def permute_mutation_data(G, genes, patients, seed, Q=100):
if fortranBindings:
# Compute the desired pieces of the graph structure
A = biadjacency_matrix(G,
row_order=genes,
column_order=patients,
dtype=np.int32)
if sp.sparse.issparse(A):
A = A.todense()
A = np.asarray(A, dtype=np.int32)
# Set up and call the permute matrix function
B = bipartite_edge_swap(A,
nswap=len(G.edges()) * Q,
max_tries=2**31 - 1,
seed=seed,
verbose=False)
H = nx.Graph()
H.add_nodes_from(
genes + patients) # some patients/genes may have zero mutations
H.add_edges_from([(genes[u], patients[v])
for u, v in zip(*np.where(B == 1))])
else:
H = G.copy()
random.seed(seed)
bipartite_double_edge_swap(H,
genes,
patients,
nswap=Q * len(G.edges()))
return graph_to_mutation_data(H, genes, patients)
def call_bipartite_edge_swap( G, xs, ys, Q ):
# Compute the desired pieces of the graph structure
A = np.array(bipartite.biadjacency_matrix(G, row_order=xs, column_order=ys, dtype=np.int32))
# Set up and call the permute matrix function
max_tries = 2**31-1
seed = random.randint(0, 2**31-1)
nswap = len(G.edges()) * Q
B = bipartite_edge_swap(A, nswap, max_tries, seed=seed, verbose=True)
H = nx.Graph()
H.add_edges_from([ (xs[u], ys[v]) for u, v in zip(*np.where(B == 1)) ])
return H
def call_bipartite_edge_swap( G, xs, ys, Q ):
# Compute the desired pieces of the graph structure
A = np.array(bipartite.biadjacency_matrix(G, row_order=xs, column_order=ys, dtype=np.int32))
# Set up and call the permute matrix function
max_tries = 2**31-1
seed = random.randint(0, 2**31-1)
nswap = len(G.edges()) * Q
B = bipartite_edge_swap(A, nswap, max_tries, seed=seed, verbose=True)
H = nx.Graph()
H.add_edges_from([ (xs[u], ys[v]) for u, v in zip(*np.where(B == 1)) ])
return H
def permute_mutation_data(G, genes, patients, seed, Q=100): if fortranBindings: # Compute the desired pieces of the graph structure A = np.array(biadjacency_matrix(G, row_order=genes, column_order=patients, dtype=np.int32), dtype=np.int32) # Set up and call the permute matrix function B = bipartite_edge_swap(A, nswap=len(G.edges()) * Q, max_tries=2**31-1, seed=seed, verbose=False) H = nx.Graph() H.add_nodes_from( genes + patients ) # some patients/genes may have zero mutations H.add_edges_from([ (genes[u], patients[v]) for u, v in zip(*np.where(B == 1)) ]) else: H = G.copy() random.seed(seed) bipartite_double_edge_swap(H, genes, patients, nswap=Q * len( G.edges() )) return graph_to_mutation_data(H, genes, patients)
def call_bipartite_edge_swap( G, xs, ys, Q ):
# Compute the desired pieces of the graph structure
xs.sort(G.degree, reverse=True)
ys.sort(G.degree, reverse=True)
x_degrees = [ G.degree(x) for x in xs ]
y_degrees = [ G.degree(y) for y in ys ]
A = np.array(bipartite.biadjacency_matrix(G, row_order=xs, column_order=ys, dtype=np.int32))
# Set up and call the permute matrix function
max_tries = 1e75
seed = random.randint(0, 2**32-1)
nswap = len(G.edges()) * Q
B = bipartite_edge_swap(A, x_degrees, y_degrees, nswap, max_tries, seed)
H = nx.Graph()
H.add_edges_from([ (xs[u], ys[v]) for u, v in zip(*np.where(B == 1)) ])
return H
def call_bipartite_edge_swap(G, xs, ys, Q):
# Compute the desired pieces of the graph structure
xs.sort(G.degree, reverse=True)
ys.sort(G.degree, reverse=True)
x_degrees = [G.degree(x) for x in xs]
y_degrees = [G.degree(y) for y in ys]
A = np.array(
bipartite.biadjacency_matrix(G,
row_order=xs,
column_order=ys,
dtype=np.int32))
# Set up and call the permute matrix function
max_tries = 1e75
seed = random.randint(0, 2**32 - 1)
nswap = len(G.edges()) * Q
B = bipartite_edge_swap(A, x_degrees, y_degrees, nswap, max_tries, seed)
H = nx.Graph()
H.add_edges_from([(xs[u], ys[v]) for u, v in zip(*np.where(B == 1))])
return H
def permute_mutation_data(G, genes, patients, seed, Q=100):
print
if fortranBindings:
# Compute the desired pieces of the graph structure
xs = sorted(genes, key=G.degree, reverse=True)
ys = sorted(patients, key=G.degree, reverse=True)
x_degrees = [ G.degree(x) for x in xs ]
y_degrees = [ G.degree(y) for y in ys ]
A = np.array(biadjacency_matrix(G, row_order=xs,
column_order=ys,
dtype=np.int32))
# Set up and call the permute matrix function
B = bipartite_edge_swap(A, x_degrees, y_degrees, len(G.edges()) * Q, 1e9, seed=seed)
H = nx.Graph()
H.add_nodes_from( genes + patients ) # some patients/genes may have zero mutations
H.add_edges_from([ (xs[u], ys[v]) for u, v in zip(*np.where(B == 1)) ])
else:
H = G.copy()
random.seed(seed)
bipartite_double_edge_swap(H, genes, patients, nswap=Q * len( G.edges() ))
return graph_to_mutation_data(H, genes, patients)
