def test_06_tad_clustering(self):
if ONLY and ONLY != "06":
return
if CHKTIME:
t0 = time()
test_chr = Chromosome(
name="Test Chromosome",
experiment_tads=[exp4],
experiment_names=["exp1"],
experiment_hic_data=[PATH + "/20Kb/chrT/chrT_D.tsv"],
experiment_resolutions=[20000, 20000],
silent=True,
)
all_tads = []
for _, tad in test_chr.iter_tads("exp1", normed=False):
all_tads.append(tad)
# align1, align2, _ = optimal_cmo(all_tads[7], all_tads[10], 7,
# method='score')
align1, align2, _ = optimal_cmo(all_tads[1], all_tads[3], 7, method="score")
# Values with square root normalization.
# self.assertEqual(align1, [0, 1, '-', 2, 3, '-', 4, 5, 6, 7, 8, 9, 10])
# self.assertEqual(align2,[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12])
self.assertEqual(align1, [0, 1, 2, "-", "-", 3, 4, 5, 6, 7, 8, "-", 9])
self.assertEqual(align2, [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12])
if CHKTIME:
print "6", time() - t0
def test_06_tad_clustering(self):
if ONLY and not "06" in ONLY:
return
if CHKTIME:
t0 = time()
test_chr = Chromosome(
name="Test Chromosome",
experiment_tads=[exp4],
experiment_names=["exp1"],
experiment_hic_data=[PATH + "/20Kb/chrT/chrT_D.tsv"],
experiment_resolutions=[20000, 20000],
silent=True)
all_tads = []
for _, tad in test_chr.iter_tads("exp1", normed=False):
all_tads.append(tad)
#align1, align2, _ = optimal_cmo(all_tads[7], all_tads[10], 7,
# method="score")
align1, align2, _ = optimal_cmo(all_tads[1],
all_tads[3],
7,
method="score")
# Values with square root normalization.
#self.assertEqual(align1, [0, 1, "-", 2, 3, "-", 4, 5, 6, 7, 8, 9, 10])
#self.assertEqual(align2,[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12])
self.assertEqual(align1, [0, 1, 2, "-", "-", 3, 4, 5, 6, 7, 8, "-", 9])
self.assertEqual(align2, [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12])
if CHKTIME:
print "6", time() - t0
def test_06_tad_clustering(self):
if ONLY and ONLY != '06':
return
if CHKTIME:
t0 = time()
test_chr = Chromosome(name='Test Chromosome',
experiment_tads=[exp4],
experiment_names=['exp1'],
experiment_hic_data=[
PATH + '/20Kb/chrT/chrT_D.tsv'],
experiment_resolutions=[20000,20000],
silent=True)
all_tads = []
for _, tad in test_chr.iter_tads('exp1', normed=False):
all_tads.append(tad)
#align1, align2, _ = optimal_cmo(all_tads[7], all_tads[10], 7,
# method='score')
align1, align2, _ = optimal_cmo(all_tads[1], all_tads[3], 7,
method='score')
# Values with square root normalization.
#self.assertEqual(align1, [0, 1, '-', 2, 3, '-', 4, 5, 6, 7, 8, 9, 10])
#self.assertEqual(align2,[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12])
self.assertEqual(align1, [0, 1, 2, '-', '-', 3, 4, 5, 6, 7, 8, '-', 9])
self.assertEqual(align2, [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12])
if CHKTIME:
print '6', time() - t0
def test_06_tad_clustering(self):
if CHKTIME:
t0 = time()
test_chr = Chromosome(name='Test Chromosome',
experiment_tads=[exp4],
experiment_names=['exp1'],
experiment_hic_data=[
PATH + '/20Kb/chrT/chrT_D.tsv'],
experiment_resolutions=[20000,20000],
silent=True)
all_tads = []
for _, tad in test_chr.iter_tads('exp1'):
all_tads.append(tad)
#align1, align2, _ = optimal_cmo(all_tads[7], all_tads[10], 7,
# method='score')
align1, align2, _ = optimal_cmo(all_tads[1], all_tads[3], 7,
method='score')
# Values with square root normalization.
#self.assertEqual(align1, [0, 1, '-', 2, 3, '-', 4, 5, 6, 7, 8, 9, 10])
#self.assertEqual(align2,[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12])
self.assertEqual(align1, [0, 1, 2, '-', '-', 3, 4, 5, 6, 7, 8, '-', 9])
self.assertEqual(align2, [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12])
if CHKTIME:
print '6', time() - t0
def tad_clustering(self):
test_chr = Chromosome(name="Test Chromosome", resolution=20000)
test_chr.add_experiment("chrT/chrT_A.tsv", name="exp1")
test_chr.find_TAD(["exp1"])
all_tads = list(test_chr.iter_tads("exp1"))
align1, align2 = optimal_cmo(all_tads[4], all_tads[8], 9)
self.assertEqual(align1, [1, 2, "-", "-", "-", "-", 3, "-", 4, 5, 6, "-", 7, 8])
self.assertEqual(align2, [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15])
def test_06_tad_clustering(self):
test_chr = Chromosome(name='Test Chromosome',
experiment_tads=[exp4],
experiment_names=['exp1'],
experiment_hic_data=['20Kb/chrT/chrT_D.tsv'],
experiment_resolutions=[20000,20000])
all_tads = []
for _, tad in test_chr.iter_tads('exp1'):
all_tads.append(tad)
align1, align2, _ = optimal_cmo(all_tads[7], all_tads[10], 7,
method='score')
self.assertEqual(align1, [0, 1, '-', 2, 3, '-', 4, 5, 6, 7, 8, 9, 10])
self.assertEqual(align2,[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12])
def main():
"""
main function
"""
n_pick = 4
n_tot = 10
test_chr = Chromosome(name='Test Chromosome')
test_chr.add_experiment('exp1', 100000, xp_handler=PATH +
'HIC_gm06690_chr19_chr19_100000_obs.txt')
test_chr.find_tad(['exp1'])
real_tads = {}
for i, t in enumerate(test_chr.iter_tads('exp1', normed=False)):
real_tads[i] = test_chr.experiments['exp1'].tads[i]
real_tads[i]['hic'] = t[1]
global DISTRA
global DISTRD
DISTRA, DISTRD = get_hic_distr(real_tads)
# pick some tads
picked_tads = []
picked_keys = []
for i in xrange(n_pick):
key, new_tad = get_random_tad(real_tads)
while key in picked_keys or (new_tad['end'] - new_tad['start'] < 15):
key, new_tad = get_random_tad(real_tads)
picked_tads.append(new_tad)
picked_keys.append(key)
# mutate this tads
tads = {}
tad_matrices = []
tad_names = []
for i in xrange(n_pick):
print i
tads[uppercase[i] + '_' + str(0)] = picked_tads[i]
tad_names.append(uppercase[i] + '_' + str(0))
for j in xrange(1, n_tot):
hic, indels = generate_random_contacts(
tad1=picked_tads[i]['hic'], prob=0.05, ext=int(random()*4) + 1,
indel=int(random() * 4) + 1)[1:]
# indels = '|'.join([str(n-1) if n>0 else '-' + str((abs(n)-1)) for n in indels])
tads[uppercase[i] + '_' + str(j)] = {
'hic' : hic,
'start': picked_tads[i]['start'],
'end' : picked_tads[i]['end']}
tad_matrices.append(hic)
tad_names.append(uppercase[i] + '_' + str(j))
distances, cci = get_distances(tad_matrices, max_num_v=4,
n_cpus=mu.cpu_count())
results, clusters = pre_cluster(distances, cci, len(tad_matrices))
paint_clustering(results, clusters, len(tad_matrices), test_chr,
tad_names, tad_matrices)
def main():
test_chr = Chromosome(name='Test Chromosome')
test_chr.add_experiment('exp1', 100000, xp_handler=PATH +
'HIC_k562_chr19_chr19_100000_obs.txt')
test_chr.find_tad(['exp1'])
tad_names = []
tad_matrices = []
for name, matrix in test_chr.iter_tads('exp1'):
if test_chr.experiments['exp1'].tads[name]['score'] < 0:
continue
if (test_chr.experiments['exp1'].tads[name]['end'] -
test_chr.experiments['exp1'].tads[name]['start']) < 10:
continue
tad_names.append(name)
tad_matrices.append(matrix)
num = len(tad_names)
distances, cci = get_distances(tad_matrices, max_num_v=mu.cpu_count())
results, clusters = pre_cluster(distances, cci, num)
paint_clustering(results, clusters, num, test_chr, tad_names)
def main():
test_chr = Chromosome(name='Test Chromosome')
test_chr.add_experiment('exp1',
100000,
xp_handler=PATH +
'HIC_k562_chr19_chr19_100000_obs.txt')
test_chr.find_tad(['exp1'])
tad_names = []
tad_matrices = []
for name, matrix in test_chr.iter_tads('exp1'):
if test_chr.experiments['exp1'].tads[name]['score'] < 0:
continue
if (test_chr.experiments['exp1'].tads[name]['end'] -
test_chr.experiments['exp1'].tads[name]['start']) < 10:
continue
tad_names.append(name)
tad_matrices.append(matrix)
num = len(tad_names)
distances, cci = get_distances(tad_matrices, max_num_v=mu.cpu_count())
results, clusters = pre_cluster(distances, cci, num)
paint_clustering(results, clusters, num, test_chr, tad_names)
ali.get_column(3)
cond1 = lambda x: x['score'] > 7
print ali.get_column(cond1=cond1)
cond2 = lambda x: x['pos'] > 50
print ali.get_column(cond1=cond1, cond2=cond2)
print ali.get_column(cond1=cond1, cond2=cond2, min_num=1)
exp = my_chrom.experiments[0]
tad1 = list(my_chrom.iter_tads('First Hi-C experiment'))[41]
tad2 = list(my_chrom.iter_tads('First Hi-C experiment'))[39]
from pytadbit.tad_clustering.tad_cmo import optimal_cmo
align1, align2, score = optimal_cmo(tad1[1], tad2[1], max_num_v=8, long_nw=True, long_dist=True, method='frobenius')
from pytadbit.tad_clustering.tad_cmo import merge_tads
matrix1, matrix2, matrix_merged = merge_tads(tad1[1], tad2[1], align1, align2)
from matplotlib import pyplot as plt
from numpy import log2
