def test_get_tf_pairs(self):
"""Test iterating over transcription factor pairs."""
graph = BELGraph()
p1, p2, p3 = (Protein('test', str(i)) for i in range(1, 4))
r4, r5, r6 = (Rna('test', str(j)) for j in range(4, 7))
g4 = r4.get_gene()
self.assertIsNotNone(g4)
g5 = r5.get_gene()
self.assertIsNotNone(g5)
c14, c25 = ComplexAbundance([p1, g4]), ComplexAbundance([p2, g5])
_tf_up(graph, p1, r4)
_tf_down(graph, p2, r5)
graph.add_correlation(p3, r6, citation=n(), evidence=n())
self.assertEqual({p1, p2, p3, r4, r5, r6, g4, g5, c14, c25},
set(graph))
expected_edges = [
(c14, r4),
(p1, c14),
(g4, c14),
(c25, r5),
(p2, c25),
(g5, c25),
(p3, r6),
(r6, p3),
]
sorted_expected_edges = sorted(expected_edges, key=_bel_pair_key)
sorted_actual_edges = sorted(graph.edges(), key=_bel_pair_key)
self.assertEqual(sorted_expected_edges, sorted_actual_edges)
pairs = set(get_tf_pairs(graph))
expected_pairs = {(p1, r4, +1), (p2, r5, -1)}
self.assertEqual(expected_pairs, pairs)
def _add_my_row(graph: BELGraph, row) -> None:
relation = row['relation']
source_uniprot_id = row['source']
target_uniprot_id = row['target']
pubmed_ids = row['pubmed_ids']
pubmed_ids = pubmed_ids.split('|')
source = pybel.dsl.Protein(
namespace='uniprot',
identifier=source_uniprot_id,
name=get_mnemonic(source_uniprot_id),
)
target = pybel.dsl.Protein(
namespace='uniprot',
identifier=target_uniprot_id,
name=get_mnemonic(target_uniprot_id),
)
for pubmed_id in pubmed_ids:
if relation == 'deubiquitination':
target_ub = target.with_variants(
pybel.dsl.ProteinModification('Ub'))
graph.add_decreases(
source,
target_ub,
citation=pubmed_id,
evidence='From intact',
)
elif relation == 'ubiqutination':
target_ub = target.with_variants(
pybel.dsl.ProteinModification('Ub'))
graph.add_increases(
source,
target_ub,
citation=...,
evidence='From intact',
)
elif relation == 'degratation':
graph.add_decreases(
source,
target,
citation=...,
evidence='From intact',
)
elif relation == 'activates':
graph.add_increases(
source,
target,
...,
object_modifier=pybel.dsl.activity(),
)
elif relation == 'co-expressed':
graph.add_correlation(
pybel.dsl.Rna(
namespace='uniprot',
identifier=source_uniprot_id,
name=get_mnemonic(source_uniprot_id),
),
pybel.dsl.Rna(
namespace='uniprot',
identifier=target_uniprot_id,
name=get_mnemonic(target_uniprot_id),
),
annotations=dict(cell_line={'HEK2': True}),
)
