diff --git a/cami_src/cami_suite.py b/cami_src/cami_suite.py
index 889a197fd5e2b8de3efd4de1f5aea67c3573ff97..c068d96798ff0d4f80122df05b3a3223d36b852e 100644
--- a/cami_src/cami_suite.py
+++ b/cami_src/cami_suite.py
@@ -9,6 +9,23 @@ from configparser import ConfigParser
import preprocess
from consensus import cami_v1, cami_v2, cami_v3
import matplotlib.pyplot as plt
+import itertools
+
+
+
+def generate_param_combinations(params_dict):
+ params_keys = list(params_dict.keys())
+ params_values = [params_dict[k] for k in params_keys if k != 'function']
+ function_dict = params_dict['function']
+ function_names = list(function_dict.keys())
+ param_combinations = itertools.product(*params_values)
+ result = []
+ for function_name, function in function_dict.items():
+ for combination in param_combinations:
+ param_dict = dict(zip(params_keys[:-1], combination))
+ params_str = '_'.join([f"{k}_{v}".replace(' ', '') for k, v in param_dict.items()])
+ result.append([function_name, params_str, {'params': param_dict, 'function': function}])
+ return result
def initialize_cami(path_to_ppi_file=''):
cami_params = {}
@@ -298,7 +315,7 @@ class cami():
result_sets[tool] = set(result_list)
return result_sets
- def create_consensus(self, result_sets):
+ def create_consensus(self, result_sets, save_output=True):
"""takes a set of active module predictions and creates a consensus
that combines all the results of the different tools.
@@ -321,51 +338,83 @@ class cami():
# remove seeds from result sets
for tool in result_sets:
result_sets[tool] -= set(self.seed_lst)
-
- params = {'hub_pentalty': [0, 0.25, 0.5, 0.75, 1.0], 'damping_factor': [0.1, 0.25, 0.5, 0.75], 'confidence_level': [0.2, 0.35, 0.5, 0.75], 'ranking':["trustrank", "betweenness", "harmonic"], 'function':[cami_v2.run_cami, cami_v3.run_cami]}
-
- camis = {
- 'union': {'function': cami_v1.make_union, 'params': {}},
- 'intersection': {'function': cami_v1.make_intersection, 'params': {}},
- 'first_neighbors': {'function': cami_v1.make_first_neighbor_result_set, 'params': {}},
- 'cami_v1': {'function': cami_v1.run_cami, 'params': {'consens_threshold': consens_threshold}},
- 'cami_v2_param1_tr': {'function': cami_v2.run_cami, 'params': {
- 'hub_penalty': 0.3, 'damping_factor': 0.7, 'confidence_level': 0.5
- }},
- 'cami_v2_param1_b': {'function': cami_v2.run_cami, 'params': {
- 'hub_penalty': 0.3, 'damping_factor': 0.7, 'ranking': 'betweenness', 'confidence_level': 0.5
- }},
- 'cami_v2_param1_hc': {'function': cami_v2.run_cami, 'params': {
- 'hub_penalty': 0.3, 'damping_factor': 0.7, 'ranking': 'harmonic', 'confidence_level': 0.5
- }},
- 'cami_v2_param2_tr': {'function': cami_v2.run_cami, 'params': {
- 'hub_penalty': 0, 'damping_factor': 0.7, 'confidence_level': 0.5
- }},
- 'cami_v2_param2_b': {'function': cami_v2.run_cami, 'params': {
- 'hub_penalty': 0, 'damping_factor': 0.7, 'ranking': 'betweenness', 'confidence_level': 0.5
- }},
- 'cami_v2_param2_hc': {'function': cami_v2.run_cami, 'params': {
- 'hub_penalty': 0, 'damping_factor': 0.7, 'ranking': 'harmonic', 'confidence_level': 0.5
- }},
- 'cami_v3_param1_tr': {'function': cami_v3.run_cami, 'params': {
- 'hub_penalty': 0.3, 'damping_factor': 0.7, 'confidence_level': 0.5
- }},
- 'cami_v3_param1_b': {'function': cami_v3.run_cami, 'params': {
- 'hub_penalty': 0.3, 'damping_factor': 0.7, 'ranking': 'betweenness', 'confidence_level': 0.5
- }},
- 'cami_v3_param1_hc': {'function': cami_v3.run_cami, 'params': {
- 'hub_penalty': 0.3, 'damping_factor': 0.7, 'ranking': 'harmonic', 'confidence_level': 0.5
- }},
- 'cami_v3_param2_tr': {'function': cami_v3.run_cami, 'params': {
- 'hub_penalty': 0, 'damping_factor': 0.7, 'confidence_level': 0.5
- }},
- 'cami_v3_param2_b': {'function': cami_v3.run_cami, 'params': {
- 'hub_penalty': 0, 'damping_factor': 0.7, 'ranking': 'betweenness', 'confidence_level': 0.5
- }},
- 'cami_v3_param2_hc': {'function': cami_v3.run_cami, 'params': {
- 'hub_penalty': 0, 'damping_factor': 0.7, 'ranking': 'harmonic', 'confidence_level': 0.5
- }},
- }
+
+ params_1 = {'consens_threshold': [consens_threshold],
+ 'function': {'cami_v1': cami_v1.run_cami}}
+
+ params_0 = {'function': {'union':cami_v1.make_union,
+ 'intersection':cami_v1.make_intersection,
+ 'first_neighbours': cami_v1.make_first_neighbor_result_set}
+ }
+
+ params_tr = {'hub_penalty': [0, 0.25, 0.5, 0.75, 1.0],
+ 'damping_factor': [0.1, 0.25, 0.5, 0.75],
+ 'confidence_level': [0.2, 0.35, 0.5, 0.75],
+ 'ranking': ['trustrank'],
+ 'function': {'cami_v2': cami_v2.run_cami,
+ 'cami_v3':cami_v3.run_cami}}
+
+ params_b_m = {'hub_penalty': [0, 0.25, 0.5, 0.75, 1.0],
+ 'confidence_level': [0.2, 0.35, 0.5, 0.75],
+ 'ranking': ['betweenness', 'harmonic'],
+ 'function': {'cami_v2': cami_v2.run_cami,
+ 'cami_v3':cami_v3.run_cami}}
+
+ cami_setting_list = generate_param_combinations(params_0)+\
+ generate_param_combinations(params_1)+\
+ generate_param_combinations(params_tr)+\
+ generate_param_combinations(params_b_m)
+
+ camis = {}
+ for setting in cami_setting_list:
+ if setting[1]:
+ func_name = setting[0] + '_' +setting[1]
+ else:
+ func_name = setting[0]
+
+ camis[func_name] = setting[2]
+ # camis = {
+ # 'union': {'function': cami_v1.make_union, 'params': {}},
+ # 'intersection': {'function': cami_v1.make_intersection, 'params': {}},
+ # 'first_neighbors': {'function': cami_v1.make_first_neighbor_result_set, 'params': {}},
+ # 'cami_v1': {'function': cami_v1.run_cami, 'params': {'consens_threshold': consens_threshold}},
+ # 'cami_v2_param1_tr': {'function': cami_v2.run_cami, 'params': {
+ # 'hub_penalty': 0.3, 'damping_factor': 0.7, 'confidence_level': 0.5
+ # }},
+ # 'cami_v2_param1_b': {'function': cami_v2.run_cami, 'params': {
+ # 'hub_penalty': 0.3, 'damping_factor': 0.7, 'ranking': 'betweenness', 'confidence_level': 0.5
+ # }},
+ # 'cami_v2_param1_hc': {'function': cami_v2.run_cami, 'params': {
+ # 'hub_penalty': 0.3, 'damping_factor': 0.7, 'ranking': 'harmonic', 'confidence_level': 0.5
+ # }},
+ # 'cami_v2_param2_tr': {'function': cami_v2.run_cami, 'params': {
+ # 'hub_penalty': 0, 'damping_factor': 0.7, 'confidence_level': 0.5
+ # }},
+ # 'cami_v2_param2_b': {'function': cami_v2.run_cami, 'params': {
+ # 'hub_penalty': 0, 'damping_factor': 0.7, 'ranking': 'betweenness', 'confidence_level': 0.5
+ # }},
+ # 'cami_v2_param2_hc': {'function': cami_v2.run_cami, 'params': {
+ # 'hub_penalty': 0, 'damping_factor': 0.7, 'ranking': 'harmonic', 'confidence_level': 0.5
+ # }},
+ # 'cami_v3_param1_tr': {'function': cami_v3.run_cami, 'params': {
+ # 'hub_penalty': 0.3, 'damping_factor': 0.7, 'confidence_level': 0.5
+ # }},
+ # 'cami_v3_param1_b': {'function': cami_v3.run_cami, 'params': {
+ # 'hub_penalty': 0.3, 'damping_factor': 0.7, 'ranking': 'betweenness', 'confidence_level': 0.5
+ # }},
+ # 'cami_v3_param1_hc': {'function': cami_v3.run_cami, 'params': {
+ # 'hub_penalty': 0.3, 'damping_factor': 0.7, 'ranking': 'harmonic', 'confidence_level': 0.5
+ # }},
+ # 'cami_v3_param2_tr': {'function': cami_v3.run_cami, 'params': {
+ # 'hub_penalty': 0, 'damping_factor': 0.7, 'confidence_level': 0.5
+ # }},
+ # 'cami_v3_param2_b': {'function': cami_v3.run_cami, 'params': {
+ # 'hub_penalty': 0, 'damping_factor': 0.7, 'ranking': 'betweenness', 'confidence_level': 0.5
+ # }},
+ # 'cami_v3_param2_hc': {'function': cami_v3.run_cami, 'params': {
+ # 'hub_penalty': 0, 'damping_factor': 0.7, 'ranking': 'harmonic', 'confidence_level': 0.5
+ # }},
+ # }
# transform all vertex indices to their corresponding gene names in a result set
for tool in result_sets:
@@ -405,8 +454,9 @@ class cami():
sys.setrecursionlimit(recursion_limit)
# save the results in outputfiles
- self.generate_output(cami_method_name, seed_genes, cami_vlist, cami_vertices, putative_vertices, cami_genes,
- gene_name_map, codes2tools, cami_scores)
+ if save_output:
+ self.generate_output(cami_method_name, seed_genes, cami_vlist, cami_vertices, putative_vertices, cami_genes,
+ gene_name_map, codes2tools, cami_scores)
# add seeds to result sets for drugstone and digest
for toolname in self.result_gene_sets:
diff --git a/cami_src/consensus/cami_v2.py b/cami_src/consensus/cami_v2.py
index 72a02f3b99f4ed0cf7706b07ee8f535e8952efea..5857bd330c5d2b3d6b5dc818e485b6b5e25780c8 100644
--- a/cami_src/consensus/cami_v2.py
+++ b/cami_src/consensus/cami_v2.py
@@ -8,7 +8,6 @@ from utils.networks import trustrank, betweenness, must, closeness
# This uses a trustrank algorithm to rank all putative nodes starting from the seeds and only accepts the top 0.X entries
# TODO maybe find a smart way to cutoff automatically?
def run_cami(result_sets, ppi_graph, seed_lst, predicted_by, cami_scores, code2toolname, tool_code, params):
- damping_factor = params['damping_factor']
hub_penalty = params['hub_penalty']
confidence_level = params.get('confidence_level',0.5)
weighted = 'weighted' in params and params['weighted']
@@ -36,6 +35,7 @@ def run_cami(result_sets, ppi_graph, seed_lst, predicted_by, cami_scores, code2t
for v, c in counts.items():
weights.a[int(v)] = c
if ranking_method == 'trustrank':
+ damping_factor = params['damping_factor']
scores = trustrank(subnet, seed_lst, damping_factor, hub_penalty, weights)
elif ranking_method == 'betweenness':
scores = betweenness(subnet, hub_penalty, weights)
diff --git a/cami_src/consensus/cami_v3.py b/cami_src/consensus/cami_v3.py
index aa6522e6bceb58f70bd4d672afaab27cd9fcd6cf..8326847ed2795851339b9c38804ca74ae29c3321 100644
--- a/cami_src/consensus/cami_v3.py
+++ b/cami_src/consensus/cami_v3.py
@@ -7,7 +7,6 @@ import graph_tool as gt
# This uses a trustrank algorithm to rank all putative nodes starting from the seeds and only accepts the top 0.X entries
# TODO maybe find a smart way to cutoff automatically?
def run_cami(result_sets, ppi_graph, seed_lst, predicted_by, cami_scores, code2toolname, tool_code, params):
- damping_factor = params['damping_factor']
hub_penalty = params['hub_penalty']
confidence_level = params.get('confidence_level', 0.5)
weighted = 'weighted' in params and params['weighted']
@@ -39,6 +38,7 @@ def run_cami(result_sets, ppi_graph, seed_lst, predicted_by, cami_scores, code2t
weights.a[int(v)] = c
if ranking_method == 'trustrank':
+ damping_factor = params['damping_factor']
scores = trustrank(subnet, seed_lst, damping_factor, hub_penalty, weights)
elif ranking_method == 'betweenness':
scores = betweenness(subnet, hub_penalty, weights)
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..3a4c4a04065d8c479448f9e9f77d2474aacd3b2b
--- /dev/null
+++ b/cami_src/evaluation_scripts/seed_variation_script.py
@@ -0,0 +1,226 @@
+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
+
+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=False):
+ 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())
+ 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\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])}\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
diff --git a/cami_src/preprocess.py b/cami_src/preprocess.py
index 406bf9d681c20de451e6b81f9ce77fd2c4b30eec..f3e2d58b0cb450493dfcb6b3944e438823b7361d 100644
--- a/cami_src/preprocess.py
+++ b/cami_src/preprocess.py
@@ -144,7 +144,7 @@ def csv2graph(inputfile,
if unseen_vertices == 0:
break
g.vertex_properties["cami_score"] = g.new_vertex_property("float", val=0.0)
- values = (50) * [-1]
+ values = (255) * [-1]
g.vertex_properties["predicted_by"] = g.new_vertex_property("vector<int16_t>", val=values)
return g