params = parser.parse_args()
M = MatLoader()
M.load_left()
M.load_lrmap()
for deg in DEG:
print('Degre: %d' % deg)
C = M.load_consensus_graphs(deg)
if params.mode == 'pre':
S = M.load_consensus_chemical_synapse(deg)
elif params.mode == 'post':
S = M.load_consensus_chemical_post_synapse(deg)
else:
S = M.load_consensus_gap_junctions(deg)
e = Matrix(cam, params.matrix)
e.load_genes()
e.load_cells(sorted(C.A.nodes()))
e.assign_expression()
e.binarize()
D = nx.DiGraph()
for cell in tqdm(M.left, desc='Cells'):
if not C.C.has_node(cell): continue
syn, neigh = predict.get_synapse_data(S[cell],
e,
cpartners=set(
C.C.neighbors(cell)))
if not syn: continue
gene_sig = set(predict.gene_differential(e.E, syn, neigh))
parser.add_argument('--ie_iter',
dest='ie_iter',
action='store',
required=False,
default=1000,
type=int,
help="Number IE iterations")
params = parser.parse_args()
M = MatLoader()
M.load_left()
D = M.load_consensus_graphs(params.deg)
S = M.load_consensus_chemical_synapse(params.deg)
G = M.load_consensus_gap_junctions(params.deg)
nodes = sorted(D.A.nodes())
e = Matrix(cam, params.matrix)
e.load_genes()
e.load_cells(sorted(D.A.nodes()))
e.assign_expression()
e.binarize()
e.difference_matrix()
wbe = cam_lus.wbe(e, D, cells=M.left)
wbe_data = wbe.get_data()
tmp = params.fout.replace('.', '_wbe.')
aux.write.from_list(tmp, wbe_data)