entrez"},
+            #     ref=set(seed_gene_lst),
+            #     ref_id='entrez'
+            #     )
+            # if sub_validation_results['status'] == 'ok':
+            #     biodigest.single_validation.save_results(sub_validation_results, f'{result_set}_{self.uid}',
+            #                                              self.output_dir)
+            #     biodigest.evaluation.d_utils.plotting_utils.create_plots(results=sub_validation_results,
+            #                                                              mode='subnetwork-set',
+            #                                                              tar=set(self.result_module_sets[result_set]),
+            #                                                              tar_id='entrez',
+            #                                                              out_dir=self.output_dir,
+            #                                                              prefix=f'{result_set}_{self.uid}',
+            #                                                              file_type='png')
+            
 
     def run_threaded_tool(self, tool, pred_sets):
         """run a tool in one thread and save the results into a dictionary pred_sets

cami_src/evaluation_scripts/seed_variation_script.py

@@ -51,6 +51,11 @@ def make_seedvariation(cami, n_iterations, removal_frac=0.2, vis=False, plot=Tru
                 res_table.write(f'\t{tool}_rdr_ks_pvalue')
             for tool in prediction_tools:
                 res_table.write(f'\t{tool}_msr_ks_pvalue')
+            
+            with open(os.path.join(cami.tmp_dir, f'{used_tools[0]}_{cami.uid}_relevance_scores.tsv)'), 'r') as f:
+                for line in f:
+                    val_name = line.split('\t')[0]
+                    redisc_table.write(f'\t{val_name}')
             res_table.write('\n')
             # result dictionaries of the form {tool:list(value for each iteration)}
             
@@ -135,6 +140,12 @@ def make_seedvariation(cami, n_iterations, removal_frac=0.2, vis=False, plot=Tru
                 for pred_tool in prediction_tools:
                     p_val = kolmogorov_smirnoff.calculate_ks_p_value(list(module_size_dict[tool]), 
                                                                      list(module_size_dict[pred_tool]))
+                    res_table.write(f'\t{p_val}')
+                    
+                with open(os.path.join(cami.tmp_dir, f'{tool}_{cami.uid}_relevance_scores.tsv)'), 'r') as f:
+                    for line in f:
+                        rel_score = line.split('\t')[1].strip()
+                        res_table.write(f'\t{rel_score}')
                 res_table.write('\n')
     
     print(f'Result tables are saved in the following locations:')