def _rename_network_nodes(self, nodeclass_name, nodeset_name, node_name,
new_node_name):
new_mapping = {node_name: new_node_name}
for nk in self.networks:
if nk.graph['sourceType'] == nodeclass_name and nk.graph['source'] == nodeset_name or \
nk.graph['targetType'] == nodeclass_name and nk.graph['target'] == nodeset_name:
nx.relabel_nodes(nk, new_mapping)
def test_relabel_toposort():
K5 = nx.complete_graph(4)
G = nx.complete_graph(4)
G = nx.relabel_nodes(G, {i: i + 1 for i in range(4)}, copy=False)
assert nx.is_isomorphic(K5, G)
G = nx.complete_graph(4)
G = nx.relabel_nodes(G, {i: i - 1 for i in range(4)}, copy=False)
assert nx.is_isomorphic(K5, G)
def test_relabel_selfloop():
G = nx.DiGraph([(1, 1), (1, 2), (2, 3)])
G = nx.relabel_nodes(G, {1: 'One', 2: 'Two', 3: 'Three'}, copy=False)
assert sorted(G.nodes()) == ['One', 'Three', 'Two']
G = nx.MultiDiGraph([(1, 1), (1, 2), (2, 3)])
G = nx.relabel_nodes(G, {1: 'One', 2: 'Two', 3: 'Three'}, copy=False)
assert sorted(G.nodes()) == ['One', 'Three', 'Two']
G = nx.MultiDiGraph([(1, 1)])
G = nx.relabel_nodes(G, {1: 0}, copy=False)
assert sorted(G.nodes()) == [0]
def _rename_network_nodes(self, nodeclass_name, nodeset_name, node_name, new_node_name):
new_mapping = {node_name: new_node_name}
for nk in self.networks:
if (
nk.graph["sourceType"] == nodeclass_name
and nk.graph["source"] == nodeset_name
or nk.graph["targetType"] == nodeclass_name
and nk.graph["target"] == nodeset_name
):
nx.relabel_nodes(nk, new_mapping)
def make_(self):
"Result on one (w,k,length_attri) list (usually (w,k,1)) for 1D CNN "
forcnn = []
self.all_subgraph = []
f = self.select_node_sequence()
for graph in f:
frelabel = nx.relabel_nodes(
graph, nx.get_node_attributes(
graph, 'labeling')) #rename the nodes wrt the labeling
self.all_subgraph.append(frelabel)
if self.one_hot > 0:
forcnn.append([
utils.indices_to_one_hot(x[1], self.one_hot)
for x in sorted(nx.get_node_attributes(
frelabel, 'attr_name').items(),
key=lambda x: x[0])
])
else:
forcnn.append([
x[1] for x in sorted(nx.get_node_attributes(
frelabel, 'attr_name').items(),
key=lambda x: x[0])
])
if np.array(forcnn).shape[0] != self.w or np.array(
forcnn).shape[1] != self.k:
raise BadShapeError(
'Shapes do not match : {0} instead of {1}'.format(
np.array(forcnn).shape, (self.w, self.k)))
return forcnn
def create_all_rfs(self):
"""
Method that transforms the graph attribute of the PCSN object into suitable input for CNN
:return: (width, rf_size, attr_dim) input for CNN
"""
input_to_cnn = list()
# select node sequence returns full list of receptive fields created
receptive_fields = self.node_sequence_selection()
for field in receptive_fields:
relabeled_nodes = nx.relabel_nodes(field, nx.get_node_attributes(field, 'labeling'))
input_to_cnn.append(
[x[1] for x in sorted(nx.get_node_attributes(relabeled_nodes, 'attr_name').items(),
key=lambda x: x[0])])
return input_to_cnn
def plot_so(so, terms_to_include, name, joiners=[]):
g = so.subgraph([
node for node, data in so.nodes(data=True)
if data['name'] in terms_to_include + joiners
])
g = g.reverse()
#print(g.edge[list(a)[0][0]])
for node, data in g.nodes_iter(data=True):
if data['name'] in joiners:
g.node[node]['style'] = 'dashed'
g = nx.relabel_nodes(
g, {node: data['name']
for node, data in g.nodes(data=True)})
a = nx_agraph.to_agraph(g)
a.layout(
'dot',
args=
'-Nfontsize=12 -Nwidth=".2" -Nheight=".2" -Nshape="box" -Nmargin=.04 -Gfontsize=8 -Earrowsize=.5'
)
a.draw('reports/' + name + '.png')
def wl_normalization(self, graph):
result = {}
labeled_graph = nx.Graph(graph)
relabel_dict_ = {}
graph_node_list = list(graph.nodes())
for i in range(len(graph_node_list)):
relabel_dict_[graph_node_list[i]] = i
i += 1
inv_relabel_dict_ = {v: k for k, v in relabel_dict_.items()}
graph_relabel = nx.relabel_nodes(graph, relabel_dict_)
label_lookup = {}
label_counter = 0
l_aux = list(
nx.get_node_attributes(graph_relabel, 'attr_name').values())
labels = np.zeros(len(l_aux), dtype=np.int32)
adjency_list = list([
list(x[1].keys()) for x in graph_relabel.adjacency()
]) #adjency list à l'ancienne comme version 1.0 de networkx
for j in range(len(l_aux)):
if not (l_aux[j] in label_lookup):
label_lookup[l_aux[j]] = label_counter
labels[j] = label_counter
label_counter += 1
else:
labels[j] = label_lookup[l_aux[j]]
# labels are associated to a natural number
# starting with 0.
new_labels = copy.deepcopy(labels)
# create an empty lookup table
label_lookup = {}
label_counter = 0
for v in range(len(adjency_list)):
# form a multiset label of the node v of the i'th graph
# and convert it to a string
long_label = np.concatenate(
(np.array([labels[v]]), np.sort(labels[adjency_list[v]])))
long_label_string = str(long_label)
# if the multiset label has not yet occurred, add it to the
# lookup table and assign a number to it
if not (long_label_string in label_lookup):
label_lookup[long_label_string] = label_counter
new_labels[v] = label_counter
label_counter += 1
else:
new_labels[v] = label_lookup[long_label_string]
# fill the column for i'th graph in phi
labels = copy.deepcopy(new_labels)
dict_ = {inv_relabel_dict_[i]: labels[i] for i in range(len(labels))}
nx.set_node_attributes(labeled_graph, dict_, 'labeling')
result['labeled_graph'] = labeled_graph
result['ordered_nodes'] = [
x[0] for x in sorted(dict_.items(), key=lambda x: x[1])
]
return result
def poly_aaa_consequences(variants):
mutations_in_cds_hgvs_format = defaultdict(list)
indels = Counter()
all = Counter()
for variant in all_poly_a_variants(variants, preserve_sources=True):
for transcript in variant.affected_transcripts:
if not transcript.poly_aaa:
continue
for alt, aaa_data in transcript.poly_aaa.items():
if aaa_data.increased:
category = 'increased'
elif aaa_data.decreased:
category = 'decreased'
else:
category = 'constant'
hgvs = transcript.as_hgvs(variant.ref, alt)
if 'del' in hgvs or 'ins' in hgvs:
indels[category] += 1
all[category] += 1
mutations_in_cds_hgvs_format[category].append(hgvs)
mutations_in_cds_hgvs_format['all'].append(hgvs)
print('Indels enrichment:')
for category in indels:
print(category, indels[category] / all[category] * 100, '%')
for category, muts in mutations_in_cds_hgvs_format.items():
report(
'Mutations which result in ' + category + ' in cds hgvs formats',
muts)
consequences = defaultdict(Counter)
skipped = Counter()
for category, muts in mutations_in_cds_hgvs_format.items():
filename = report(
'Mutations which result in ' + category + ' in cds hgvs formats',
muts)
vep_filename = vep(filename)
for line in open(vep_filename):
if line.startswith('#'):
continue
line = line.split('\t')
tested_transcript = line[0].split(':')[0]
vep_transcript = line[4]
if line[5] != 'Transcript':
skipped['Not a transcript feature'] += 1
continue
if tested_transcript != vep_transcript:
skipped['Different transcript'] += 1
continue
variant_consequences = line[6].split(',')
for consequence in variant_consequences:
consequences[category][consequence] += 1
print(skipped)
print('Raw consequences')
print(consequences)
graph = load_sequence_ontology()
expanded_consequences = propagate_consequences(graph, consequences)
for category, counts in expanded_consequences.items():
consequences_to_include = ['coding_sequence_variant']
consequences_to_include.extend(counts.keys())
g = graph.subgraph([
node for node, data in graph.nodes(data=True)
if data['name'] in consequences_to_include
])
g = g.reverse()
max_count = max(counts.values())
for node, data in g.nodes_iter(data=True):
name = data['name']
count = counts[name]
color = (255 - int(log((count / max_count) + 1) * 255), 255, 255)
g.node[node]['style'] = 'filled'
g.node[node]['shape'] = 'box'
color = '#%02x%02x%02x' % color
g.node[node]['fillcolor'] = color
if name not in consequences[category]:
g.node[node]['style'] = 'dashed,filled'
g = nx.relabel_nodes(
g, {
node: data['name'].replace('variant', 'v.') +
': %s' % counts.get(data['name'])
for node, data in g.nodes(data=True)
})
a = nx_agraph.to_agraph(g)
a.layout(
'dot',
args=
'-Nfontsize=14 -Nwidth=".2" -Nheight=".2" -Nmargin=.1 -Gfontsize=8 -Earrowsize=.5'
)
a.draw('reports/poly_a_consequences_dag_' + category + '.svg')
selected_consequences_groups = {
'General coding':
['synonymous_variant', 'frameshift_variant', 'inframe_variant'],
'Inframe': [
'inframe_deletion', 'inframe_insertion', 'missense_variant',
'stop_gained', 'stop_lost'
]
}
for group, selected_consequences in selected_consequences_groups.items():
for category, counts in expanded_consequences.items():
data = {
consequence: counts[consequence]
for consequence in selected_consequences
}
data = OrderedDict(sorted(data.items(), key=itemgetter(1)))
# Create a pie chart
wedges = plt.pie(
list(data.values()),
labels=list(data.keys()),
shadow=False,
colors=plt.cm.tab20(
numpy.linspace(1, 0, len(selected_consequences))),
startangle=0,
autopct='%1.1f%%',
)
for pie_wedge in wedges[0]:
pie_wedge.set_edgecolor('black')
# View the plot drop above
plt.axis('equal')
plt.title(group + ' consequences for variants causing ' +
category + ' in poly(A) length')
plt.tight_layout()
save_plot(plt, hide_title=True)