diff --git a/cami_src/cami.py b/cami_src/cami.py
index 12cf3ef55e38fd9e991f6ff30bf41847a43696fa..9d08515dce066b941b62ae4747f497ae2604971f 100755
--- a/cami_src/cami.py
+++ b/cami_src/cami.py
@@ -14,6 +14,8 @@ import argparse
import webbrowser
import random
import matplotlib.pyplot as plt
+import seaborn as sb
+import pandas as pd
#MC:
from configparser import ConfigParser
@@ -172,9 +174,35 @@ def main(ppi_network, seeds, tools, tool_weights, consensus, evaluate,
# SEED VARIATION
if seed_variation:
+
def predict_and_make_consensus(vis=False):
result_sets = cami.make_predictions()
cami.create_consensus(result_sets)
+ if vis:
+ n_results = len(cami.result_gene_sets)
+ comparison_matrix = pd.DataFrame([[int(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)
+ for algo1 in cami.result_gene_sets:
+ for algo2 in cami.result_gene_sets:
+ comparison_matrix.loc[algo1,algo2] = int(len(cami.result_gene_sets[algo1].intersection(cami.result_gene_sets[algo2])))
+
+ fig2, ax3 = plt.subplots(figsize=(20,20))
+
+ ax3 = sb.heatmap(comparison_matrix, annot=True, fmt='g')
+ ax3.set_title('Intersections of result_gene_sets of all analyzed algorithms.')
+ fig2.savefig(f'{output_dir}/heatmap_{cami.uid}.png', bbox_inches="tight")
+ print(f'saved intersection heatmap of all algorithms under: {output_dir}/heatmap_{cami.uid}.png')
+ plt.close(fig2)
+
+ fig2a, ax3a = plt.subplots(figsize=(20,20))
+ comparison_matrix_normalized = comparison_matrix.apply(lambda row: row/row.max(), axis=1)
+ ax3a = sb.heatmap(comparison_matrix_normalized, annot=True, fmt='.2f')
+ ax3a.set_title('Normalized intersections of result_gene_sets of all analyzed algorithms.')
+ fig2a.savefig(f'{output_dir}/heatmap_{cami.uid}_normalized.png', bbox_inches="tight")
+ print(f'saved intersection heatmap of all algorithms under: {output_dir}/heatmap_{cami.uid}_normalized.png')
+ plt.close(fig2a)
if nvenn and vis:
url = cami.use_nvenn()
cami.download_diagram(url)
@@ -208,6 +236,12 @@ def main(ppi_network, seeds, tools, tool_weights, consensus, evaluate,
result_file_2 = f'{output_dir}/00_seedvariation_results2.tsv'
result_file_3 = f'{output_dir}/00_seedvariation_results3.tsv'
result_file_4 = f'{output_dir}/00_seedvariation_results4.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(result_file_1, 'w') as res_table1:
with open(result_file_2, 'w') as res_table2:
with open(result_file_3, 'w') as res_table3:
@@ -256,7 +290,7 @@ def main(ppi_network, seeds, tools, tool_weights, consensus, evaluate,
for tool in result_dict:
nof_predictions = len(result_dict[tool]) + len(used_seeds)
- redisc_seeds = list(set(result_dict[tool]).intersection(set(rem_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] = redisc_rate #true positive rate: rate verhältnis von gefundenen und allgemein gefundenen
@@ -270,18 +304,34 @@ def main(ppi_network, seeds, tools, tool_weights, consensus, evaluate,
res_table1.write('\t')
for idx,seed in enumerate(redisc_seeds):
if idx == 0:
- res_table1.write(f'{redisc_seeds[0]}')
+ res_table1.write(f'{list(redisc_seeds)[0]}')
else:
res_table1.write(f',{seed}')
print(f'{tool} rediscovered {redisc_seeds} after removing {rem_seeds}.')
res_table2.write(f'{ident}\t{tool}\t{nof_removals},\t{rem_seeds}\t{used_seeds}\t{redisc_prev}\t{redisc_rate}\t{redisc_seeds}\n')
res_table1.write('\n')
variation_results.append((redisc_rate_dict, redisc_seeds_dict, used_seeds, rem_seeds))
-
+ for algo1 in redisc_seeds_dict:
+ for algo2 in redisc_rate_dict:
+ redisc_intersection_matrix.loc[algo1,algo2] += len(redisc_seeds_dict[algo1].intersection(redisc_seeds_dict[algo2]))
print(f'Result tables are saved in the following locations:')
print(f'Rediscovered seeds: {result_file_1}')
print(f'Rediscovery Rates: {result_file_2}')
print(f'Sensitivity: {result_file_3}')
+ fig3, ax6 = plt.subplots(figsize=(20,20))
+ ax6 = sb.heatmap(redisc_intersection_matrix, annot=True, fmt='g')
+ ax6.set_title('Number of times the algorithms rediscovered the same seeds')
+ fig3.savefig(f'{output_dir}/00_seedvariation_heatmap_{identifier}.png', bbox_inches="tight")
+ print(f'saved intersection heatmap of all algorithms under:{output_dir}/heatmap_{identifier}.png')
+ plt.close(fig3)
+ fig3a, ax6a = plt.subplots(figsize=(20,20))
+ redisc_intersection_matrix_normalized = redisc_intersection_matrix.apply(lambda row: row/row.max(), axis=1)
+ ax6a = sb.heatmap(redisc_intersection_matrix_normalized, annot=True, fmt='.2f')
+ ax6a.set_title('Normalized numbers of times the algorithms rediscovered the same seeds')
+ fig3a.savefig(f'{output_dir}/00_seedvariation_heatmap_{identifier}_normalized.png', bbox_inches="tight")
+ print(f'saved intersection heatmap of all algorithms under:{output_dir}/heatmap_normalized_{cami.uid}.png')
+ plt.close(fig3a)
+
# print(variation_results)
rediscovery_rates_results = [results[0] for results in variation_results]
# print(rediscovery_rates_results)
@@ -300,8 +350,8 @@ def main(ppi_network, seeds, tools, tool_weights, consensus, evaluate,
#PLOT
# Create a figure instance
#print(sys.getrecursionlimit())
- fig, (ax1, ax5, ax4) = plt.subplots(3, 1, figsize=(20,20))
- fig.subplots_adjust(left=0.2)
+ 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
@@ -350,9 +400,9 @@ def main(ppi_network, seeds, tools, tool_weights, consensus, evaluate,
ax5.set_ylabel('precision (<rediscovered seeds>/<module size>)', fontsize=14)
ax5.tick_params(axis='x', labelsize=11)
- fig.tight_layout()
- fig.savefig(f'{output_dir}/00_{identifier}_seed_variation_result.png', bbox_inches="tight")
- plt.close(fig)
+ fig1.tight_layout()
+ fig1.savefig(f'{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')
if save_temps: