def _find_ab_compartments(gamma, matrix, breaks, cmprtsec, save=True, verbose=False):
# function to convert correlation into distances
gamma += 1
func = lambda x: -abs(x)**gamma / x
funczero = lambda x: 0.0
# calculate distance_matrix
dist_matrix = [[0 for _ in xrange(len(breaks))]
for _ in xrange(len(breaks))]
scores = {}
for k, cmprt in enumerate(cmprtsec):
beg1, end1 = cmprt['start'], cmprt['end']
diff1 = end1 - beg1
scores[(k,k)] = dist_matrix[k][k] = -1
for l in xrange(k + 1, len(cmprtsec)):
beg2, end2 = cmprtsec[l]['start'], cmprtsec[l]['end']
val = nansum([matrix[i][j] for i in xrange(beg1, end1)
for j in xrange(beg2, end2)]) / (end2 - beg2) / diff1
try:
scores[(k,l)] = dist_matrix[k][l] = scores[(l,k)] = dist_matrix[l][k] = func(val)
except ZeroDivisionError:
scores[(k,l)] = dist_matrix[k][l] = scores[(l,k)] = dist_matrix[l][k] = funczero(val)
if isnan(scores[(k,l)]):
scores[(k,l)] = dist_matrix[k][l] = scores[(l,k)] = dist_matrix[l][k] = funczero(0)
# cluster compartments according to their correlation score
try:
clust = linkage(dist_matrix, method='ward')
except UnboundLocalError:
print('WARNING: Chromosome probably too small. Skipping')
warn('WARNING: Chromosome probably too small. Skipping')
return (0,0,0,0)
# find best place to divide dendrogram (only check 1, 2, 3 or 4 clusters)
solutions = {}
for k in clust[:,2][-3:]:
clusters = {}
_ = [clusters.setdefault(j, []).append(i) for i, j in
enumerate(fcluster(clust, k, criterion='distance'))]
solutions[k] = {'out': clusters}
solutions[k]['score'] = calinski_harabasz(scores, clusters)
try:
# take best cluster according to calinski_harabasz score
clusters = [solutions[s] for s in sorted(
solutions, key=lambda x: solutions[x]['score'])
if solutions[s]['score']>0][-1]['out']
except IndexError:
#warn('WARNING: compartment clustering is not clear. Skipping')
return (0,0,0,0)
if len(clusters) != 2:
#warn('WARNING: compartment clustering is too clear. Skipping')
return (0,0,0,0)
# labelling compartments. A compartments shall have lower
# mean intra-interactions
dens = {}
for k in clusters:
val = sum([cmprtsec[c]['dens']
for c in clusters[k]]) / len(clusters[k])
dens['A' if val < 1 else 'B'] = [
cmprtsec[c]['dens'] for c in clusters[k]
if cmprtsec[c]['end'] - cmprtsec[c]['start'] > 2]
if save:
for c in clusters[k]:
cmprtsec[c]['type'] = 'A' if val < 1 else 'B'
try:
tt, pval = ttest_ind(dens['A'], dens['B'])
except ZeroDivisionError:
return (0,0,0,0)
prop = float(len(dens['A'])) / (len(dens['A']) + len(dens['B']))
score = 5000*(prop- 0.5)**4 - 2
if verbose:
print 'g:%5s %5s%% pen:%7s tt:%7s score:%7s pv:%s' % (
gamma - 1, round(prop*100, 1), round(score, 3), round(tt, 3),
round(score + tt, 3), pval)
return score + tt, tt, prop
def _find_ab_compartments(gamma,
matrix,
breaks,
cmprtsec,
save=True,
verbose=False):
# function to convert correlation into distances
gamma += 1
func = lambda x: -abs(x)**gamma / x
funczero = lambda x: 0.0
# calculate distance_matrix
dist_matrix = [[0 for _ in xrange(len(breaks))]
for _ in xrange(len(breaks))]
scores = {}
for k, cmprt in enumerate(cmprtsec):
beg1, end1 = cmprt['start'], cmprt['end']
diff1 = end1 - beg1
scores[(k, k)] = dist_matrix[k][k] = -1
for l in xrange(k + 1, len(cmprtsec)):
beg2, end2 = cmprtsec[l]['start'], cmprtsec[l]['end']
val = nansum([
matrix[i][j] for i in xrange(beg1, end1)
for j in xrange(beg2, end2)
]) / (end2 - beg2) / diff1
try:
scores[(k, l)] = dist_matrix[k][l] = scores[(
l, k)] = dist_matrix[l][k] = func(val)
except ZeroDivisionError:
scores[(k, l)] = dist_matrix[k][l] = scores[(
l, k)] = dist_matrix[l][k] = funczero(val)
if isnan(scores[(k, l)]):
scores[(k, l)] = dist_matrix[k][l] = scores[(
l, k)] = dist_matrix[l][k] = funczero(0)
# cluster compartments according to their correlation score
try:
clust = linkage(dist_matrix, method='ward')
except UnboundLocalError:
print('WARNING: Chromosome probably too small. Skipping')
warn('WARNING: Chromosome probably too small. Skipping')
return (0, 0, 0, 0)
# find best place to divide dendrogram (only check 1, 2, 3 or 4 clusters)
solutions = {}
for k in clust[:, 2][-3:]:
clusters = {}
_ = [
clusters.setdefault(j, []).append(i)
for i, j in enumerate(fcluster(clust, k, criterion='distance'))
]
solutions[k] = {'out': clusters}
solutions[k]['score'] = calinski_harabasz(scores, clusters)
try:
# take best cluster according to calinski_harabasz score
clusters = [
solutions[s]
for s in sorted(solutions, key=lambda x: solutions[x]['score'])
if solutions[s]['score'] > 0
][-1]['out']
except IndexError:
#warn('WARNING: compartment clustering is not clear. Skipping')
return (0, 0, 0, 0)
if len(clusters) != 2:
#warn('WARNING: compartment clustering is too clear. Skipping')
return (0, 0, 0, 0)
# labelling compartments. A compartments shall have lower
# mean intra-interactions
dens = {}
for k in clusters:
val = sum([cmprtsec[c]['dens']
for c in clusters[k]]) / len(clusters[k])
dens['A' if val < 1 else 'B'] = [
cmprtsec[c]['dens'] for c in clusters[k]
if cmprtsec[c]['end'] - cmprtsec[c]['start'] > 2
]
if save:
for c in clusters[k]:
cmprtsec[c]['type'] = 'A' if val < 1 else 'B'
try:
tt, pval = ttest_ind(dens['A'], dens['B'])
except ZeroDivisionError:
return (0, 0, 0, 0)
prop = float(len(dens['A'])) / (len(dens['A']) + len(dens['B']))
score = 5000 * (prop - 0.5)**4 - 2
if verbose:
print 'g:%5s %5s%% pen:%7s tt:%7s score:%7s pv:%s' % (
gamma - 1, round(prop * 100, 1), round(score, 3), round(
tt, 3), round(score + tt, 3), pval)
return score + tt, tt, prop
