def reactions2nodes(get_r_comp, graph, id2n, input_model):
# get_sp_comp = lambda _id: graph[COMPARTMENT][id2n[_id]]
get_sp_comp = lambda _id: input_model.getSpecies(_id).getCompartment()
def link_reaction_to_species(reaction_node,
sp_ref,
all_comps,
is_reactant=True,
reversible=False):
s_id = sp_ref.getSpecies()
all_comps.add(get_sp_comp(s_id))
# todo:
if SKIP_UBS and s_id not in id2n:
return
species_node = id2n[s_id]
e = graph.addEdge(species_node,
reaction_node) if is_reactant else graph.addEdge(
reaction_node, species_node)
stoich = sp_ref.getStoichiometry()
if not stoich:
stoich = sp_ref.getStoichiometryMath()
if not stoich:
stoich = 1
graph[STOICHIOMETRY][e] = stoich
graph[NAME][e] = input_model.getSpecies(s_id).getName()
ub = graph[UBIQUITOUS][species_node]
graph[UBIQUITOUS][e] = ub
graph[REVERSIBLE][e] = reversible and not ub
for r in input_model.getListOfReactions():
name = r.getName()
# do not add fake isa reactions
# if name.find("isa ") != -1 and 1 == r.getNumReactants() == r.getNumProducts() and get_sp_comp(
# r.getListOfReactants().get(0).getSpecies()) == get_sp_comp(
# r.getListOfProducts().get(0).getSpecies()):
# continue
n = graph.addNode()
graph[TERM][n] = get_gene_association(r)
graph[ID][n] = r.getId()
graph[NAME][n] = name
graph[TYPE][n] = TYPE_REACTION
graph[REVERSIBLE][n] = r.getReversible()
graph[VIEW_SHAPE][n] = REACTION_SHAPE
graph[VIEW_SIZE][n] = get_n_size(graph, n)
all_comps = set()
for sp_ref in r.getListOfReactants():
link_reaction_to_species(n,
sp_ref,
all_comps,
is_reactant=True,
reversible=r.getReversible())
for sp_ref in r.getListOfProducts():
link_reaction_to_species(n,
sp_ref,
all_comps,
is_reactant=False,
reversible=r.getReversible())
graph[TRANSPORT][n] = len(all_comps) > 1
graph[COMPARTMENT_ID][n] = get_r_comp(all_comps)
def save_as_cytoscape_json(n2lo, model, out_json, ub_sp_ids):
"""
Converts a model with the given node and edge layout to a json file readable by Cytoscape.
:param ub_sp_ids: collection of ubiquitous species ids
:param n2lo: node layout as a dictionary {node_id: ((x, y), (w, h)) if node is not ubiquitous
else node_id : {r_ids: ((x, y), (w, h)) for r_ids of
reactions using each duplicated metabolite}}
:param model: SBML model
:param out_json: path where to save the resulting json file.
"""
# let's scale the map so that a minimal node has a width == 16 (so that the labels fit)
h_min, (x_shift, y_shift), _ = get_layout_characteristics(n2lo)
scale_factor = MARGIN * 1.0 / h_min if h_min else 1
(x_shift, y_shift) = shift(scale((x_shift, y_shift), scale_factor), MARGIN, MARGIN)
nodes, edges = [], []
for comp in model.getListOfCompartments():
c_id = comp.getId()
c_name = get_name(comp)
if c_id in n2lo:
(x, y), (w, h) = transform(n2lo[c_id], x_shift, y_shift, scale_factor)
nodes.append(get_node(x=x + w / 2, y=y + h / 2, z=-1,
**{NAME: c_name, ID: c_id, WEIGHT: w, HEIGHT: h, TYPE: TYPE_COMPARTMENT}))
for species in model.getListOfSpecies():
s_id = species.getId()
s_name = get_name(species)
glyph_type = UNSPECIFIED_ENTITY
sbo_term = species.getSBOTermID()
if sbo_term:
sbo_term = sbo_term.upper().strip()
if sbo_term in SBO_2_GLYPH_TYPE:
glyph_type = SBO_2_GLYPH_TYPE[sbo_term]
if s_id in n2lo:
if isinstance(n2lo[s_id], dict):
elements = n2lo[s_id].items()
else:
elements = [('', n2lo[s_id])]
for r_ids, coords in elements:
if not r_ids or next((it for it in (model.getReaction(r_id) for r_id in r_ids) if it), False):
(x, y), (w, h) = transform(coords, x_shift, y_shift, scale_factor)
nodes.append(
get_node(x=x + w / 2, y=y + h / 2, z=1,
**{GLYPH_TYPE: glyph_type, ID: "%s_%s" % (s_id, '_'.join(r_ids)) if r_ids else s_id,
COMPARTMENT: species.getCompartment(), NAME: s_name, WEIGHT: w, HEIGHT: h,
TYPE: TYPE_SPECIES, UBIQUITOUS: s_id in ub_sp_ids,
'ChEBI': get_chebi_id(species)}))
def get_sref_id(s_id):
if isinstance(n2lo[s_id], dict):
for r_ids in n2lo[s_id].keys():
if r_id in r_ids:
return "%s_%s" % (s_id, '_'.join(r_ids))
return s_id
for reaction in model.getListOfReactions():
r_id = reaction.getId()
r_name = get_name(reaction)
ga = get_gene_association(reaction)
if r_id in n2lo:
(x, y), (w, h) = transform(n2lo[r_id], x_shift, y_shift, scale_factor)
nodes.append(get_node(x=x + w / 2, y=y + h / 2, z=1,
**{ID: r_id, NAME: ga, WEIGHT: w, HEIGHT: h, TYPE: TYPE_REACTION,
REVERSIBLE: reaction.getReversible(),
UBIQUITOUS:
next((False for s_id in chain(get_reactants(reaction), get_products(reaction))
if s_id not in ub_sp_ids), True),
'genes': ga, 'r_name': r_name}))
for s_id in get_reactants(reaction):
edges.append(get_edge(**{ID: "%s_%s" % (r_id, s_id), SOURCE: r_id, TARGET: get_sref_id(s_id),
NAME: '%s is a substrate of %s' % (get_name(model.getSpecies(s_id)), r_name),
UBIQUITOUS: s_id in ub_sp_ids, INTERACTION: SUBSTRATE}))
for s_id in get_products(reaction):
edges.append(get_edge(**{ID: "%s_%s" % (r_id, s_id), SOURCE: r_id, TARGET: get_sref_id(s_id),
NAME: '%s is a product of %s' % (get_name(model.getSpecies(s_id)), r_name),
UBIQUITOUS: s_id in ub_sp_ids, INTERACTION: PRODUCT}))
save_cyjson(nodes, edges, out_json)
ch_term.get_id() if ch_term else ''])
row += 1
unm_l += 1
print unm_l
ws = wb.create_sheet(2, "Reaction Groups")
row = 1
add_values(ws, row, 1, ["Group Id", "Id", "Name", "Formula", "Gene Association"], HEADER_STYLE)
row += 1
processed_r_ids = set()
for (g_id, g_name), r_ids in sorted(clu2r_ids.items(), key=lambda ((g_id, g_name), _): g_id):
add_values(ws, row, 1, [g_id])
for r_id in sorted(r_ids, key=lambda r_id: r_id[r_id.find('__'):]):
r = model.getReaction(r_id)
add_values(ws, row, 2, [r_id, r.getName(), get_sbml_r_formula(model, r, show_compartments=True, show_metabolite_ids=True),
get_gene_association(r)])
row += 1
processed_r_ids |= r_ids
ws = wb.create_sheet(3, "Ungrouped reactions")
row = 1
add_values(ws, row, 1, ["Id", "Name", "Formula", "Gene Association"], HEADER_STYLE)
row += 1
unm_l = 0
for r in sorted(model.getListOfReactions(), key=lambda r: r.getId()[r.getId().find('__'):]):
if r.getId() not in processed_r_ids:
add_values(ws, row, 1, [r.getId(), r.getName(), get_sbml_r_formula(model, r, show_compartments=True, show_metabolite_ids=True),
get_gene_association(r)])
row += 1
unm_l += 1
print unm_l
