"Using SCIPY to compute the matrix exponential, t=0.1...\n")
# No kernel supplied: use SCIPY to generate a kernel on the fly, and then use it
# for subsequent diffusion operations
if path.exists('tiedie_kernel.pkl'):
fh = open('tiedie_kernel.pkl', 'rb')
diffuser = pickle.load(fh)
fh.close()
else:
diffuser = SciPYKernel(opts.network)
pk_file = open('tiedie_kernel.pkl', 'wb')
pickle.dump(diffuser, pk_file)
pk_file.close()
# Check to make sure the node universe matches the
k_labels = diffuser.getLabels()
if len(network_nodes) != len(k_labels) or len(
network_nodes.intersection(k_labels)) != len(k_labels):
sys.stderr.write(
"Error: the universe of gene/node labels in the network file doesn't match the supplied kernel file!\n"
)
sys.exit(1)
sys.stderr.write("Diffusing Heats...\n")
# Separately perform diffusion for each input set
up_heats_diffused = diffuser.diffuse(up_heats, reverse=False)
down_heats_diffused = diffuser.diffuse(down_heats, reverse=True)
# Extract a subnetwork solution from the diffused heats, the set of nodes found, the Relevance score for this network solution
# and the heat scores for all linker genes
subnet_soln, subnet_soln_nodes, alpha_score, linker_scores = extractSubnetwork(
