def extract_and_analyze_domains(targets,baselines,input_state):
domain_set = set() # domains from targets and baselines
target_domains = {}
baseline_domains = {}
args = input_state.get_args()
node_types = input_state.node_types
subsmat = input_state.subsmat
num_runs = args['runs']
output_file = args['o']
# output_base = args['o']
# if output_base is not None:
# consensus_file = output_base + ".consensuses.txt"
# domain_file = output_base + ".domains.txt"
ded = DomainExtractionDriver(targets,baselines,node_types,subsmat,num_runs,input_state)
ded.set_debug(1)
ded.start()
domain_set = ded.domain_set
target_domains = ded.target_domains
baseline_domains = ded.baseline_domains
target_consensuses = ded.target_consensuses
baseline_consensuses = ded.baseline_consensuses
print("Testing ded.target_consensuses: "+str(type(target_consensuses[0])))
# Cluster domains
if args['cluster']:
combined_occurrences = merge_counts(target_domains,baseline_domains)
domain_clusters = cluster_domains(combined_occurrences,node_types,subsmat,args)
# Compile counts for each cluster from the individual domains
target_counts = {length:{seed:sum(target_domains[domain] for domain in domain_clusters[length][seed] if domain in target_domains) for seed in domain_clusters[length]} for length in domain_clusters}
baseline_counts = {length:{seed:sum(baseline_domains[domain] for domain in domain_clusters[length][seed] if domain in baseline_domains) for seed in domain_clusters[length]} for length in domain_clusters}
''' old method
target_clust_counts = {}
baseline_clust_counts = {}
#for clust_num in range(len(domain_clusters)):
for seed in domain_clusters:
target_count = 0
baseline_count = 0
for domain in domain_clusters[seed]:
if domain in target_domains.keys():
target_count += target_domains[domain]
if domain in baseline_domains.keys():
baseline_count += baseline_domains[domain]
target_clust_counts[seed] = target_count
baseline_clust_counts[seed] = baseline_count
target_counts = target_clust_counts
baseline_counts = baseline_clust_counts
'''
else:
domain_clusters = {}
target_counts = {}
baseline_counts = {}
# Move all domains into their own clusters, keyed by length
#print("Domain set: "+str(domain_set))
min_length = min(len(domain) for domain in domain_set)
max_length = max(len(domain) for domain in domain_set)
target_counts = {}
baseline_counts = {}
for length in range(min_length,min_domain_size):
domain_clusters[length] = list([[domain] for domain in domain_set if len(domain) == length])
target_counts[length] = {domain:target_domains[domain] for domain in domain_set if len(domain) == length}
baseline_counts[length] = {domain:baseline_domains[domain] for domain in domain_set if len(domain) == length}
# Calculate probability of target being different from baseline for each domain (cluster)
domain_matrices = []
for length in domain_clusters:
dab = DomainAbundanceBuilder(target_counts[length],baseline_counts[length])
domain_matrices = domain_matrices + dab.build();
# Print out domain cluster seeds, their domains, and their p-value
domain_tuples = []
for domain_mat in domain_matrices:
cluster_name = domain_mat.name
#i = int(cluster_name.replace("DC",""))-1
#cluster_seed = domain_seeds[i]
cluster_seed = cluster_name
target_count = target_counts[len(cluster_name)][cluster_name]
baseline_count = baseline_counts[len(cluster_name)][cluster_name]
pval = domain_mat.get_hypergeometric_pval()
# domain_tuples.append((i, cluster_name, cluster_seed, pval, target_counts, baseline_counts))
domain_tuples.append((cluster_name, pval, target_count, baseline_count))
sorted_tuples = sorted(domain_tuples, key=lambda domain: domain[1])
if output_file is not None:
handle = open(output_file,'w')
handle.write("Cluster Seed,pVal,Target Counts,Baseline Counts,Domains\n")
for tup in sorted_tuples:
handle.write(tup[0]+','+str(round(tup[1], 6))+','+str(tup[2])+','+str(tup[3])+', "'+str(domain_clusters[len(tup[0])][tup[0]])+'"\n')
handle.flush()
handle.close()
handle = open(output_file+".consensuses.txt",'w')
handle.write('TARGETS:\n')
for consensus in target_consensuses:
handle.write(consensus.replace('-','')+'\n')
handle.write('\nBASELINES:\n')
for consensus in baseline_consensuses:
handle.write(consensus.replace('-','')+'\n')
handle.close()
else:
for tup in sorted_tuples:
tuple_str = tup[0]+','+str(round(tup[1], 6))+','+str(tup[2])+','+str(tup[3])
print(tuple_str)
print(domain_clusters[len(tup[0])][tup[0]])
print()
print('TARGET CONSENSUSES:\n')
for consensus in target_consensuses:
print(consensus.replace('-',''))
print('\nBASELINES:')
for consensus in baseline_consensuses:
print(consensus.replace('-',''))
print('penne - v.' + str(version) + '\n=============')
cons_query = XMLBuildReader(args['query']).parse().consensuses[0]
cons_baseline = XMLBuildReader(args['baseline']).parse().consensuses[0]
# next, yield domains for both query and baseline datasets.
dsb_query = DomainSetBuilder(cons_query, args['win'], args['max_g'],
args['strip'], is_enum=args['enumerate'])
dsb_baseline = DomainSetBuilder(cons_baseline, args['win'], args['max_g'],
args['strip'], is_enum=args['enumerate'])
# dsb_baseline = DomainSetBuilder(win=args['win'], max_gap=args['max_g'],
# is_enum=args['enumerate'], consensus=cons_baseline,
# is_strip=args['strip'])
domains_query = dsb_query.build() # build abundance counts
domains_baseline = dsb_baseline.build()
status_message('Identifying domains', 'OK')
db = DomainAbundanceBuilder(query=domains_query, baseline=domains_baseline)
domains = db.build() # build contingency matrices
dpp = DomainPrettyPrinter(domains = domains, pval = args['p'],
out=args['o'])
dpp.display() # pretty-print domains
status_message('Domain over-representation computation complete ', 'OK')
else:
args.update({'f':args['query'],'f2':args['baseline'],'a':None})
input_state = InputWrapperState(args)
#input_state.assign_matrix() # parse in-built or custom matrix
targets = input_state.parse_fasta(input_state.fname)
baselines = input_state.parse_fasta(input_state.fname2)
if not args['overlap']:
target_names = list([target.name for target in targets])
baselines = list([baseline for baseline in baselines if baseline.name not in target_names])