edited seed_variation

cami_src/evaluation_scripts/seed_variation_script.py

@@ -6,6 +6,7 @@ import random
 from cami_suite import cami
 import utils.comparison_matrix as comparison_matrix
 import numpy as np
+from utils import kolmogorov_smirnoff
 
 def predict_and_make_consensus(cami, vis=False):
     result_sets = cami.make_predictions()
@@ -16,7 +17,7 @@ def predict_and_make_consensus(cami, vis=False):
         if vis:
             cami.use_nvenn(download=True)
             
-def make_seedvariation(cami, n_iterations, removal_frac=0.2, vis=False, plot=False):
+def make_seedvariation(cami, n_iterations, removal_frac=0.2, vis=False, plot=True):
     identifier = cami.uid
     base_seeds = cami.origin_seed_lst
     original_seeds = [cami.ppi_vertex2gene[seed] for seed in base_seeds]
@@ -26,6 +27,7 @@ def make_seedvariation(cami, n_iterations, removal_frac=0.2, vis=False, plot=Fal
     removal_frac = removal_frac
     nof_iterations = int(n_iterations)
     used_tools = list(cami.result_gene_sets.keys())
+    prediction_tools = cami.prediction_tools
     nof_seeds = len(base_seeds)
     nof_removals = max([int(nof_seeds * removal_frac), 1])
     
@@ -44,8 +46,12 @@ def make_seedvariation(cami, n_iterations, removal_frac=0.2, vis=False, plot=Fal
             for tool in used_tools:
                 redisc_table.write(f'\t{tool}')                    
             redisc_table.write('\n')
-            res_table.write('tool\trdr\trdr_std\tsensitivity\tsensitivity_std\tprecision\tprecision_std\n')
-            
+            res_table.write('tool\trdr\trdr_std\tsensitivity\tsensitivity_std\tprecision\tprecision_std')
+            for tool in prediction_tools:
+                res_table.write(f'\t{tool}_rdr_ks_pvalue')
+            for tool in prediction_tools:
+                res_table.write(f'\t{tool}_msr_ks_pvalue')
+            res_table.write('\n')
             # result dictionaries of the form {tool:list(value for each iteration)}
             
             tp_rate_dict = {k:list() for k in used_tools}
@@ -121,7 +127,15 @@ def make_seedvariation(cami, n_iterations, removal_frac=0.2, vis=False, plot=Fal
                 res_table.write(f'{np.mean(tp_rate_dict[tool])}\t')
                 res_table.write(f'{np.std(tp_rate_dict[tool])}\t')
                 res_table.write(f'{np.mean(module_size_dict[tool])}\t')
-                res_table.write(f'{np.std(module_size_dict[tool])}\n')
+                res_table.write(f'{np.std(module_size_dict[tool])}')
+                for pred_tool in prediction_tools:
+                    p_val = kolmogorov_smirnoff.calculate_ks_p_value(list(redisc_rate_dict[tool]), 
+                                                                     list(redisc_rate_dict[pred_tool]))
+                    res_table.write(f'\t{p_val}')
+                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('\n')
     
     print(f'Result tables are saved in the following locations:')
     
@@ -175,7 +189,6 @@ def make_seedvariation(cami, n_iterations, removal_frac=0.2, vis=False, plot=Fal
 
         ax1.set_ylabel('Rediscovery rate (<rediscovered seeds>/<removed seeds>)', wrap=True, fontsize=14)
 
-
         violins2 = ax4.violinplot([tp_rate_dict[tool] for tool in tools], showmeans=True, showextrema=True)
         for violinpart in list(violins2.keys())[2:]:
             violins2[violinpart].set_color('k')

cami_src/seed_variationconf

@@ -12,10 +12,10 @@ visualization_flag = False
 output_name = 'modules.out'
 para = 1
 c = 'false'
-toolweight : 1
+toolweight = 1
 
 [diamond]
-alpha : 1
+alpha = 1
 pred_factor : 3
 max_preds : 200
 p_value_cutoff : 1e-05