def test_read_pickled_and_run_reporter(self):
from nampy.multipartiteanalysis import reporterfeatures
from nampy.networkio import networkio
the_network = networkio.load_pickled_network(test_reporter_input_network_filename)
the_network.convert_to_multipartite()
p_values_dict = {}
for the_reaction in the_network.nodetypes.get_by_id('reaction').nodes:
p_values_dict[the_reaction.id] = 1E-3
the_reporter_dict = reporterfeatures.calculate_reporter_scores(the_network, p_values_dict, 'metabolite', 'reaction', number_of_randomizations = 100, verbose = False)
self.assertEqual(len(the_reporter_dict['p_values']), len(the_network.nodetypes.get_by_id('metabolite').nodes))
the_model_gene_pval_dict[the_gene_id] = aggregated_pvalue_dict['mapped'][the_gene_id]
# Check how many mapped
len(the_model_gene_pval_dict.keys())
len(the_entrez_ids)
# Now we need to map back to the model transcripts
the_transcript_pval_dict = {}
for the_transcript in the_network.nodetypes.get_by_id("gene").nodes:
the_gene_id = the_transcript.id.split(".")[0]
if the_gene_id in the_model_gene_pval_dict.keys():
the_transcript_pval_dict[the_transcript.id] = the_model_gene_pval_dict[the_gene_id]
# Now we map to model reactions and then run reporter metabolites
hyperedge_score_dict = reporterfeatures.evaluate_reaction_pvalues(the_network, the_transcript_pval_dict)
the_reporter_dict = reporterfeatures.calculate_reporter_scores(the_network, hyperedge_score_dict['p'], 'metabolite', 'reaction', number_of_randomizations = 10000)
# We can prepare summar calculations to
# help with network visualization.
# These files can be imported to Cytoscape
# to visualize the results.
from math import log10
node_property_dict = {}
# Uncorrected p's for the nodes
from copy import deepcopy
node_property_dict['uncorrected_p'] = deepcopy(hyperedge_score_dict['p'])
node_property_dict['dir'] = deepcopy(hyperedge_score_dict['dir'])
transcript_uncorrected_p = {}
for the_transcript_id in the_transcript_pval_dict.keys():
