def setUp(self):
self.ont = Ontology(hierarchy, mapping)
synapse_semantic = pd.DataFrame(columns=gene_name,
index=gene_name,
data=sim_score)
synapse_semantic.to_csv(path + 'GO_synapse_semantic_similarity_matrix.csv')
# adjacency list
with open(path + 'GO_synapse_semantic_similarity_list.txt', 'w') as f:
for row in range((sim_score.shape[0])):
for column in range((sim_score.shape[1])):
if column > row:
f.write(gene_name[column] + '\t' + gene_name[row] + '\t' +
str(sim_score[row, column]) + '\n')
# In[9]:
# Run CliXO on flattened hierarchy and time
CX_File = 'GO_synapse_semantic_similarity_list.txt'
t0 = timeit.default_timer()
CX.RunCliXO(CX_File, a_=0.01, b_=0.5, M_=0.0001, z_=0.05)
t1 = timeit.default_timer()
print('time: {:.1f}'.format(t1 - t0))
# In[14]:
# create ontology object from CliXO (useless for recreated, but similar code can be used in future for
# creating an ontology object from CliXO output on real data)
CX_ONT = 'RUN_' + CX_File[0:-4] + 'ONT'
hierarchy, mapping = CX.CliXO_Parser(CX_ONT)
ont_CX = Ontology(hierarchy, mapping, ignore_orphan_terms=True)
# In[7]:
pc = pd.DataFrame(columns=['alpha', 'precision', 'recall'])
index = 0
alphas = [0.5,1,0.05,2,1.5,3,0.2]
for alpha in alphas:
try:
# run CliXO with specified alpha
CX.RunCliXO(CX_File, a_ = alpha,b_ = 0.5, M_ = 0.0001,z_ = 0.05)
t1 = timeit.default_timer()
print('time: {:.1f}'.format(t1-t0))
# generate ontology object from generated CliXO ontology
hierarchy,mapping = CX.CliXO_Parser(CX_ONT)
ontCX_synapse = Ontology(hierarchy, mapping, ignore_orphan_terms=True)
# calculate precision recall and
precision, recall = precision_recall(ontCX_synapse, ontGO_synapse)
row = [alpha, precision, recall]
pc.loc[index, :] = row
pc.to_csv(path + 'precision_recall_draft_1.csv', sep = '\t')
index += 1
except:
pass