def run(opts):
check_options(opts)
launch_time = time.localtime()
fname1, fname2 = load_parameters_fromdb(opts)
param_hash = digest_parameters(opts)
reads = path.join(opts.workdir, '03_filtered_reads',
'all_r1-r2_intersection_%s.tsv' % param_hash)
mreads = path.join(opts.workdir, '03_filtered_reads',
'valid_r1-r2_intersection_%s.tsv' % param_hash)
if not opts.resume:
mkdir(path.join(opts.workdir, '03_filtered_reads'))
# compute the intersection of the two read ends
print 'Getting intersection between read 1 and read 2'
count, multiples = get_intersection(fname1, fname2, reads)
# compute insert size
print 'Get insert size...'
hist_path = path.join(opts.workdir,
'histogram_fragment_sizes_%s.pdf' % param_hash)
median, max_f, mad = insert_sizes(
reads, nreads=1000000, stats=('median', 'first_decay', 'MAD'),
savefig=hist_path)
print ' - median insert size =', median
print ' - double median absolution of insert size =', mad
print ' - max insert size (when a gap in continuity of > 10 bp is found in fragment lengths) =', max_f
max_mole = max_f # pseudo DEs
min_dist = max_f + mad # random breaks
print (' Using the maximum continuous fragment size'
'(%d bp) to check '
'for pseudo-dangling ends') % max_mole
print (' Using maximum continuous fragment size plus the MAD '
'(%d bp) to check for random breaks') % min_dist
print "identify pairs to filter..."
masked = filter_reads(reads, max_molecule_length=max_mole,
over_represented=opts.over_represented,
max_frag_size=opts.max_frag_size,
min_frag_size=opts.min_frag_size,
re_proximity=opts.re_proximity,
min_dist_to_re=min_dist, fast=True)
n_valid_pairs = apply_filter(reads, mreads, masked,
filters=opts.apply)
finish_time = time.localtime()
print median, max_f, mad
# save all job information to sqlite DB
save_to_db(opts, count, multiples, reads, mreads, n_valid_pairs, masked,
hist_path, median, max_f, mad, launch_time, finish_time)
def test_19_matrix_manip(self):
if ONLY and not "19" in ONLY:
return
if CHKTIME:
t0 = time()
hic_data1 = load_hic_data_from_reads("lala-map~", resolution=10000)
hic_map(hic_data1, savedata="lala-map.tsv~", savefig="lala.pdf")
hic_map(hic_data1,
by_chrom="intra",
savedata="lala-maps~",
savefig="lalalo~")
hic_map(hic_data1,
by_chrom="inter",
savedata="lala-maps~",
savefig="lalala~")
# slowest part of the all test:
hic_data2 = read_matrix("lala-map.tsv~", resolution=10000)
self.assertEqual(hic_data1, hic_data2)
# vals = plot_distance_vs_interactions(hic_data1)
# self.assertEqual([round(i, 2) if str(i)!="nan" else 0.0 for i in
# reduce(lambda x, y: x + y, vals)],
# [-1.68, -2.08, 0.02, 2.76, -8.99, 0.0, 0.82, -6.8, 0.0])
a, b = insert_sizes("lala-map~")
self.assertEqual([int(a), int(b)], [43, 1033])
hic_data1 = read_matrix(PATH + "/20Kb/chrT/chrT_A.tsv",
resolution=20000)
hic_data2 = read_matrix(PATH + "/20Kb/chrT/chrT_B.tsv",
resolution=20000)
corr = correlate_matrices(hic_data1, hic_data2)
corr = [round(i, 3) for i in corr[0]]
self.assertEqual(corr, [
0.755, 0.729, 0.804, 0.761, 0.789, 0.776, 0.828, 0.757, 0.797,
0.832
])
ecorr = eig_correlate_matrices(hic_data1,
hic_data2,
savefig='lala3.pdf')
ecorr = [round(i, 3) for i in reduce(lambda x, y: x + y, ecorr)]
self.assertEqual(ecorr, [
0.997, 0.322, 0.442, 0.017, 0.243, 0.014, 0.321, 0.999, 0.01,
0.006, 0.0, 0.007, 0.451, 0.012, 0.996, 0.031, 0.013, 0.004, 0.002,
0.006, 0.029, 0.974, 0.076, 0.03, 0.219, 0.013, 0.031, 0.08, 0.974,
0.018, 0.028, 0.004, 0.0, 0.028, 0.034, 0.89
])
system("rm -rf lala*")
if CHKTIME:
self.assertEqual(True, True)
print "19", time() - t0
def test_19_matrix_manip(self):
if ONLY and ONLY != '19':
return
if CHKTIME:
t0 = time()
hic_data1 = load_hic_data_from_reads('lala-map~', resolution=10000)
hic_map(hic_data1, savedata='lala-map.tsv~', savefig='lala.pdf~')
hic_map(hic_data1,
by_chrom='intra',
savedata='lala-maps~',
savefig='lalalo~')
hic_map(hic_data1,
by_chrom='inter',
savedata='lala-maps~',
savefig='lalala~')
# slowest part of the all test:
hic_data2 = read_matrix('lala-map.tsv~', resolution=10000)
self.assertEqual(hic_data1, hic_data2)
vals = plot_distance_vs_interactions(hic_data1)
self.assertEqual([
round(i, 2) if str(i) != 'nan' else 0.0
for i in reduce(lambda x, y: x + y, vals)
], [-1.68, -2.08, 0.02, 2.76, -8.99, 0.0, 0.82, -6.8, 0.0])
a, b = insert_sizes('lala-map~')
self.assertEqual([int(a), int(b)], [43, 1033])
hic_data1 = read_matrix('20Kb/chrT/chrT_A.tsv', resolution=20000)
hic_data2 = read_matrix('20Kb/chrT/chrT_B.tsv', resolution=20000)
corr = correlate_matrices(hic_data1, hic_data2)
corr = [round(i, 3) for i in corr[0]]
self.assertEqual(corr, [
0.755, 0.729, 0.804, 0.761, 0.789, 0.776, 0.828, 0.757, 0.797,
0.832
])
ecorr = eig_correlate_matrices(hic_data1, hic_data2)
ecorr = [round(i, 3) for i in reduce(lambda x, y: x + y, ecorr)]
self.assertEqual(ecorr, [
0.997, 0.322, 0.442, 0.017, 0.243, 0.014, 0.321, 0.999, 0.01,
0.006, 0.0, 0.007, 0.451, 0.012, 0.996, 0.031, 0.013, 0.004, 0.002,
0.006, 0.029, 0.974, 0.076, 0.03, 0.219, 0.013, 0.031, 0.08, 0.974,
0.018, 0.028, 0.004, 0.0, 0.028, 0.034, 0.89
])
system('rm -rf lala*')
if CHKTIME:
self.assertEqual(True, True)
print '19', time() - t0
def test_19_matrix_manip(self):
if ONLY and ONLY != '19':
return
if CHKTIME:
t0 = time()
hic_data1 = load_hic_data_from_reads('lala-map~', resolution=10000)
hic_map(hic_data1, savedata='lala-map.tsv~', savefig='lala.pdf~')
hic_map(hic_data1, by_chrom='intra', savedata='lala-maps~', savefig='lalalo~')
hic_map(hic_data1, by_chrom='inter', savedata='lala-maps~', savefig='lalala~')
# slowest part of the all test:
hic_data2 = read_matrix('lala-map.tsv~', resolution=10000)
self.assertEqual(hic_data1, hic_data2)
vals = plot_distance_vs_interactions(hic_data1)
self.assertEqual([round(i, 2) if str(i)!='nan' else 0.0 for i in
reduce(lambda x, y: x + y, vals)],
[-1.74, 4.2, 0.52, 1.82, -0.44, 0.0, -0.5, 2.95, 0.0])
a, b = insert_sizes('lala-map~')
self.assertEqual([int(a),int(b)], [43, 1033])
hic_data1 = read_matrix('20Kb/chrT/chrT_A.tsv', resolution=20000)
hic_data2 = read_matrix('20Kb/chrT/chrT_B.tsv', resolution=20000)
corr = correlate_matrices(hic_data1, hic_data2)
corr = [round(i,3) for i in corr[0]]
self.assertEqual(corr, [0.755, 0.729, 0.804, 0.761, 0.789, 0.776, 0.828,
0.757, 0.797, 0.832])
ecorr = eig_correlate_matrices(hic_data1, hic_data2)
ecorr = [round(i,3) for i in reduce(lambda x, y:x+y, ecorr)]
self.assertEqual(ecorr, [0.997, 0.322, 0.442, 0.017, 0.243, 0.014,
0.321, 0.999, 0.01, 0.006, 0.0, 0.007, 0.451,
0.012, 0.996, 0.031, 0.013, 0.004, 0.002,
0.006, 0.029, 0.974, 0.076, 0.03, 0.219, 0.013,
0.031, 0.08, 0.974, 0.018, 0.028, 0.004, 0.0,
0.028, 0.034, 0.89])
system('rm -rf lala*')
if CHKTIME:
self.assertEqual(True, True)
print '19', time() - t0
def run(opts):
check_options(opts)
launch_time = time.localtime()
fname1, fname2 = load_parameters_fromdb(opts)
param_hash = digest_parameters(opts)
reads = path.join(opts.workdir, '03_filtered_reads',
'all_r1-r2_intersection_%s.tsv' % param_hash)
mreads = path.join(opts.workdir, '03_filtered_reads',
'valid_r1-r2_intersection_%s.tsv' % param_hash)
if not opts.resume:
mkdir(path.join(opts.workdir, '03_filtered_reads'))
# compute the intersection of the two read ends
print 'Getting intersection between read 1 and read 2'
count, multiples = get_intersection(fname1, fname2, reads)
# compute insert size
print 'Get insert size...'
hist_path = path.join(opts.workdir,
'histogram_fragment_sizes_%s.pdf' % param_hash)
median, max_f, mad = insert_sizes(reads,
nreads=1000000,
stats=('median', 'first_decay',
'MAD'),
savefig=hist_path)
print ' - median insert size =', median
print ' - double median absolution of insert size =', mad
print ' - max insert size (when a gap in continuity of > 10 bp is found in fragment lengths) =', max_f
max_mole = max_f # pseudo DEs
min_dist = max_f + mad # random breaks
print(
' Using the maximum continuous fragment size'
'(%d bp) to check '
'for pseudo-dangling ends') % max_mole
print(
' Using maximum continuous fragment size plus the MAD '
'(%d bp) to check for random breaks') % min_dist
print "identify pairs to filter..."
masked = filter_reads(reads,
max_molecule_length=max_mole,
over_represented=0.001,
max_frag_size=100000,
min_frag_size=50,
re_proximity=5,
min_dist_to_re=min_dist,
fast=True)
n_valid_pairs = apply_filter(reads, mreads, masked, filters=opts.apply)
finish_time = time.localtime()
print median, max_f, mad
# save all job information to sqlite DB
save_to_db(opts, count, multiples, reads, mreads, n_valid_pairs, masked,
hist_path, median, max_f, mad, launch_time, finish_time)
savefig=outfile)
reads_mapped_per_iteration = pd.DataFrame.from_dict(reads_mapped_per_iteration)
reads_mapped_per_iteration.columns = ['read1', 'read2']
fraction_mapped_read1 = list(
reads_mapped_per_iteration['read1'])[-1] / float(n_reads_trimmed)
fraction_mapped_read2 = list(
reads_mapped_per_iteration['read2'])[-1] / float(n_reads_trimmed)
fraction_mapped_str = ",".join(
[str(i) for i in [fraction_mapped_read1, fraction_mapped_read2]])
# Plot: distribution of dangling-end lengths
plt.rcParams['font.size'] = 12
infile = '%s/%s_both_map.tsv' % (PROCESSED, pair_id)
outfile = '%s/%s_plot_distribution_dangling_ends_lengths.png' % (
POSTMAPPING_PLOTS, pair_id)
insert_sizes(infile, xlog=False, max_size=99.9, savefig=outfile)
# Plot: Decay of interaction counts with genomic distamce
plt.rcParams['font.size'] = 12
outfile = '%s/%s_plot_decay_interaction_counts_genomic_distance.png' % (
POSTMAPPING_PLOTS, pair_id)
myvalues = plot_distance_vs_interactions(infile,
max_diff=50000000,
resolution=10000,
savefig=outfile)
slope = str(myvalues[1][0])
# Plot: sequencing coverage along chromosomes
outfile = '%s/%s_plot_genomic_coverage_mapped_%s.png' % (
POSTMAPPING_PLOTS, pair_id, genomic_coverage_resolution)
plt.rcParams['font.size'] = 20
