def test_textfile_read_and_propagate(self):
from nampy.manipulation import manipulation
from nampy.monopartiteanalysis import prince
the_network = create_test_model(test_network_file = test_network_text_filename)
the_node_pairs = [x.get_node_pair() for x in the_network.edges]
the_node_pairs_1 = the_node_pairs[0:16]
the_node_ids_1 = []
for the_node_pair in the_node_pairs_1:
the_node_ids_1 += [the_node_pair[0].id, the_node_pair[1].id]
the_node_ids_1 = list(set(the_node_ids_1))
the_subnetwork_1 = manipulation.make_subnetwork(the_network, the_node_ids_1, 'subnetwork_1')
for the_node in the_subnetwork_1.nodetypes[0].nodes:
the_node.source = 1
the_result, the_permutations = prince.prince(the_subnetwork_1, n_permutations = 10, verbose = False)
self.assertEqual(len(the_permutations[the_node_ids_1[0]]), 10)
# e.g. the database might not requrn the id's we query with
counter = 0
# Just have one nodetype
for the_node in humannet.nodetypes[0].nodes:
if len(the_node.notes["Entrez Gene (GeneID)"]) == 0:
the_node.notes["Entrez Gene (GeneID)"].append(the_node.id)
counter +=1
print(counter)
# APMS host targets from
# Jäger, S., Cimermancic, P., Gulbahce, N., Johnson, J. R., McGovern, K. E., Clarke, S. C.,
# … Krogan, N. J. (2012). Global landscape of HIV-human protein complexes.
# Nature, 481(7381), 365–70. doi:10.1038/nature10719
hiv_apms_file = data_dir + "published_hiv_apms_factors.txt"
apms_source = networkio.create_source_dict_from_textfile(hiv_apms_file)
apms_source_dict = idmapping.get_more_source_dict_ids(apms_source, "UniProtKB ACC/ID", mapping_types = ["Entrez Gene (GeneID)", "UniProtKB ACC", "UniProtKB ID"])
counter = 0
for the_key in apms_source_dict.keys():
if len(apms_source_dict[the_key]["UniProtKB ACC"]) == 0:
apms_source_dict[the_key]["UniProtKB ACC"].append(the_key)
counter +=1
print(counter)
humannet, unmatched_ids_dict = manipulation.add_source(humannet, apms_source_dict, match_key_type = 'Entrez Gene (GeneID)')
# Just 10 permutations done here for demonstration purposes, to establish statistics you will need more
the_result, the_permutations = prince.prince(humannet, n_permutations = 10, verbose = True)
# The propagation scores are stored in the_result
