def cluster_domains(domain_occurrences,node_types,subsmat,args):
'''
A class that finds pairwise edit distances between domains and clusters them, does so for each domain length.
Returns a domain-seed keyed dictionary of domain lists (clusters).
'''
# Set the minimum size at which domains should be clustered
min_domain_size = 7 # maybe should be 8
print("Clustering domains")
# Set up pairwise alignment args
domain_args = {'f':None,'f2':None,'a':None,'subsmat':subsmat,'gap':-1,'gapopen':0,'matrix':None,'custom':None,'o':None,'n':args['n'],'node_types':None}
input_state = InputWrapperState(domain_args)
input_state.subsmat = subsmat
domains = list([domain for domain in domain_occurrences.keys()])
# Cluster results via average distance criterion
#linkages = cluster.hierarchy.average(distance_mat)
### Split domains up by size first, then cluster those that are large enough
# Determine max length
print("Domains: "+str(domains))
min_length = min(len(domain) for domain in domains)
max_length = max(len(domain) for domain in domains)
clusters_by_length = {}
# Move short domains into their own clusters
for length in range(min_length,min_domain_size):
domains_sub = list([domain for domain in domains if len(domain) == length])
clusters = {}
for domain in domains_sub:
clusters[domain] = [domain]
clusters_by_length[length] = clusters
# Cluster domains that are long enough for each size
for length in range(min_domain_size,max_length+1):
domains_sub = list([domain for domain in domains if len(domain) == length])
#domains_ns = list([NeuriteSequence("D"+str(i),domains[i]) for i in range(len(domains))])
domains_ns = list([NeuriteSequence(domains_sub[i],domains_sub[i]) for i in range(len(domains_sub))])
domain_id_map = {domains_sub[i]:i for i in range(len(domains_sub))}
driver = PairwiseDriver(domains_ns, domains_ns, input_state, store_pairwise=True, score_type='num_gaps')
driver.start()
distance_mat = driver.get_score_matrix()
'''
Determine clusters from hierarchy ???
Use abundance sort and then UCLUST
- First and subsequent sequences are seeds unless they are close enough to an existing seed,
in which case they are merged with the seed's cluster.
- Domains shorter than min_domain_size go in their own separate clusters.
- Domains that are exact sub- or super-sequences of a domain already in a cluster are excluded
'''
# First sort domains by occurrence frequency, requires creating tuples from domain and occurrence count
domain_tuples = []
for domain in domains_sub:
domain_tuples.append((domain,domain_occurrences[domain]))
sorted_tuples = sorted(domain_tuples, key=lambda domain_tup: (domain_tup[1],len(domain_tup[0])), reverse=True)
sorted_domains = list([tup[0] for tup in sorted_tuples])
if len(sorted_domains) > 0:
# UCLUST implementation
separates = {}
clusters = {sorted_domains[0]:list([sorted_domains[0]])}
for domain in sorted_domains[1:]:
domain_pos = domain_id_map[domain]
closest_cluster = -1,1000
if len(domain) < min_domain_size:
separates[domain] = list([domain])
else:
for seed in clusters:
seed_pos = domain_id_map[seed]
dist = max(distance_mat[domain_pos][seed_pos],distance_mat[seed_pos][domain_pos])
if dist <= max_cluster_dist and dist < closest_cluster[1]:
closest_cluster = seed,dist
if closest_cluster[0] == -1:
# If not close enough to any existing seed, add new cluster
clusters[domain] = list([domain])
else:
# Otherwise add it to the cluster it's closest to
clusters[closest_cluster[0]].append(domain)
clusters_by_length[length] = clusters
return clusters_by_length
def run_local(targets, queries, input_state):
driver = PairwiseDriver(targets, queries, input_state)
driver.start() # start only pairwise alignment