def test_complex_expansion(self):
input_param = {'Signor': data_formats.pathway['signor']}
settings.setup(network_pickle_cache=False)
# a network with all complexes expanded to single proteins
settings.setup(network_expand_complexes=True)
netw_complex_expanded = main.PyPath()
netw_complex_expanded.keep_raw = True
netw_complex_expanded.init_network(input_param)
# a network with complexes as entities (vertices) in it
settings.setup(network_expand_complexes=False)
netw_complex_entities = main.PyPath()
netw_complex_entities.init_network(input_param)
# collecting all interactions of the complexes
expanded_connections = set()
for v in netw_complex_entities.graph.vs:
if v['type'] != 'complex':
continue
for vid_nb in netw_complex_entities.graph.neighbors(v.index):
names_nb = netw_complex_entities.graph.vs[vid_nb]['name']
# if the neighbor is another complex
uniprots_nb = (names_nb.components.keys() if hasattr(
names_nb, 'components') else (names_nb, ))
for uniprot_nb in uniprots_nb:
for uniprot_comp in v['name'].components.keys():
expanded_connections.add(
tuple(sorted((
uniprot_nb,
uniprot_comp,
))))
# checking if all these connections exist in the expanded network
missing = set()
for uniprot1, uniprot2 in expanded_connections:
if (not netw_complex_expanded.get_edge(uniprot1, uniprot2)
and uniprot1 != uniprot2):
missing.add((uniprot1, uniprot2))
assert not missing
def get_resource(dataset: str):
if dataset == 'graph':
from pypath import main
from pypath import data_formats
pa = main.PyPath()
pa.init_network(data_formats.pathway)
return pa
# TODO add more options
elif dataset == 'complexes':
from pypath import complexes
co = complexes.ComplexAggregator()
return co
elif dataset == 'ptms':
from pypath import ptm
es = ptm.PtmAggregator()
return es
elif dataset == 'annotations':
from pypath import annot
an = ptm.AnnotationTable(keep_annotators = True)
return an
else:
_logger._log('Unknown resource type: `%s`.' % dataset)
def setup_network(self):
if self.network is None:
if not self.omnipath and 'lst' not in self.network_param:
self.network_param['lst'] = data_formats.pathway
self.network = main_mod.PyPath()
if self.omnipath:
self.network.load_omnipath(**self.network_param)
else:
self.network.init_network(**self.network_param)
if isinstance(self.network, main_mod.PyPath):
self.network = network_mod.Network.from_igraph(self.network)
if isinstance(self.network, network_mod.Network):
pass
def test_filter_via(self):
pa = main.PyPath()
pa.init_network(netres.ptm_misc)
assert any(
len(list(a.evidences.filter(via='MIMP')))
for a in pa.graph.es['attrs'])
def test_contains(self):
sr = netres.pathway['signor']
sk = sr.key
si = 'SIGNOR'
pa = main.PyPath()
pa.init_network(netres.pathway)
assert ([sr in a for a in pa.graph.es['attrs']] == [
sk in a for a in pa.graph.es['attrs']
] == [si in a for a in pa.graph.es['attrs']])
def setup_network(self):
if self.network is None:
if not self.omnipath and 'lst' not in self.network_param:
self.network_param['lst'] = data_formats.pathway
self.network = main_mod.PyPath()
self.network.init_network(**self.network_param)
if isinstance(self.network, main_mod.PyPath):
self.network = network_mod.Network(records=self.network)
if isinstance(self.network, network_mod.Network):
pass
def setup_network(self):
if self.network is None:
if not self.use_omnipath and 'lst' not in self.network_param:
resources = copy.deepcopy(data_formats.pathway_all)
resources.update(copy.deepcopy(data_formats.ligand_receptor))
self.network_param['lst'] = resources
self.network = main_mod.PyPath()
self.network.init_network(**self.network_param)
if isinstance(self.network, main_mod.PyPath):
self.network = network_mod.Network(records = self.network)
if isinstance(self.network, network_mod.Network):
pass
def load_network(self):
if not self.do_load_network:
return
self.network = main.PyPath()
if os.path.exists(self.network_pickle):
self.network.init_network(pfile=self.network_pickle)
else:
network_input = copy.deepcopy(data_formats.omnipath)
network_input.update(data_formats.ligand_receptor)
network_input.update(data_formats.ptm_misc)
self.network.load_omnipath(
omnipath=network_input,
remove_htp=False,
)
def test_orthology_translation(self):
"""
We test this on a nice complicated example case when 3 human
genes map to 4 mouse orthologues. We select one interacting partner
and check if it has one and only one interaction with this partner
with the correct direction and sign.
"""
pa = main.PyPath()
pa.init_network({'s': data_formats.pathway['signor']})
pa.homology_translation(target=10090)
adcy1_vs = pa.graph.vs.select(name='O88444')
gnas_1_vs = pa.graph.vs.select(name='P63094')
gnas_2_vs = pa.graph.vs.select(name='Q6R0H7')
gnas_3_vs = pa.graph.vs.select(name='Q6R0H6')
gnas_4_vs = pa.graph.vs.select(name='Q9Z0F1')
assert all(
len(vs) == 1
for vs in (adcy1_vs, gnas_1_vs, gnas_2_vs, gnas_3_vs, gnas_4_vs))
adcy1_i = adcy1_vs[0].index
for gnas_vs in (gnas_1_vs, gnas_2_vs, gnas_3_vs, gnas_4_vs):
gnas_i = gnas_vs[0].index
es = pa.graph.es.select(_between=((gnas_i, ), (adcy1_i, )))
assert len(es) == 1
edge = es[0]
assert edge['dirs'].which_dirs()[0][1] == 'O88444'
assert (edge['dirs'].get_sign(*edge['dirs'].which_dirs()) == [
True, False
])
def interactions(self):
self._log('Building `interactions` data frame.')
dataframes = []
tfregulons = copy.deepcopy(data_formats.transcription)
tfregulons['tfregulons'].input_args['levels'] = {
'A', 'B', 'C', 'D', 'E'
}
param = (
('load_omnipath', {
'kinase_substrate_extra': True,
'ligand_receptor_extra': True,
'pathway_extra': True,
}
),
('init_network', {'lst': tfregulons}),
('init_network', {'lst': data_formats.mirna_target})
)
for to_call, kwargs in param:
pa = main.PyPath()
getattr(pa, to_call)(**kwargs)
e = export.Export(pa)
e.webservice_interactions_df()
dataframes.append(e.df)
if not self.only_human:
graph_human = None
for rodent in (10090, 10116):
if pa.ncbi_tax_id == 9606:
if pa.graph.ecount() < 100000:
graph_human = copy.deepcopy(pa.graph)
else:
if graph_human:
pa.graph = graph_human
pa.ncbi_tax_id = 9606
pa.genesymbol_labels(remap_all = True)
pa.update_vname()
else:
del e
del pa
pa = main.PyPath()
getattr(pa, to_call)(**kwargs)
pa.orthology_translation(rodent)
e = export.Export(pa)
e.webservice_interactions_df()
dataframes.append(e.df)
del e
del pa
self.df_interactions = pd.concat(dataframes)
self.df_interactions.to_csv(
self.outfile_interactions,
sep = '\t',
index = False
)
self._log('Data frame `interactions` has been exported to `%s`.' % (
self.outfile_interactions,
))
