diff --git a/cami_src/evaluation_scripts/seed_variation_script.py b/cami_src/evaluation_scripts/seed_variation_script.py
new file mode 100644
index 0000000000000000000000000000000000000000..aecc9cb9e2c92ae522c170ea29b1909d09931528
--- /dev/null
+++ b/cami_src/evaluation_scripts/seed_variation_script.py
@@ -0,0 +1,250 @@
+import matplotlib.pyplot as plt
+import seaborn as sb
+import pandas as pd
+import os
+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()
+ cami.create_consensus(result_sets, save_output=False)
+ if vis:
+ n_results = len(cami.result_gene_sets)
+ cami.visualize_and_save_comparison_matrix()
+ if vis:
+ cami.use_nvenn(download=True)
+
+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]
+ print(f'All given seeds:{original_seeds}')
+
+ random.seed(50)
+ 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])
+
+ redisc_seeds_file = f'{cami.output_dir}/00_seedvariation_rediscovered_seeds.tsv'
+ result_table_file = f'{cami.output_dir}/00_seedvariation_result_table.tsv'
+ n_results = len(cami.result_gene_sets)
+
+ redisc_intersection_matrix = pd.DataFrame([[0 for _ in range(n_results)] for __ in range(n_results)],
+ columns = list(cami.result_gene_sets.keys()),
+ index = list(cami.result_gene_sets.keys()),
+ dtype=int)
+
+ with open(redisc_seeds_file, 'w') as redisc_table:
+ with open(result_table_file, 'w') as res_table:
+ redisc_table.write('id')
+ 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')
+ 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')
+
+ 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)}
+
+ tp_rate_dict = {k:list() for k in used_tools}
+ redisc_rate_dict = {k:list() for k in used_tools}
+ module_size_dict = {k:list() for k in used_tools}
+
+ # removed and used seeds per iteration
+ all_removed_seeds = list()
+ all_used_seeds = list()
+
+ all_redisc_seeds = []
+
+ for ident in range(nof_iterations):
+ redisc_table.write(f'{ident}')
+ # update uid
+ new_identifier = identifier + f'_{ident}'
+ # reset cami
+ cami.reset_cami(new_uid=new_identifier)
+# cami.ppi_graph = original_ppi
+
+ #remove seeds (again)
+ print(f'Removing {nof_removals} seeds from the original seed list...')
+ removed_seeds_idx = random.sample(list(range(nof_seeds)), nof_removals)
+ removed_seeds = cami.remove_seeds(removed_seeds_idx)
+ rem_seeds = [cami.ppi_vertex2gene[seed] for seed in removed_seeds]
+ print(f'Removed: {rem_seeds} from the seed list')
+ print('Updating tools and repeat CAMI')
+ # reinitialize tools
+ cami.initialize_all_tools()
+
+ # repeat consensus
+ if ident%20==0:
+ predict_and_make_consensus(cami)
+ else:
+ predict_and_make_consensus(cami)
+
+ used_seeds = [cami.ppi_vertex2gene[seed] for seed in cami.seed_lst]
+
+ redisc_seeds_dict = {}
+ result_dict = cami.result_gene_sets
+
+ for tool in result_dict:
+ nof_predictions = len(result_dict[tool]) + len(used_seeds)
+ redisc_seeds = set(result_dict[tool]).intersection(set(rem_seeds))
+ redisc_prev = len(redisc_seeds)
+ redisc_rate = redisc_prev / nof_removals
+ redisc_rate_dict[tool].append(redisc_rate)
+ redisc_seeds_dict[tool] = redisc_seeds
+ tp_rate = redisc_prev / len(removed_seeds)
+ tp_rate_dict[tool].append(tp_rate)
+ module_size_frac = redisc_prev / nof_predictions
+ assert module_size_frac <= 1
+ module_size_dict[tool].append(module_size_frac)
+ redisc_table.write('\t')
+ for idx,seed in enumerate(redisc_seeds):
+ if idx == 0:
+ redisc_table.write(f'{list(redisc_seeds)[0]}')
+ else:
+ redisc_table.write(f',{seed}')
+ print(f'{tool} rediscovered {redisc_seeds} after removing {rem_seeds}.')
+ all_redisc_seeds.append(redisc_seeds_dict)
+ redisc_table.write('\n')
+ all_used_seeds.append(used_seeds)
+ all_removed_seeds.append(rem_seeds)
+ for algo1 in redisc_seeds_dict:
+ for algo2 in redisc_seeds_dict:
+ redisc_intersection_matrix.loc[algo1,algo2] += len(redisc_seeds_dict[algo1].intersection(redisc_seeds_dict[algo2]))
+
+ for tool in redisc_rate_dict:
+ res_table.write(f'{tool}\t')
+ res_table.write(f'{np.mean(redisc_rate_dict[tool])}\t')
+ res_table.write(f'{np.std(redisc_rate_dict[tool])}\t')
+ 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])}')
+ 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(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:')
+
+ fig1,ax1, fig2,ax2 = comparison_matrix.plot_comparison_matrix(redisc_intersection_matrix, n_rows=cami.nof_tools,
+ title=f'number of times algorithms rediscovered the same seeds after removing {nof_removals} seeds')
+ fig1.savefig(f'{cami.output_dir}/same_rediscs_{identifier}_comparison_matrix.png')
+ fig2.savefig(f'{cami.output_dir}/same_rediscs_{identifier}_comparison_matrix_normalized.png')
+ # print(variation_results)
+ # print(rediscovery_rates_results)
+ tools = [tool for tool in redisc_rate_dict.keys()]
+ tool_labels = tools.copy()
+
+ for idx,tool in enumerate(tools):
+ if '_' in tool:
+ # find the index of the second occurrence of the character
+ second_occurrence_index = tool.find('_', tool.find('_') + 1)
+ if second_occurrence_index > -1:
+ # replace the character at that index with the replacement character
+ tool_name = tool[:second_occurrence_index] + '\n' + tool[second_occurrence_index + 1:]
+ tool_labels[idx] = tool_name
+ if plot:
+ #PLOT
+ # Create a figure instance
+ #print(sys.getrecursionlimit())
+ fig1, (ax1, ax5, ax4) = plt.subplots(3, 1, figsize=(20,20))
+ fig1.subplots_adjust(left=0.2)
+ # Extract Figure and Axes instance
+
+ # Create a plot
+ violins1 = ax1.violinplot([redisc_rate_dict[tool] for tool in tools], showmeans=True, showextrema=True)
+
+ for violinpart in list(violins1.keys())[2:]:
+ violins1[violinpart].set_color('k')
+
+ for violin, tool in zip(violins1['bodies'], tools):
+ if tool in [tw.name for tw in cami.tool_wrappers]:
+ violin.set_facecolor('saddlebrown')
+ elif tool == 'first_neighbors':
+ violin.set_facecolor('orange')
+ elif tool in ['union', 'intersection']:
+ violin.set_facecolor('peachpuff')
+ else:
+ violin.set_facecolor('red')
+
+ # Add title
+ ax1.set_title(f'Rediscovery rate after randomly removing {nof_removals} seeds {nof_iterations} times from {identifier} seeds.', wrap=True, fontsize=14)
+
+ ax1.set_xticks(list(range(1,len(tools)+1)))
+ ax1.set_xticklabels(tool_labels)
+ ax1.tick_params(axis='x', labelsize=11)
+
+ 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')
+ for violin, tool in zip(violins2['bodies'], tools):
+ if tool in [tw.name for tw in cami.tool_wrappers]:
+ violin.set_facecolor('tan')
+ elif tool == 'first_neighbors':
+ violin.set_facecolor('peachpuff')
+ elif tool in ['union', 'intersection']:
+ violin.set_facecolor('orange')
+ else:
+ violin.set_facecolor('darkorange')
+ # Add title
+ ax4.set_title(f'True positive rates after randomly removing {nof_removals} seeds {nof_iterations} times from {identifier} seeds.', wrap=True, fontsize=14)
+
+ ax4.set_xticks(list(range(1,len(tools)+1)))
+ ax4.set_xticklabels(tool_labels)
+ ax4.tick_params(axis='x', labelsize=11)
+
+ ax4.set_ylabel('Sensitivity (TP/TP + FN)', wrap=True, fontsize=14)
+
+ violins3 = ax5.violinplot([module_size_dict[tool] for tool in tools], showmeans=True, showextrema=True)
+ # Add title
+ for violinpart in list(violins3.keys())[2:]:
+ violins3[violinpart].set_color('k')
+
+ for violin, tool in zip(violins3['bodies'], tools):
+ if tool in [tw.name for tw in cami.tool_wrappers]:
+ violin.set_facecolor('midnightblue')
+ elif tool == 'first_neighbors':
+ violin.set_facecolor('mediumblue')
+ elif tool in ['union', 'intersection']:
+ violin.set_facecolor('lightsteelblue')
+ else:
+ violin.set_facecolor('royalblue')
+
+ ax5.set_title(f'Ratio of number of rediscovered seeds and predicted module size after removing {nof_removals} seeds {nof_iterations} times from {identifier} seeds.', wrap=True, fontsize=14)
+
+ ax5.set_xticks(list(range(1,len(tools)+1)))
+ ax5.set_xticklabels(tool_labels)
+
+ ax5.set_ylabel('precision (<rediscovered seeds>/<module size>)', fontsize=14)
+ ax5.tick_params(axis='x', labelsize=11)
+ fig1.tight_layout()
+ fig1.savefig(f'{cami.output_dir}/00_{identifier}_seed_variation_result.png', bbox_inches="tight")
+ plt.close(fig1)
+ print(f'Violin plot saved under: 00_{identifier}_seed_variation_result.png')
+ return cami
\ No newline at end of file