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)
samtools = which(opts.samtools)
launch_time = time.localtime()
param_hash = digest_parameters(opts)
reso1 = reso2 = None
if opts.bam1:
mreads1 = path.realpath(opts.bam1)
biases1 = opts.biases1
else:
biases1, mreads1, reso1 = load_parameters_fromdb(
opts.workdir1, opts.jobid1, opts, opts.tmpdb1)
mreads1 = path.join(opts.workdir1, mreads1)
try:
biases1 = path.join(opts.workdir1, biases1)
except AttributeError:
biases1 = None
except TypeError: # Py3
biases1 = None
if opts.bam2:
mreads2 = path.realpath(opts.bam2)
biases2 = opts.biases2
else:
biases2, mreads2, reso2 = load_parameters_fromdb(
opts.workdir2, opts.jobid2, opts, opts.tmpdb2)
mreads2 = path.join(opts.workdir2, mreads2)
try:
biases2 = path.join(opts.workdir2, biases2)
except AttributeError:
biases2 = None
except TypeError: # Py3
biases1 = None
filter_exclude = opts.filter
if reso1 != reso2:
raise Exception('ERROR: differing resolutions between experiments to '
'be merged')
mkdir(path.join(opts.workdir, '00_merge'))
if not opts.skip_comparison:
printime(' - loading first sample %s' % (mreads1))
hic_data1 = load_hic_data_from_bam(mreads1, opts.reso, biases=biases1,
tmpdir=path.join(opts.workdir, '00_merge'),
ncpus=opts.cpus,
filter_exclude=filter_exclude)
printime(' - loading second sample %s' % (mreads2))
hic_data2 = load_hic_data_from_bam(mreads2, opts.reso, biases=biases2,
tmpdir=path.join(opts.workdir, '00_merge'),
ncpus=opts.cpus,
filter_exclude=filter_exclude)
if opts.workdir1 and opts.workdir2:
masked1 = {'valid-pairs': {'count': 0}}
masked2 = {'valid-pairs': {'count': 0}}
else:
masked1 = {'valid-pairs': {'count': sum(hic_data1.values())}}
masked2 = {'valid-pairs': {'count': sum(hic_data2.values())}}
decay_corr_dat = path.join(opts.workdir, '00_merge', 'decay_corr_dat_%s_%s.txt' % (opts.reso, param_hash))
decay_corr_fig = path.join(opts.workdir, '00_merge', 'decay_corr_dat_%s_%s.png' % (opts.reso, param_hash))
eigen_corr_dat = path.join(opts.workdir, '00_merge', 'eigen_corr_dat_%s_%s.txt' % (opts.reso, param_hash))
eigen_corr_fig = path.join(opts.workdir, '00_merge', 'eigen_corr_dat_%s_%s.png' % (opts.reso, param_hash))
printime(' - comparing experiments')
printime(' => correlation between equidistant loci')
corr, _, scc, std, bads = correlate_matrices(
hic_data1, hic_data2, normalized=opts.norm,
remove_bad_columns=True, savefig=decay_corr_fig,
savedata=decay_corr_dat, get_bads=True)
print(' - correlation score (SCC): %.4f (+- %.7f)' % (scc, std))
printime(' => correlation between eigenvectors')
eig_corr = eig_correlate_matrices(hic_data1, hic_data2, normalized=opts.norm,
remove_bad_columns=True, nvect=6,
savefig=eigen_corr_fig,
savedata=eigen_corr_dat)
printime(' => reproducibility score')
reprod = get_reproducibility(hic_data1, hic_data2, num_evec=20, normalized=opts.norm,
verbose=False, remove_bad_columns=True)
print(' - reproducibility score: %.4f' % (reprod))
ncols = len(hic_data1)
else:
ncols = 0
decay_corr_dat = 'None'
decay_corr_fig = 'None'
eigen_corr_dat = 'None'
eigen_corr_fig = 'None'
masked1 = {}
masked2 = {}
corr = eig_corr = scc = std = reprod = 0
bads = {}
# merge inputs
mkdir(path.join(opts.workdir, '03_filtered_reads'))
outbam = path.join(opts.workdir, '03_filtered_reads',
'intersection_%s.bam' % (param_hash))
if not opts.skip_merge:
outbam = path.join(opts.workdir, '03_filtered_reads',
'intersection_%s.bam' % (param_hash))
printime(' - Mergeing experiments')
system(samtools + ' merge -@ %d %s %s %s' % (opts.cpus, outbam, mreads1, mreads2))
printime(' - Indexing new BAM file')
# check samtools version number and modify command line
version = LooseVersion([l.split()[1]
for l in Popen(samtools, stderr=PIPE,
universal_newlines=True).communicate()[1].split('\n')
if 'Version' in l][0])
if version >= LooseVersion('1.3.1'):
system(samtools + ' index -@ %d %s' % (opts.cpus, outbam))
else:
system(samtools + ' index %s' % (outbam))
else:
outbam = ''
finish_time = time.localtime()
save_to_db (opts, mreads1, mreads2, decay_corr_dat, decay_corr_fig,
len(list(bads.keys())), ncols, scc, std, reprod,
eigen_corr_dat, eigen_corr_fig, outbam, corr, eig_corr,
biases1, biases2, masked1, masked2, launch_time, finish_time)
printime('\nDone.')
def run(opts):
check_options(opts)
launch_time = time.localtime()
param_hash = digest_parameters(opts)
reso1 = reso2 = None
if opts.bed1:
mreads1 = path.realpath(opts.bed1)
bad_co1 = opts.bad_co1
biases1 = opts.biases1
else:
bad_co1, biases1, mreads1, reso1 = load_parameters_fromdb(
opts.workdir1, opts.jobid1, opts, opts.tmpdb1)
mreads1 = path.join(opts.workdir1, mreads1)
if opts.bed2:
mreads2 = path.realpath(opts.bed2)
bad_co2 = opts.bad_co2
biases2 = opts.biases2
else:
bad_co2, biases2, mreads2, reso2 = load_parameters_fromdb(
opts.workdir2, opts.jobid2, opts, opts.tmpdb2)
mreads2 = path.join(opts.workdir2, mreads2)
if reso1 != reso2:
raise Exception('ERROR: differing resolutions between experiments to '
'be merged')
mkdir(path.join(opts.workdir, '00_merge'))
if not opts.skip_comparison:
print 'Comparison'
print ' - loading first sample', mreads1
hic_data1 = load_hic_data_from_reads(mreads1, opts.reso)
print ' - loading second sample', mreads2
hic_data2 = load_hic_data_from_reads(mreads2, opts.reso)
if opts.norm and biases1:
bad_co1 = path.join(opts.workdir1, bad_co1)
print ' - loading bad columns from first sample', bad_co1
hic_data1.bads = dict(
(int(l.strip()), True) for l in open(bad_co1))
biases1 = path.join(opts.workdir1, biases1)
print ' - loading biases from first sample', biases1
hic_data1.bias = dict((int(l.split()[0]), float(l.split()[1]))
for l in open(biases1))
elif opts.norm:
raise Exception('ERROR: biases or filtered-columns not found')
if opts.norm and biases2:
bad_co2 = path.join(opts.workdir2, bad_co2)
print ' - loading bad columns from second sample', bad_co2
hic_data2.bads = dict(
(int(l.strip()), True) for l in open(bad_co2))
biases2 = path.join(opts.workdir2, biases2)
print ' - loading biases from second sample', biases2
hic_data2.bias = dict((int(l.split()[0]), float(l.split()[1]))
for l in open(biases2))
elif opts.norm:
raise Exception('ERROR: biases or filtered-columns not found')
decay_corr_dat = path.join(
opts.workdir, '00_merge',
'decay_corr_dat_%s_%s.txt' % (opts.reso, param_hash))
decay_corr_fig = path.join(
opts.workdir, '00_merge',
'decay_corr_dat_%s_%s.png' % (opts.reso, param_hash))
eigen_corr_dat = path.join(
opts.workdir, '00_merge',
'eigen_corr_dat_%s_%s.txt' % (opts.reso, param_hash))
eigen_corr_fig = path.join(
opts.workdir, '00_merge',
'eigen_corr_dat_%s_%s.png' % (opts.reso, param_hash))
else:
hic_data1 = {}
hic_data2 = {}
decay_corr_dat = 'None'
decay_corr_fig = 'None'
eigen_corr_dat = 'None'
eigen_corr_fig = 'None'
# if opts.norm:
# has bias file
if not opts.skip_comparison:
print ' => correlation between equidistant loci'
corr, _, bads = correlate_matrices(hic_data1,
hic_data2,
normalized=opts.norm,
remove_bad_columns=True,
savefig=decay_corr_fig,
savedata=decay_corr_dat,
get_bads=True)
print ' => correlation between eigenvectors'
eig_corr = eig_correlate_matrices(hic_data1,
hic_data2,
normalized=opts.norm,
remove_bad_columns=True,
nvect=6,
savefig=eigen_corr_fig,
savedata=eigen_corr_dat)
else:
corr = eig_corr = 0
bads = {}
# merge inputs
mkdir(path.join(opts.workdir, '03_filtered_reads'))
outbed = path.join(opts.workdir, '03_filtered_reads',
'valid_r1-r2_intersection_%s.tsv' % (param_hash))
print '\nMergeing...'
nreads = merge_2d_beds(mreads1, mreads2, outbed)
finish_time = time.localtime()
save_to_db(opts, mreads1, mreads2, decay_corr_dat, decay_corr_fig,
len(bads.keys()), len(hic_data1), nreads, eigen_corr_dat,
eigen_corr_fig, outbed, corr, eig_corr, biases1, bad_co1,
biases2, bad_co2, launch_time, finish_time)
print '\n\nDone.'
def run(opts):
check_options(opts)
samtools = which(opts.samtools)
launch_time = time.localtime()
param_hash = digest_parameters(opts)
reso1 = reso2 = None
if opts.bam1:
mreads1 = path.realpath(opts.bam1)
biases1 = opts.biases1
else:
biases1, mreads1, reso1 = load_parameters_fromdb(
opts.workdir1, opts.jobid1, opts, opts.tmpdb1)
mreads1 = path.join(opts.workdir1, mreads1)
try:
biases1 = path.join(opts.workdir1, biases1)
except AttributeError:
biases1 = None
if opts.bam2:
mreads2 = path.realpath(opts.bam2)
biases2 = opts.biases2
else:
biases2, mreads2, reso2 = load_parameters_fromdb(
opts.workdir2, opts.jobid2, opts, opts.tmpdb2)
mreads2 = path.join(opts.workdir2, mreads2)
try:
biases2 = path.join(opts.workdir2, biases2)
except AttributeError:
biases2 = None
filter_exclude = opts.filter
if reso1 != reso2:
raise Exception('ERROR: differing resolutions between experiments to '
'be merged')
mkdir(path.join(opts.workdir, '00_merge'))
if not opts.skip_comparison:
printime(' - loading first sample %s' % (mreads1))
hic_data1 = load_hic_data_from_bam(mreads1, opts.reso, biases=biases1,
tmpdir=path.join(opts.workdir, '00_merge'),
ncpus=opts.cpus,
filter_exclude=filter_exclude)
printime(' - loading second sample %s' % (mreads2))
hic_data2 = load_hic_data_from_bam(mreads2, opts.reso, biases=biases2,
tmpdir=path.join(opts.workdir, '00_merge'),
ncpus=opts.cpus,
filter_exclude=filter_exclude)
decay_corr_dat = path.join(opts.workdir, '00_merge', 'decay_corr_dat_%s_%s.txt' % (opts.reso, param_hash))
decay_corr_fig = path.join(opts.workdir, '00_merge', 'decay_corr_dat_%s_%s.png' % (opts.reso, param_hash))
eigen_corr_dat = path.join(opts.workdir, '00_merge', 'eigen_corr_dat_%s_%s.txt' % (opts.reso, param_hash))
eigen_corr_fig = path.join(opts.workdir, '00_merge', 'eigen_corr_dat_%s_%s.png' % (opts.reso, param_hash))
printime(' - comparing experiments')
printime(' => correlation between equidistant loci')
corr, _, scc, std, bads = correlate_matrices(
hic_data1, hic_data2, normalized=opts.norm,
remove_bad_columns=True, savefig=decay_corr_fig,
savedata=decay_corr_dat, get_bads=True)
print ' - correlation score (SCC): %.4f (+- %.7f)' % (scc, std)
printime(' => correlation between eigenvectors')
eig_corr = eig_correlate_matrices(hic_data1, hic_data2, normalized=opts.norm,
remove_bad_columns=True, nvect=6,
savefig=eigen_corr_fig,
savedata=eigen_corr_dat)
printime(' => reproducibility score')
reprod = get_reproducibility(hic_data1, hic_data2, num_evec=20, normalized=opts.norm,
verbose=False, remove_bad_columns=True)
print ' - reproducibility score: %.4f' % (reprod)
ncols = len(hic_data1)
else:
ncols = 0
decay_corr_dat = 'None'
decay_corr_fig = 'None'
eigen_corr_dat = 'None'
eigen_corr_fig = 'None'
corr = eig_corr = 0
bads = {}
# merge inputs
mkdir(path.join(opts.workdir, '03_filtered_reads'))
outbam = path.join(opts.workdir, '03_filtered_reads',
'intersection_%s.bam' % (param_hash))
printime(' - Mergeing experiments')
system(samtools + ' merge -@ %d %s %s %s' % (opts.cpus, outbam, mreads1, mreads2))
printime(' - Indexing new BAM file')
# check samtools version number and modify command line
version = LooseVersion([l.split()[1]
for l in Popen(samtools, stderr=PIPE).communicate()[1].split('\n')
if 'Version' in l][0])
if version >= LooseVersion('1.3.1'):
system(samtools + ' index -@ %d %s' % (opts.cpus, outbam))
else:
system(samtools + ' index %s' % (outbam))
finish_time = time.localtime()
save_to_db (opts, mreads1, mreads2, decay_corr_dat, decay_corr_fig,
len(bads.keys()), ncols, scc, std, reprod,
eigen_corr_dat, eigen_corr_fig, outbam, corr, eig_corr,
biases1, biases2, launch_time, finish_time)
printime('\nDone.')
def run(opts):
check_options(opts)
launch_time = time.localtime()
param_hash = digest_parameters(opts)
reso1 = reso2 = None
if opts.bed1:
mreads1 = path.realpath(opts.bed1)
bad_co1 = opts.bad_co1
biases1 = opts.biases1
else:
bad_co1, biases1, mreads1, reso1 = load_parameters_fromdb(
opts.workdir1, opts.jobid1, opts, opts.tmpdb1)
mreads1 = path.join(opts.workdir1, mreads1)
if opts.bed2:
mreads2 = path.realpath(opts.bed2)
bad_co2 = opts.bad_co2
biases2 = opts.biases2
else:
bad_co2, biases2, mreads2, reso2 = load_parameters_fromdb(
opts.workdir2, opts.jobid2, opts, opts.tmpdb2)
mreads2 = path.join(opts.workdir2, mreads2)
if reso1 != reso2:
raise Exception('ERROR: differing resolutions between experiments to '
'be merged')
print 'loading first sample', mreads1
hic_data1 = load_hic_data_from_reads(mreads1, opts.reso)
print 'loading second sample', mreads2
hic_data2 = load_hic_data_from_reads(mreads2, opts.reso)
if opts.norm and biases1:
bad_co1 = path.join(opts.workdir1, bad_co1)
print 'loading bad columns from first sample', bad_co1
hic_data1.bads = dict((int(l.strip()), True) for l in open(bad_co1))
biases1 = path.join(opts.workdir1, biases1)
print 'loading biases from first sample', biases1
hic_data1.bias = dict((int(l.split()[0]), float(l.split()[1]))
for l in open(biases1))
elif opts.norm:
raise Exception('ERROR: biases or filtered-columns not found')
if opts.norm and biases2:
bad_co2 = path.join(opts.workdir2, bad_co2)
print 'loading bad columns from second sample', bad_co2
hic_data2.bads = dict((int(l.strip()), True) for l in open(bad_co2))
biases2 = path.join(opts.workdir2, biases2)
print 'loading biases from second sample', biases2
hic_data2.bias = dict((int(l.split()[0]), float(l.split()[1]))
for l in open(biases2))
elif opts.norm:
raise Exception('ERROR: biases or filtered-columns not found')
mkdir(path.join(opts.workdir, '00_merge'))
if not opts.skip_comparison:
decay_corr_dat = path.join(opts.workdir, '00_merge', 'decay_corr_dat_%s_%s.txt' % (opts.reso, param_hash))
decay_corr_fig = path.join(opts.workdir, '00_merge', 'decay_corr_dat_%s_%s.png' % (opts.reso, param_hash))
eigen_corr_dat = path.join(opts.workdir, '00_merge', 'eigen_corr_dat_%s_%s.txt' % (opts.reso, param_hash))
eigen_corr_fig = path.join(opts.workdir, '00_merge', 'eigen_corr_dat_%s_%s.png' % (opts.reso, param_hash))
else:
decay_corr_dat = 'None'
decay_corr_fig = 'None'
eigen_corr_dat = 'None'
eigen_corr_fig = 'None'
# if opts.norm:
# has bias file
if not opts.skip_comparison:
print 'correlation between equidistant loci'
corr, _, bads = correlate_matrices(hic_data1, hic_data2, normalized=opts.norm,
remove_bad_columns=True,
savefig=decay_corr_fig,
savedata=decay_corr_dat, get_bads=True)
print 'correlation between eigenvectors'
eig_corr = eig_correlate_matrices(hic_data1, hic_data2, normalized=opts.norm,
remove_bad_columns=True, nvect=6,
savefig=eigen_corr_fig,
savedata=eigen_corr_dat)
else:
corr = eig_corr = None
bads = {}
# merge inputs
mkdir(path.join(opts.workdir, '03_filtered_reads'))
outbed = path.join(opts.workdir, '03_filtered_reads', 'valid_r1-r2_intersection_%s.tsv' % (
param_hash))
nreads = merge_2d_beds(mreads1, mreads2, outbed)
finish_time = time.localtime()
save_to_db (opts, mreads1, mreads2, decay_corr_dat, decay_corr_fig,
len(bads.keys()), len(hic_data1), nreads,
eigen_corr_dat, eigen_corr_fig, outbed, corr, eig_corr,
biases1, bad_co1, biases2, bad_co2, launch_time, finish_time)
