# -*- coding: utf-8 -*-
"""Convert BEL to y0 mixed graphs."""
from typing import Optional
import pybel
import pybel.constants as pc
from ananke.graphs import ADMG
from pybel import BELGraph
from pybel.dsl import ComplexAbundance
from y0.graph import NxMixedGraph
__all__ = [
'bel_to_nxmg',
'bel_to_admg',
'bel_to_causaleffect',
'emmaa_to_nxmg',
]
CORRELATIVE_RELATIONS = pc.CORRELATIVE_RELATIONS | {pc.CORRELATION}
VALID = {'di', 'bi', 'skip'}
[docs]def bel_to_nxmg(
bel_graph: BELGraph,
*,
include_associations: bool = False,
indirect_handler: Optional[str] = None,
) -> NxMixedGraph:
"""Convert a BEL Graph to a y0 networkx mixed graph.
Rules:
- Directly increases, directly decreases, and directly regulates all become directed edges
- Optional: increases, decreases, and regulates become bidirected edges because there might
be some confounders in the middle
- Positive correlation, negative correlation, and correlation become bidirected edges
- Association edges are excluded by default, could be optionally included
- Only include protein-protein relationships identified by HGNC
:param bel_graph: A BEL graph
:param include_associations: Should :data:`pybel.constants.ASSOCIATION` relationships be included as bidirected
edges in the graph? Defaults to false.
:param indirect_handler: How should indirected edges be handled? If 'bi', adds as bidirected edges. Elif 'di',
adds as bidirected edges. If 'skip', do not include. If None, defaults to 'bi'.
:return: A y0 networkx mixed graph
:raises ValueError: for invalid input on "indirect_handler"
"""
rv = NxMixedGraph()
if indirect_handler is None:
indirect_handler = 'bi'
if indirect_handler not in VALID:
raise ValueError(f'invalid indirect edge handler: {indirect_handler}. Should be in {VALID}')
for u, v, d in bel_graph.edges(data=True):
u_name = _get_name(u)
v_name = _get_name(v)
if u_name == v_name:
continue
if d[pc.RELATION] in CORRELATIVE_RELATIONS:
rv.add_undirected_edge(u_name, v_name)
elif include_associations and d[pc.RELATION] == pc.ASSOCIATION:
rv.add_undirected_edge(u_name, v_name)
elif d[pc.RELATION] in pc.DIRECT_CAUSAL_RELATIONS:
rv.add_directed_edge(u_name, v_name)
elif d[pc.RELATION] in pc.INDIRECT_CAUSAL_RELATIONS:
if indirect_handler == 'bi':
rv.add_undirected_edge(u_name, v_name)
elif indirect_handler == 'di':
rv.add_directed_edge(u_name, v_name)
return rv
def _get_name(u):
if isinstance(u, ComplexAbundance):
return '-'.join(map(_get_name, u.members)) + ' complex'
try:
return u.name
except AttributeError:
return str(u)
[docs]def bel_to_admg(
graph: BELGraph,
*,
include_associations: bool = False,
indirect_handler: Optional[str] = None,
) -> ADMG:
"""Convert a BEL Graph to an Ananke ADMG.
:param graph: A BEL graph
:param include_associations: Should :data:`pybel.constants.ASSOCIATION` relationships be included as bidirected
edges in the graph? Defaults to false.
:param indirect_handler: How should indirected edges be handled? If 'bi', adds as bidirected edges. Elif 'di',
adds as bidirected edges. If 'skip', do not include. If None, defaults to 'bi'.
:return: An Ananke ADMG
>>> import pybel
>>> from y0.dsl import P, Variable
>>> from y0.identify import is_identifiable
>>> from y0_bio.resources import BEL_EXAMPLE
>>> from y0_bio.io.bel import bel_to_nxmg
>>> bel_graph = pybel.load(BEL_EXAMPLE)
>>> nxmg = bel_to_nxmg(bel_graph)
>>> is_identifiable(nxmg, P(Variable('Severe Acute Respiratory Syndrome') @ Variable('angiotensin II')))
"""
nxmg = bel_to_nxmg(
graph,
include_associations=include_associations,
indirect_handler=indirect_handler,
)
return nxmg.to_admg()
[docs]def bel_to_causaleffect(
graph: BELGraph,
*,
include_associations: bool = False,
indirect_handler: Optional[str] = None,
) -> ADMG:
"""Convert a BEL Graph to a CausalEffect R graph object.
:param graph: A BEL graph
:param include_associations: Should :data:`pybel.constants.ASSOCIATION` relationships be included as bidirected
edges in the graph? Defaults to false.
:param indirect_handler: How should indirected edges be handled? If 'bi', adds as bidirected edges. Elif 'di',
adds as bidirected edges. If 'skip', do not include. If None, defaults to 'bi'.
:return: A CausalEffect R graph object
"""
nxmg = bel_to_nxmg(
graph,
include_associations=include_associations,
indirect_handler=indirect_handler,
)
return nxmg.to_causaleffect()
[docs]def emmaa_to_nxmg(model: str, date: Optional[str] = None, **kwargs) -> NxMixedGraph:
"""Get content from EMMAA and convert to a NXMG.
:param model: The name of the EMMAA model
:param date: The optional date of the EMMAA model. See :func:`pybel.from_emmaa`.
:param kwargs: Keyword arguments to pass to :func:`bel_to_nxmg`
:return: A y0 networkx mixed graph
The following example uses the `RAS model <https://www.ndexbio.org/#/network/cc9f904f-4ffd-11e9-9f06-0ac135e8bacf>`_
on EMMAA.
>>> from y0.dsl import P, Variable
>>> from y0.identify import is_identifiable
>>> from y0_bio.io.bel import emmaa_to_nxmg
>>> KRAS = Variable('KRAS')
>>> MAPK1 = Variable('MAPK1')
>>> ras = emmaa_to_nxmg('rasmodel')
>>> is_identifiable(ras, P(MAPK1 @ KRAS))
True
"""
bel_graph = pybel.from_emmaa(model, date=date)
return bel_to_nxmg(bel_graph, **kwargs)