def preprocess(inf,species,is_mmod):
src_file = r'%s' % (my_constants.species_sbml[species])
reader = libsbml.SBMLReader()
doc = reader.readSBML(src_file)
go_loaded_model = eval_util.GoLoadedModel(doc.getModel(), species)
moredata_loaded_model = eval_util.MoreDataLoadedModel(species)
sbml_file = r'%s/dataset/networks/%s' % (my_constants.basePath, my_constants.species_sbml[species])
S, mets, rxns, revs, met_names, rxn_names, biomass, met_comparts \
= importer.sbmlStoichiometricMatrix(sbml_file, True, read_species_compart=True)# remove_biomass=method_conf['remove_biomass'], normalize_stoich=method_conf['normalize_stoich'])
go1=compute_coexpression_of_enzymes_aggregate(inf,species,is_mmod,True,S,mets,rxns,revs,go_loaded_model, moredata_loaded_model)
return go1
def preprocess(inf, species, is_mmod):
src_file = r'%s' % (my_constants.species_sbml[species])
reader = libsbml.SBMLReader()
doc = reader.readSBML(src_file)
src_file_chebi = r'forchebi/%s' % (my_constants.species_sbml[species])
reader_chebi = libsbml.SBMLReader()
doc_chebi = reader_chebi.readSBML(src_file_chebi)
if True:
chebi_loaded_model = ChebiLoadedModel(
[doc_chebi.getModel(), doc.getModel()])
sbml_file = r'%s' % (my_constants.species_sbml[species])
S, mets, rxns, revs, met_names, rxn_names, biomass, met_comparts \
= importer.sbmlStoichiometricMatrix(sbml_file, True, read_species_compart=True)# remove_biomass=method_conf['remove_biomass'], normalize_stoich=method_conf['normalize_stoich'])
go1 = compute_chebi_distance(inf, species, 'mf', not is_mmod, True, S,
mets, rxns, revs, chebi_loaded_model)
return go1
def preprocess(inf,species,criteria,is_mmod):
src_file = r'%s' % (my_constants.species_sbml[species])
reader = libsbml.SBMLReader()
doc = reader.readSBML(src_file)
go_loaded_model = eval_util.GoLoadedModel(doc.getModel(), species)
moredata_loaded_model = eval_util.MoreDataLoadedModel(species)
if criteria=='go_distance_cc_G' or criteria=='go_distance_cc_F':
type='cc'
elif criteria=='go_distance_bp_G' or criteria=='go_distance_bp_F':
type='bp'
else:
type='mf'
if criteria=='go_distance_cc_G' or criteria=='go_distance_bp_G' or criteria=='go_distance_mf_G':
restric_to_reliable_genes=True
print '*************************'
if criteria=='go_distance_cc_F' or criteria=='go_distance_bp_F' or criteria=='go_distance_mf_F':
restric_to_reliable_genes=False
print '&&&&&&&&&&&&&&&&&&&&&&&&###############'
sbml_file = r'%s' % (my_constants.species_sbml[species])
S, mets, rxns, revs, met_names, rxn_names, biomass, met_comparts \
= importer.sbmlStoichiometricMatrix(sbml_file, True, read_species_compart=True)# remove_biomass=method_conf['remove_biomass'], normalize_stoich=method_conf['normalize_stoich'])
go1=compute_go_distance(inf,species,type,restric_to_reliable_genes,is_mmod,True,S,mets,rxns,revs,go_loaded_model, moredata_loaded_model)
return go1
def preprocess(inf, species):
sbml_file = r'%s' % (my_constants.species_sbml[species])
S, mets, rxns, revs, met_names, rxn_names, biomass, met_comparts \
= importer.sbmlStoichiometricMatrix(sbml_file, True, read_species_compart=True)# remove_biomass=method_conf['remove_biomass'], normalize_stoich=method_conf['normalize_stoich'])
mo = compute_modularity(inf, S, mets, rxns, True)
return mo
species, type)
return do_compute_distance(cached_metabolite_similarities,
similarity_table, table_compound_order, inf,
species, type, is_rmod, is_partial_module, S,
mets, reacts, revs, chebi_loaded_model)
if __name__ == '__main__':
# compute_stats_for_all_species('%s/evaluation/gossto/stats_final.txt' % my_constants.basePath, '%s/evaluation/gossto/stats_reaction_types.txt' % my_constants.basePath)
#
# if True:
# exit()
species = 'ecoli_core'
out_dir = r'%s/%s/newman' % (my_constants.resultPath, species)
src_file = r'%s/dataset/networks/%s' % (my_constants.basePath,
my_constants.species_sbml[species])
S, mets, rxns, revs, met_names, rxn_names, biomass, met_comparts = importer.sbmlStoichiometricMatrix(
src_file, True, read_species_compart=True)
reader = libsbml.SBMLReader()
doc = reader.readSBML(src_file)
chebi_loaded_model = ChebiLoadedModel([doc.getModel()])
#print compute_chebi_distance('%s/final_modules.txt' % out_dir, species, 'mf', False, False, S, mets, rxns, revs, chebi_loaded_model)
preprocess(
'/Users/fatemeh/Documents/comparison_1/RESULTS/ecoli_core/Ding/final_modules.txt',
'ecoli_core', True)
couplings = copy.deepcopy(couplings)
for r in not_done_reacts:
r_idx = reacts.index(r)
for i in range(len(reacts)):
couplings[i][r_idx] = -1
couplings[r_idx][i] = -1
module_coupling_table, module_order_in_header = compute_module_coupling_table(
couplings, blocks, mods, reacts)
return compute_coupling_uncoupling_score(module_coupling_table,
module_order_in_header)
if __name__ == '__main__':
# species = 'toy_model'
species = 'ecoli_core'
out_dir = r'%s/%s/newman' % (my_constants.resultPath, species)
# S, mets, rxns, revs, met_names, rxn_names, biomass, met_comparts = importer.sbmlStoichiometricMatrix(r'D:\Programs\Python\sfw\fca\test\files\networks\pp108.sbml', True, readSpeciesCompart=True)
S, mets, rxns, revs, met_names, rxn_names, biomass, met_comparts = importer.sbmlStoichiometricMatrix(
r'%s/dataset/networks/%s' %
(my_constants.basePath, my_constants.species_sbml[species]),
True,
read_species_compart=True)
#print compute_cohesion_coupling('%s/final_modules.txt' % out_dir, True, S, mets, rxns, revs)
preprocess(
'/Users/fatemeh/Documents/comparison_1/RESULTS/ecoli_core/poolman/final_modules_0.5.txt',
'ecoli_core', False)
def compute_stats_for_all_species(res_path, reaction_types_res_path, species_filter=None):
res = open(res_path, 'w')
res.write('species\ttype\tall\tnot enzymatic\tundefined enzymes\tmissed: from sbml to simtbl\tdisrupted: from sbml to simtbl\trescued by EC Number\n')
reaction_types_res = open(reaction_types_res_path, 'w')
for species, species_file in my_constants.species_sbml.iteritems():
if species_filter and species not in species_filter:
continue
reaction_types = {}
species_row_counter = 0
for type in ['mf', 'bp', 'cc']:
for restric_to_reliable_genes in [False, True]:
by_reaction_type_classifier = []
try:
similarity_table, table_gene_order = get_cached_similarity_tables(species, type, restric_to_reliable_genes)
except:
traceback.print_exc()
continue
S, mets, rxns, revs, met_names, rxn_names, biomass, met_comparts = importer.sbmlStoichiometricMatrix(r'%s/dataset/networks/%s' % (my_constants.basePath, species_file), True, read_species_compart=True)
src_file = r'%s/dataset/networks/%s' % (my_constants.basePath, species_file)
reader = libsbml.SBMLReader()
doc = reader.readSBML(src_file)
go_loaded_model = eval_util.GoLoadedModel(doc.getModel(), species)
moredata_loaded_model = eval_util.MoreDataLoadedModel(species)
all_rxns, not_enzymatic, undefined_gpd, disrupted, missed, ok, rescued = 0, 0, 0, 0, 0, 0, 0
for ri, r in enumerate(rxns):
all_rxns += 1
enz_mapping_state, enz_grp = get_even_ecnumber_annotation_term_ids_for_reaction(r, species, 'go_distance', go_loaded_model, moredata_loaded_model, stats_mode=True)
# enz_mapping_state, enz_grp = eval_util.get_annotation_term_ids_for_reaction(r, species, 'go_distance', go_loaded_model, stats_mode=True)
# enz_all = accumulate_all_enzymes_altogether(enz_grp)
if enz_mapping_state == -1:
decided_reaction_type = -1
not_enzymatic += 1
elif enz_mapping_state == -2:
decided_reaction_type = -2
undefined_gpd += 1
elif enz_mapping_state == -3:
decided_reaction_type = -3
missed += 1
elif enz_mapping_state == -5:
decided_reaction_type = -5
rescued += 1
else:
enz_all = accumulate_all_enzymes_altogether(enz_grp)
if all([e not in similarity_table for e in enz_all]):
decided_reaction_type = -3
missed += 1
elif any([e not in similarity_table for e in enz_all]): # enz_mapping_state == -4:
decided_reaction_type = -4
disrupted += 1
else:
decided_reaction_type = 0
ok += 1
reaction_type = re.findall('[a-z]+$|^R_EX_|^R_DM_|t2$', r)
if reaction_type:
rt = reaction_type[0]
else:
rt = 'unknown'
if rt not in reaction_types:
reaction_types[rt] = set()
reaction_types[rt].add((r, decided_reaction_type, tuple([m for mi, m in enumerate(mets) if S[mi][ri] < 0]), tuple([m for mi, m in enumerate(mets) if S[mi][ri] > 0])))
by_reaction_type_classifier.append((decided_reaction_type, rt))
# for r1 in rxns:
# for r2 in rxns:
# all += 1
# code, desc = compute_similarity_of_gene_pair(r1, r2, similarity_table, species, type, restric_to_reliable_genes, go_loaded_model, stats_mode=True)
# if code == 0:
# ok += 1
# elif code == -1:
# not_enzymatic += 1
# elif code == -2:
# disrupted += 1
# elif code == -3:
# missed += 1
# else:
# print 'ERROR: unknown go_distance:compute_similarity_of_gene_pair status code!'
# exit(1)
# res.write('%s\t%s\t%d\t%d\t%d\t%d\n' % (species, '%s_%s' % (type.lower(), 'g' if restric_to_reliable_genes else 'f'), all_rxns, not_enzymatic, disrupted, missed))
ne_distribution = {}
for ft in reaction_types.keys():
ne_distribution[ft] = 0
for rtc in by_reaction_type_classifier:
if rtc[0] == -1:
ne_distribution[rtc[1]] += 1
found_types0 = sorted(list(reaction_types.keys()), key=lambda x: ne_distribution[x], reverse=True)
found_types = []
try:
rexi = found_types0.index('R_EX_')
found_types.append(found_types0[rexi])
except:
pass
try:
ti = found_types0.index('t')
found_types.append(found_types0[ti])
except:
pass
found_types.append('SUM OTHERS')
sum_others = 0
for ft in found_types0:
if ft not in {'R_EX_', 't'}:
found_types.append(ft)
sum_others += ne_distribution[ft]
ne_distribution['SUM OTHERS'] = sum_others
ne_distribution_line = ''
if species_row_counter == 0:
ne_distribution_line = '\t'.join(found_types)
elif species_row_counter == 1:
ne_distribution_line = '\t'.join([str(ne_distribution[k]) for k in found_types])
species_row_counter += 1
res.write('%s\t%s\t%d\t%d\t%d\t%d\t%d\t%d\t\t\t\t\t%s\n' % (species, '%s_%s' % (type.lower(), 'g' if restric_to_reliable_genes else 'f'), all_rxns, not_enzymatic, undefined_gpd, missed, disrupted, rescued, ne_distribution_line))
reaction_types_res.write('\n--------------------------------------------FOR %s:\n' % species)
reaction_types_res.write(' '.join(sorted(list(reaction_types.keys()))) + '\n')
for k, v in reaction_types.iteritems():
stat = reduce(lambda x, y: tuple(map(operator.add, x, y)), [(1, 0, 0, 0, 0, 0) if z[1] == -1 else (0, 1, 0, 0, 0, 0) if z[1] == -2 else (0, 0, 1, 0, 0, 0) if z[1] == -3 else (0, 0, 0, 1, 0, 0) if z[1] == -4 else (0, 0, 0, 0, 1, 0) if z[1] == -5 else (0, 0, 0, 0, 0, 1) for z in v])
reaction_types_res.write(k + ': ne: %d, undef: %d, mis: %d, dis: %d, rescued: %d, ok: %d\n' % (stat[0], stat[1], stat[2], stat[3], stat[4], stat[5]))
reaction_types_res.write(' ')
pp.pprint(v, reaction_types_res)
res.close()
reaction_types_res.close()
def preprocess(inf, is_mmod, species):
sbml_file = r'%s' % (my_constants.species_sbml[species])
S, mets, rxns, revs, met_names, rxn_names, biomass, met_comparts \
= importer.sbmlStoichiometricMatrix(sbml_file, True, read_species_compart=True)
mo = compute_modularity(inf, not is_mmod, False, True, S, mets, rxns)
return mo
