def get_deltas_classic(full_index2gene, heat, num_perms, num_cores, infmat, addtl_genes, min_cc_size, max_cc_size):
# find delta that maximizes # CCs of size >= min_cc_size for each permuted data set
deltas = ft.get_deltas_for_heat(infmat, full_index2gene, heat, addtl_genes, num_perms, NUM_CCS,
[min_cc_size], True, num_cores)
# find the multiple of the median delta s.t. the size of the largest CC in the real data
# is <= MAX_CC_SIZE
medianDelta = np.median(deltas[min_cc_size])
sim, index2gene = hn.similarity_matrix(infmat, full_index2gene, heat, False)
for i in range(1, 11):
G = hn.weighted_graph(sim, index2gene, i*medianDelta)
largest_cc_size = max([len(cc) for cc in hn.connected_components(G)])
if largest_cc_size <= max_cc_size:
break
# and run HotNet with that multiple and the next 4 multiples
return [i*medianDelta for i in range(i, i+5)]
def get_deltas_classic(full_index2gene, heat, num_perms, num_cores, infmat, addtl_genes, min_cc_size, max_cc_size):
# find delta that maximizes # CCs of size >= min_cc_size for each permuted data set
deltas = ft.get_deltas_for_heat(infmat, full_index2gene, heat, addtl_genes, num_perms, NUM_CCS,
[min_cc_size], True, num_cores)
# find the multiple of the median delta s.t. the size of the largest CC in the real data
# is <= MAX_CC_SIZE
medianDelta = np.median(deltas[min_cc_size])
sim, index2gene = hn.similarity_matrix(infmat, full_index2gene, heat, False)
for i in range(1, 11):
G = hn.weighted_graph(sim, index2gene, i*medianDelta)
largest_cc_size = max([len(cc) for cc in hn.connected_components(G)])
if largest_cc_size <= max_cc_size:
break
# and run HotNet with that multiple and the next 4 multiples
return [i*medianDelta for i in range(i, i+5)]