def main(interactome, tsv, gene_file, only_crosses):
if only_crosses and len(gene_file) < 2:
raise Exception('Must have at least 2 gene files for --only-crosses')
# Load network
G = read_network(interactome)
all_genes_in_network = set(G.nodes())
remove_self_links(G)
if tsv:
with open(tsv, 'wb') as f:
f.write('#\trank\tprotein_a\tprotein_b\td_AB\ts_AB\n')
print ''
# Read gene files and separate by protein
genes = defaultdict(list)
for data_file, title in gene_file:
with open(data_file) as f:
for line in f.read().splitlines():
if line.startswith('#'):
continue
protein, gene_ids = line.split('\t')
if title and len(gene_file) > 1:
protein = '%s [%s]' % (protein, title)
genes[protein].extend(gene_ids.split(';'))
# Returns d_AB and s_AB of a protein pair
def analyze_proteins(protein_a, protein_b):
if (protein_a, protein_b) in cache:
return cache[(protein_a, protein_b)]
genes_A = set(genes[protein_a]) & all_genes_in_network
genes_B = set(genes[protein_b]) & all_genes_in_network
# Perform calculations
d_A = calc_single_set_distance(G, genes_A)
d_B = calc_single_set_distance(G, genes_B)
d_AB = calc_set_pair_distances(G, genes_A, genes_B)
s_AB = d_AB - (d_A + d_B) / 2.
return d_AB, s_AB
# Print information about each protein set as debugging information
print 'PROTEINS'
for i, protein in enumerate(sorted(genes)):
print str(i + 1) + '. ' + protein + '; S = ' + \
str(get_lcc_size(G, genes[protein]))
print ''
# Analyze each protein combination and sort from lowest s_AB to highest
count = 1
analyses = []
for protein_a, protein_b in map(
tuple, combinations_with_replacement(sorted(genes), 2)):
if only_crosses and _title_from_protein(
protein_a) == _title_from_protein(protein_b):
continue
d_AB, s_AB = analyze_proteins(protein_a, protein_b)
analyses.append((protein_a, protein_b, d_AB, s_AB))
count += 1
analyses.sort(key=lambda x: x[3])
# Print analyses in order
for i, analysis in enumerate(analyses):
if tsv:
with open(tsv, 'ab') as f:
f.write('\t'.join((str(i + 1), analysis[0], analysis[1],
str(analysis[2]), str(analysis[3]))) + '\n')
print str(i + 1) + '. Proteins: ' + analysis[0] + ' and ' \
+ analysis[1]
print 'd_AB = ' + str(analysis[2])
print 's_AB = ' + str(analysis[3])
print ''
def main(interactome, tsv, gene_file, only_crosses):
if only_crosses and len(gene_file) < 2:
raise Exception('Must have at least 2 gene files for --only-crosses')
# Load network
G = read_network(interactome)
all_genes_in_network = set(G.nodes())
remove_self_links(G)
if tsv:
with open(tsv, 'wb') as f:
f.write('#\trank\tprotein_a\tprotein_b\td_AB\ts_AB\n')
print ''
# Read gene files and separate by protein
genes = defaultdict(list)
for data_file, title in gene_file:
with open(data_file) as f:
for line in f.read().splitlines():
if line.startswith('#'):
continue
protein, gene_ids = line.split('\t')
if title and len(gene_file) > 1:
protein = '%s [%s]' % (protein, title)
genes[protein].extend(gene_ids.split(';'))
# Returns d_AB and s_AB of a protein pair
def analyze_proteins(protein_a, protein_b):
if (protein_a, protein_b) in cache:
return cache[(protein_a, protein_b)]
genes_A = set(genes[protein_a]) & all_genes_in_network
genes_B = set(genes[protein_b]) & all_genes_in_network
# Perform calculations
d_A = calc_single_set_distance(G, genes_A)
d_B = calc_single_set_distance(G, genes_B)
d_AB = calc_set_pair_distances(G, genes_A, genes_B)
s_AB = d_AB - (d_A + d_B)/2.
return d_AB, s_AB
# Print information about each protein set as debugging information
print 'PROTEINS'
for i, protein in enumerate(sorted(genes)):
print str(i + 1) + '. ' + protein + '; S = ' + \
str(get_lcc_size(G, genes[protein]))
print ''
# Analyze each protein combination and sort from lowest s_AB to highest
count = 1
analyses = []
for protein_a, protein_b in map(tuple, combinations_with_replacement(sorted(genes), 2)):
if only_crosses and _title_from_protein(protein_a) == _title_from_protein(protein_b):
continue
d_AB, s_AB = analyze_proteins(protein_a, protein_b)
analyses.append((protein_a, protein_b, d_AB, s_AB))
count += 1
analyses.sort(key=lambda x: x[3])
# Print analyses in order
for i, analysis in enumerate(analyses):
if tsv:
with open(tsv, 'ab') as f:
f.write('\t'.join((str(i + 1), analysis[0], analysis[1],
str(analysis[2]), str(analysis[3]))) + '\n')
print str(i + 1) + '. Proteins: ' + analysis[0] + ' and ' \
+ analysis[1]
print 'd_AB = ' + str(analysis[2])
print 's_AB = ' + str(analysis[3])
print ''
def main(interactome, gene_file):
# Load network
sys.stdout = StringIO()
G = read_network(interactome)
all_genes_in_network = set(G.nodes())
remove_self_links(G)
sys.stdout = sys.__stdout__
# Read gene files and separate by protein
genes = defaultdict(list)
for data_file, title in gene_file:
with open(data_file) as f:
for line in f.read().splitlines():
if line.startswith('#'):
continue
protein, gene_ids = line.split('\t')
if title and len(gene_file) > 1:
protein = '%s [%s]' % (protein, title)
genes[protein].extend(gene_ids.split(';'))
# Returns d_AB, s_AB, and ((gene_1, gene_2, dist)...) of a protein pair
def analyze_proteins(protein_a, protein_b):
genes_A = set(genes[protein_a]) & all_genes_in_network
genes_B = set(genes[protein_b]) & all_genes_in_network
all_path_lengths = get_pathlengths_for_two_sets(G, genes_A, genes_B)
all_distances = []
# Perform calculations
d_A = calc_single_set_distance(G, genes_A)
d_B = calc_single_set_distance(G, genes_B)
for gene_A in genes_A:
all_distances_A = []
for gene_B in genes_B:
if gene_A == gene_B:
all_distances_A.append((gene_A, gene_B, 0))
else:
try:
all_distances_A.append((gene_A, gene_B, all_path_lengths[min(gene_A, gene_B)][max(gene_A, gene_B)]))
except KeyError:
pass
if len(all_distances_A) > 0:
all_distances.append(min(all_distances_A, key=lambda x: x[2]))
for gene_B in genes_B:
all_distances_B = []
for gene_A in genes_A:
if gene_A == gene_B:
all_distances_B.append((gene_A, gene_B, 0))
else:
try:
all_distances_B.append((gene_B, gene_A, all_path_lengths[min(gene_A, gene_B)][max(gene_A, gene_B)]))
except KeyError:
pass
if len(all_distances_B) > 0:
all_distances.append(min(all_distances_B, key=lambda x: x[2]))
d_AB = np.mean(map(lambda x: x[2], all_distances))
s_AB = d_AB - (d_A + d_B)/2.
return d_AB, s_AB, sorted(all_distances, key=lambda x: x[2])
# Print information about each protein set as debugging information
print 'PROTEINS'
for i, protein in enumerate(sorted(genes)):
print str(i + 1) + '. ' + protein + '; S = ' + \
str(get_lcc_size(G, genes[protein]))
print ''
# Analyze each protein combination and sort from lowest s_AB to highest
analyses = []
for protein_a, protein_b in map(tuple, combinations_with_replacement(sorted(genes), 2)):
d_AB, s_AB, distances = analyze_proteins(protein_a, protein_b)
analyses.append((protein_a, protein_b, d_AB, s_AB, distances))
analyses.sort(key=lambda x: x[3])
# Print analyses in order
for i, analysis in enumerate(analyses):
protein_a, protein_b, d_AB, s_AB, distances = analysis
print str(i + 1) + '. Proteins: ' + protein_a + ' and ' + protein_b
print 'd_AB = ' + str(d_AB)
print 's_AB = ' + str(s_AB)
for gene_1, gene_2, dist in distances:
print gene_1 + ' -> ' + gene_2 + ' = ' + str(dist)
print ''
