def get_mapped_chunk(map_folder, nreads):
seqs = {}
printime(' - loading chunk')
pos_file = 0
for fname in os.listdir(map_folder):
printime(' - ' + fname)
fhandler = magic_open(os.path.join(map_folder, fname))
for line in fhandler:
pos_file += 1
rid, seq, qal, _, pos = line.split()
pos = int(pos.split(':')[2])
rid = rid.split('~')[0]
seqs[rid, pos] = (seq, qal)
if pos_file >= nreads:
yield seqs
printime(' - loading chunk')
seqs = {}
pos_file = 0
yield seqs
def read_bam(inbam, filter_exclude, resolution, min_count=2500, biases_path='',
normalization='Vanilla', mappability=None, n_rsites=None,
cg_content=None, sigma=2, ncpus=8, factor=1, outdir='.', seed=1,
extra_out='', only_valid=False, normalize_only=False, p_fit=None,
max_njobs=100, extra_bads=None,
cis_limit=1, trans_limit=5, min_ratio=1.0, fast_filter=False):
bamfile = AlignmentFile(inbam, 'rb')
sections = OrderedDict(list(zip(bamfile.references,
[x // resolution + 1 for x in bamfile.lengths])))
total = 0
section_pos = dict()
for crm in sections:
section_pos[crm] = (total, total + sections[crm])
total += sections[crm]
bins = []
for crm in sections:
len_crm = sections[crm]
bins.extend([(crm, i) for i in range(len_crm)])
start_bin = 0
end_bin = len(bins)
total = len(bins)
regs = []
begs = []
ends = []
njobs = min(total, max_njobs) + 1
nbins = total // njobs + 1
for i in range(start_bin, end_bin, nbins):
if i + nbins > end_bin: # make sure that we stop
nbins = end_bin - i
try:
(crm1, beg1), (crm2, end2) = bins[i], bins[i + nbins - 1]
except IndexError:
try:
(crm1, beg1), (crm2, end2) = bins[i], bins[-1]
except IndexError:
break
if crm1 != crm2:
end1 = sections[crm1]
beg2 = 0
regs.append(crm1)
regs.append(crm2)
begs.append(beg1 * resolution)
begs.append(beg2 * resolution)
ends.append(end1 * resolution + resolution) # last nt included
ends.append(end2 * resolution + resolution - 1) # last nt not included (overlap with next window)
else:
regs.append(crm1)
begs.append(beg1 * resolution)
ends.append(end2 * resolution + resolution - 1)
ends[-1] += 1 # last nucleotide included
# print '\n'.join(['%s %d %d' % (a, b, c) for a, b, c in zip(regs, begs, ends)])
printime(' - Parsing BAM (%d chunks)' % (len(regs)))
# define limits for cis and trans interactions if not given
if cis_limit is None:
cis_limit = int(1_000_000 / resolution)
print(' -> cis interactions are defined as being bellow {}'.format(
nicer(cis_limit * resolution)))
if trans_limit is None:
trans_limit = cis_limit * 5
print(' -> trans interactions are defined as being bellow {}'.format(
nicer(trans_limit * resolution)))
bins_dict = dict([(j, i) for i, j in enumerate(bins)])
pool = mu.Pool(ncpus)
procs = []
read_bam_frag = read_bam_frag_valid if only_valid else read_bam_frag_filter
for i, (region, start, end) in enumerate(zip(regs, begs, ends)):
procs.append(pool.apply_async(
read_bam_frag, args=(inbam, filter_exclude, bins, bins_dict,
resolution, outdir, extra_out,
region, start, end, cis_limit, trans_limit)))
pool.close()
print_progress(procs)
pool.join()
## COLLECT RESULTS
cisprc = {}
printime(' - Collecting cis and total interactions per bin (%d chunks)' % (len(regs)))
stdout.write(' ')
for countbin, (region, start, end) in enumerate(zip(regs, begs, ends)):
if not countbin % 10 and countbin:
stdout.write(' ')
if not countbin % 50 and countbin:
stdout.write(' %9s\n ' % ('%s/%s' % (countbin , len(regs))))
stdout.write('.')
stdout.flush()
fname = path.join(outdir,
'tmp_bins_%s:%d-%d_%s.pickle' % (region, start, end, extra_out))
tmp_cisprc = load(open(fname,'rb'))
system('rm -f %s' % fname)
cisprc.update(tmp_cisprc)
stdout.write('\n')
# get cis/trans ratio
for k in cisprc:
try:
cisprc[k][3] = cisprc[k][2] / cisprc[k][3]
except ZeroDivisionError:
cisprc[k][3] = 0
# BIN FILTERINGS
printime(' - Removing columns with too few or too much interactions')
# define filter for minimum interactions per bin
if not fast_filter:
if min_count is None:
min_count = nanpercentile(
[cisprc[k][2] for k in range(total)
if cisprc.get(k, [0, 0, 0, 0])[3] < min_ratio
and cisprc.get(k, [0, 0, 0, 0])[2] >= 1], 95) # harcoded parameter we are filtering
# out bins with no interactions in cis
print(' -> too few interactions defined as less than %9d '
'interactions' % (min_count))
badcol = dict((k, True) for k in range(total)
if cisprc.get(k, [0, 0, 0, 0])[3] < min_ratio
or cisprc[k][2] < min_count)
print(' -> removed %d columns of %d (%.1f%%)' % (
len(badcol), total, float(len(badcol)) / total * 100))
else:
# if len(bamfile.references) == 1 and min_count is None:
# raise Exception("ERROR: only one chromosome can't filter by "
# "cis-percentage, set min_count instead")
# elif min_count is None and len(bamfile.references) > 1:
# badcol = filter_by_cis_percentage(
# cisprc, sigma=sigma, verbose=True, min_perc=min_perc, max_perc=max_perc,
# size=total, savefig=None)
print(' -> too few interactions defined as less than %9d '
'interactions' % (min_count))
badcol = {}
countL = 0
countZ = 0
for c in range(total):
if cisprc.get(c, [0, 0, 0, 0])[1] < min_count:
badcol[c] = cisprc.get(c, [0, 0, 0, 0])[1]
countL += 1
if not c in cisprc:
countZ += 1
print(' -> removed %d columns (%d/%d null/high counts) of %d (%.1f%%)' % (
len(badcol), countZ, countL, total, float(len(badcol)) / total * 100))
# Plot
plot_filtering(dict((k, cisprc[k][2]) for k in cisprc),
dict((k, cisprc[k][3]) for k in cisprc), total, min_count, min_ratio,
path.join(outdir, 'filtering_summary_plot_{}_{}.png'.format(nicer(resolution, sep=''),
extra_out)),
base_position=0, next_position=cis_limit, last_position=trans_limit, resolution=resolution,
legend='Filtered {} of {} bins'.format(len(badcol), total))
# no mappability will result in NaNs, better to filter out these columns
if mappability:
badcol.update((i, True) for i, m in enumerate(mappability) if not m)
# add manually columns to bad columns
if extra_bads:
removed_manually = 0
for ebc in extra_bads:
c, ebc = ebc.split(':')
b, e = list(map(int, ebc.split('-')))
b = b // resolution + section_pos[c][0]
e = e // resolution + section_pos[c][0]
removed_manually += (e - b)
badcol.update(dict((p, 'manual') for p in range(b, e)))
printime(' - Removed %d columns manually.' % removed_manually)
raw_cisprc = sum(float(cisprc[k][0]) / cisprc[k][1]
for k in cisprc if not k in badcol) / (len(cisprc) - len(badcol))
printime(' - Rescaling sum of interactions per bins')
size = len(bins)
biases = [float('nan') if k in badcol else cisprc.get(k, [0, 1., 0, 0])[1]
for k in range(size)]
if normalization == 'ICE':
printime(' - ICE normalization')
hic_data = load_hic_data_from_bam(
inbam, resolution, filter_exclude=filter_exclude,
tmpdir=outdir, ncpus=ncpus, nchunks=max_njobs)
hic_data.bads = badcol
hic_data.normalize_hic(iterations=100, max_dev=0.000001)
biases = hic_data.bias.copy()
del(hic_data)
elif normalization == 'Vanilla':
printime(' - Vanilla normalization')
mean_col = nanmean(biases)
biases = dict((k, b / mean_col * mean_col**0.5)
for k, b in enumerate(biases))
elif normalization == 'SQRT':
printime(' - Vanilla-SQRT normalization')
biases = [b**0.5 for b in biases]
mean_col = nanmean(biases)
biases = dict((k, b / mean_col * mean_col**0.5)
for k, b in enumerate(biases))
elif normalization == 'oneD':
printime(' - oneD normalization')
if len(set([len(biases), len(mappability), len(n_rsites), len(cg_content)])) > 1:
print("biases", "mappability", "n_rsites", "cg_content")
print(len(biases), len(mappability), len(n_rsites), len(cg_content))
raise Exception('Error: not all arrays have the same size')
tmp_oneD = path.join(outdir,'tmp_oneD_%s' % (extra_out))
mkdir(tmp_oneD)
biases = oneD(tmp_dir=tmp_oneD, p_fit=p_fit, tot=biases, map=mappability,
res=n_rsites, cg=cg_content, seed=seed)
biases = dict((k, b) for k, b in enumerate(biases))
rmtree(tmp_oneD)
elif normalization == 'custom':
n_pos = 0
biases = {}
print('Using provided biases...')
with open(biases_path, 'r') as r:
next(r)
for line in r:
if line[0] == 'N':
#b = float('nan')
badcol[n_pos] = 0
biases[n_pos] = float('nan')
else:
b = float(line)
if b == 0:
badcol[n_pos] = 0
biases[n_pos] = float('nan')
else:
biases[n_pos] = b
n_pos += 1
for add in range(max(biases.keys()), total + 1):
biases[add] = float('nan')
else:
raise NotImplementedError('ERROR: method %s not implemented' %
normalization)
# collect subset-matrices and write genomic one
# out = open(os.path.join(outdir,
# 'hicdata_%s.abc' % (nicer(resolution).replace(' ', ''))), 'w')
printime(' - Getting sum of normalized bins')
pool = mu.Pool(ncpus)
procs = []
for i, (region, start, end) in enumerate(zip(regs, begs, ends)):
fname = path.join(outdir,
'tmp_%s:%d-%d_%s.pickle' % (region, start, end, extra_out))
procs.append(pool.apply_async(sum_nrm_matrix,
args=(fname, biases,)))
pool.close()
print_progress(procs)
pool.join()
# to correct biases
sumnrm = sum(p.get() for p in procs)
target = (sumnrm / float(size * size * factor))**0.5
biases = dict([(b, biases[b] * target) for b in biases])
if not normalize_only:
printime(' - Computing Cis percentage')
# Calculate Cis percentage
pool = mu.Pool(ncpus)
procs = []
for i, (region, start, end) in enumerate(zip(regs, begs, ends)):
fname = path.join(outdir,
'tmp_%s:%d-%d_%s.pickle' % (region, start, end, extra_out))
procs.append(pool.apply_async(get_cis_perc,
args=(fname, biases, badcol, bins)))
pool.close()
print_progress(procs)
pool.join()
# collect results
cis = total = 0
for proc in procs:
c, t = proc.get()
cis += c
total += t
norm_cisprc = float(cis) / total
print(' * Cis-percentage: %.1f%%' % (norm_cisprc * 100))
else:
norm_cisprc = 0.
printime(' - Rescaling decay')
# normalize decay by size of the diagonal, and by Vanilla correction
# (all cells must still be equals to 1 in average)
pool = mu.Pool(ncpus)
procs = []
for i, (region, start, end) in enumerate(zip(regs, begs, ends)):
fname = path.join(outdir,
'tmp_%s:%d-%d_%s.pickle' % (region, start, end, extra_out))
procs.append(pool.apply_async(sum_dec_matrix,
args=(fname, biases, badcol, bins)))
pool.close()
print_progress(procs)
pool.join()
# collect results
nrmdec = {}
rawdec = {}
for proc in procs:
tmpnrm, tmpraw = proc.get()
for c, d in tmpnrm.items():
for k, v in d.items():
try:
nrmdec[c][k] += v
rawdec[c][k] += tmpraw[c][k]
except KeyError:
try:
nrmdec[c][k] = v
rawdec[c][k] = tmpraw[c][k]
except KeyError:
nrmdec[c] = {k: v}
rawdec[c] = {k: tmpraw[c][k]}
# count the number of cells per diagonal
# TODO: parallelize
# find largest chromosome
len_crms = dict((c, section_pos[c][1] - section_pos[c][0]) for c in section_pos)
# initialize dictionary
ndiags = dict((c, dict((k, 0) for k in range(len_crms[c]))) for c in sections)
for crm in section_pos:
beg_chr, end_chr = section_pos[crm][0], section_pos[crm][1]
chr_size = end_chr - beg_chr
thesebads = [b for b in badcol if beg_chr <= b <= end_chr]
for dist in range(1, chr_size):
ndiags[crm][dist] += chr_size - dist
# from this we remove bad columns
# bad columns will only affect if they are at least as distant from
# a border as the distance between the longest diagonal and the
# current diagonal.
bad_diag = set() # 2 bad rows can point to the same bad cell in diagonal
maxp = end_chr - dist
minp = beg_chr + dist
for b in thesebads:
if b < maxp: # not inclusive!!
bad_diag.add(b)
if b >= minp:
bad_diag.add(b - dist)
ndiags[crm][dist] -= len(bad_diag)
# different behavior for longest diagonal:
ndiags[crm][0] += chr_size - sum(beg_chr <= b < end_chr for b in thesebads)
# normalize sum per diagonal by total number of cells in diagonal
signal_to_noise = 0.05
min_n = signal_to_noise ** -2. # equals 400 when default
for crm in sections:
if not crm in nrmdec:
nrmdec[crm] = {}
rawdec[crm] = {}
tmpdec = 0 # store count by diagonal
tmpsum = 0 # store count by diagonal
ndiag = 0
val = 0
previous = [] # store diagonals to be summed in case not reaching the minimum
for k in ndiags[crm]:
tmpdec += nrmdec[crm].get(k, 0.)
tmpsum += rawdec[crm].get(k, 0.)
previous.append(k)
if tmpsum > min_n:
ndiag = sum(ndiags[crm][k] for k in previous)
val = tmpdec # backup of tmpdec kept for last ones outside the loop
try:
ratio = val / ndiag
for l in previous:
nrmdec[crm][l] = ratio
except ZeroDivisionError: # all columns at this distance are "bad"
pass
previous = []
tmpdec = 0
tmpsum = 0
# last ones we average with previous result
if len(previous) == len(ndiags[crm]):
nrmdec[crm] = {}
elif tmpsum < min_n:
ndiag += sum(ndiags[crm][k] for k in previous)
val += tmpdec
try:
ratio = val / ndiag
for k in previous:
nrmdec[crm][k] = ratio
except ZeroDivisionError: # all columns at this distance are "bad"
pass
return biases, nrmdec, badcol, raw_cisprc, norm_cisprc
def run(opts):
check_options(opts)
launch_time = time.localtime()
param_hash = digest_parameters(opts)
if opts.bam:
mreads = path.realpath(opts.bam)
else:
mreads = path.join(opts.workdir, load_parameters_fromdb(opts))
filter_exclude = opts.filter
outdir = path.join(opts.workdir, '04_normalization')
mkdir(outdir)
mappability = gc_content = n_rsites = None
if opts.normalization == 'oneD':
if not opts.fasta:
raise Exception('ERROR: missing path to FASTA for oneD normalization')
if not opts.renz:
raise Exception('ERROR: missing restriction enzyme name for oneD normalization')
if not opts.mappability:
raise Exception('ERROR: missing path to mappability for oneD normalization')
bamfile = AlignmentFile(mreads, 'rb')
refs = bamfile.references
bamfile.close()
# get genome sequence ~1 min
printime(' - parsing FASTA')
genome = parse_fasta(opts.fasta, verbose=False)
fas = set(genome.keys())
bam = set(refs)
if fas - bam:
print('WARNING: %d extra chromosomes in FASTA (removing them)' % (len(fas - bam)))
if len(fas - bam) <= 50:
print('\n'.join([(' - ' + c) for c in (fas - bam)]))
if bam - fas:
txt = ('\n'.join([(' - ' + c) for c in (bam - fas)])
if len(bam - fas) <= 50 else '')
raise Exception('ERROR: %d extra chromosomes in BAM (remove them):\n%s\n' % (
len(bam - fas), txt))
refs = [crm for crm in refs if crm in genome]
if len(refs) == 0:
raise Exception("ERROR: chromosomes in FASTA different the ones"
" in BAM")
# get mappability ~2 min
printime(' - Parsing mappability')
mappability = parse_mappability_bedGraph(
opts.mappability, opts.reso,
wanted_chrom=refs[0] if len(refs)==1 else None)
# resize chomosomes
for c in refs:
if not c in mappability:
mappability[c] = [float('nan')] * (len(refs) // opts.reso + 1)
if len(mappability[c]) < len(refs) // opts.reso + 1:
mappability[c] += [float('nan')] * (
(len(refs) // opts.reso + 1) - len(mappability[c]))
# concatenates
mappability = reduce(lambda x, y: x + y,
(mappability.get(c, []) for c in refs))
printime(' - Computing GC content per bin (removing Ns)')
gc_content = get_gc_content(genome, opts.reso, chromosomes=refs,
n_cpus=opts.cpus)
# pad mappability at the end if the size is close to gc_content
if len(mappability)<len(gc_content) and len(mappability)/len(gc_content) > 0.95:
mappability += [float('nan')] * (len(gc_content)-len(mappability))
# compute r_sites ~30 sec
# TODO: read from DB
printime(' - Computing number of RE sites per bin (+/- 200 bp)')
n_rsites = []
re_site = RESTRICTION_ENZYMES[opts.renz].replace('|', '')
for crm in refs:
for pos in range(200, len(genome[crm]) + 200, opts.reso):
seq = genome[crm][pos-200:pos + opts.reso + 200]
n_rsites.append(seq.count(re_site))
## CHECK TO BE REMOVED
# out = open('tmp_mappability.txt', 'w')
# i = 0
# for crm in refs:
# for pos in xrange(len(genome[crm]) / opts.reso + 1):
# out.write('%s\t%d\t%d\t%f\n' % (crm, pos * opts.reso, pos * opts.reso + opts.reso, mappability[i]))
# i += 1`
# out.close()
# compute GC content ~30 sec
# TODO: read from DB
biases, decay, badcol, raw_cisprc, norm_cisprc = read_bam(
mreads, filter_exclude, opts.reso, min_count=opts.min_count, sigma=2,
factor=1, outdir=outdir, extra_out=param_hash, ncpus=opts.cpus,
normalization=opts.normalization, mappability=mappability,
p_fit=opts.p_fit, cg_content=gc_content, n_rsites=n_rsites,
seed=opts.seed,
normalize_only=opts.normalize_only, max_njobs=opts.max_njobs,
extra_bads=opts.badcols, biases_path=opts.biases_path,
cis_limit=opts.cis_limit, trans_limit=opts.trans_limit,
min_ratio=opts.ratio_limit, fast_filter=opts.fast_filter)
inter_vs_gcoord = path.join(opts.workdir, '04_normalization',
'interactions_vs_genomic-coords.png_%s_%s.png' % (
opts.reso, param_hash))
# get and plot decay
if not opts.normalize_only:
printime(' - Computing interaction decay vs genomic distance')
(_, _, _), (a2, _, _), (_, _, _) = plot_distance_vs_interactions(
decay, max_diff=10000, resolution=opts.reso, normalized=not opts.filter_only,
savefig=inter_vs_gcoord)
print (' -> Decay slope 0.7-10 Mb\t%s' % a2)
else:
a2 = 0.
printime(' - Saving biases and badcol columns')
# biases
bias_file = path.join(outdir, 'biases_%s_%s.pickle' % (
nicer(opts.reso).replace(' ', ''), param_hash))
out = open(bias_file, 'wb')
dump({'biases' : biases,
'decay' : decay,
'badcol' : badcol,
'resolution': opts.reso}, out, HIGHEST_PROTOCOL)
out.close()
finish_time = time.localtime()
try:
save_to_db(opts, bias_file, mreads, len(badcol),
len(biases), raw_cisprc, norm_cisprc,
inter_vs_gcoord, a2, opts.filter,
launch_time, finish_time)
except:
# release lock anyway
print_exc()
try:
remove(path.join(opts.workdir, '__lock_db'))
except OSError:
pass
exit(1)
def run(opts):
check_options(opts)
launch_time = time.localtime()
param_hash = digest_parameters(opts, extra=['quiet'])
biases = None
if opts.zrange:
vmin = float(opts.zrange.split(',')[0])
vmax = float(opts.zrange.split(',')[1])
else:
vmin = vmax = None
if opts.figsize:
opts.figsize = list(map(float, opts.figsize.split(',')))
clean = True # change for debug
biases = None
if opts.bam:
mreads = path.realpath(opts.bam)
if not opts.biases and all(v != 'raw' for v in opts.normalizations):
raise Exception('ERROR: external BAM input, should provide path to'
' biases file.')
else:
biases, mreads = load_parameters_fromdb(opts)
mreads = path.join(opts.workdir, mreads)
biases = path.join(opts.workdir, biases) if biases else None
if opts.biases:
biases = opts.biases
coord1 = opts.coord1
coord2 = opts.coord2
if coord2 and not coord1:
coord1, coord2 = coord2, coord1
if not coord1:
region1 = None
start1 = None
end1 = None
region2 = None
start2 = None
end2 = None
else:
try:
crm1, pos1 = coord1.split(':')
start1, end1 = pos1.split('-')
region1 = crm1
start1 = int(start1)
end1 = int(end1)
except ValueError:
region1 = coord1
start1 = None
end1 = None
if coord2:
try:
crm2, pos2 = coord2.split(':')
start2, end2 = pos2.split('-')
region2 = crm2
start2 = int(start2)
end2 = int(end2)
except ValueError:
region2 = coord2
start2 = None
end2 = None
else:
region2 = None
start2 = None
end2 = None
if opts.plot and not opts.force_plot:
if opts.interactive:
max_size = 3500**2
else:
max_size = 5000**2
else:
max_size = None
outdir = path.join(opts.workdir, '05_sub-matrices')
mkdir(outdir)
tmpdir = path.join(opts.workdir, '05_sub-matrices',
'_tmp_sub-matrices_%s' % param_hash)
mkdir(tmpdir)
if region1:
if region1:
if not opts.quiet:
stdout.write('\nExtraction of %s' % (region1))
if start1:
if not opts.quiet:
stdout.write(':%s-%s' % (start1, end1))
else:
if not opts.quiet:
stdout.write(' (full chromosome)')
if region2:
if not opts.quiet:
stdout.write(' intersection with %s' % (region2))
if start2:
if not opts.quiet:
stdout.write(':%s-%s\n' % (start2, end2))
else:
if not opts.quiet:
stdout.write(' (full chromosome)\n')
else:
if not opts.quiet:
stdout.write('\n')
else:
if not opts.quiet:
stdout.write('\nExtraction of %s genome\n' %
('partial' if opts.chr_name else 'full'))
out_files = {}
out_plots = {}
if opts.matrix or opts.plot:
sections, section_pos = get_sections(mreads, opts.chr_name)
for norm in opts.normalizations:
norm_string = ('RAW' if norm == 'raw' else
'NRM' if norm == 'norm' else 'DEC')
printime('Getting %s matrices' % norm)
try:
matrix, bads1, bads2, regions, name, bin_coords = get_matrix(
mreads,
opts.reso,
load(open(biases, 'rb'))
if biases and norm != 'raw' else None,
normalization=norm,
filter_exclude=opts.filter,
region1=region1,
start1=start1,
end1=end1,
region2=region2,
start2=start2,
end2=end2,
tmpdir=tmpdir,
ncpus=opts.cpus,
return_headers=True,
nchunks=opts.nchunks,
verbose=not opts.quiet,
clean=clean,
max_size=max_size,
chr_order=opts.chr_name)
except NotImplementedError:
if norm == "raw&decay":
warn('WARNING: raw&decay normalization not implemented '
'for matrices\n... skipping\n')
continue
raise
b1, e1, b2, e2 = bin_coords
b1, e1 = 0, e1 - b1
b2, e2 = 0, e2 - b2
if opts.row_names:
starts = [start1, start2]
ends = [end1, end2]
row_names = ((reg, p + 1, p + opts.reso)
for r, reg in enumerate(regions) for p in range(
starts[r] if r < len(starts) and starts[r]
else 0, ends[r] if r < len(ends) and ends[r]
else sections[reg], opts.reso))
if opts.matrix:
printime(' - Writing: %s' % norm)
fnam = '%s_%s_%s%s.mat' % (norm, name, nicer(
opts.reso, sep=''), ('_' + param_hash))
out_files[norm_string] = path.join(outdir, fnam)
out = open(path.join(outdir, fnam), 'w')
for reg in regions:
out.write('# CRM %s\t%d\n' % (reg, sections[reg]))
if region2:
out.write('# BADROWS %s\n' %
(','.join([str(b) for b in bads1])))
out.write('# BADCOLS %s\n' %
(','.join([str(b) for b in bads2])))
else:
out.write('# MASKED %s\n' %
(','.join([str(b) for b in bads1])))
if opts.row_names:
out.write('\n'.join('%s\t%d\t%d\t' %
(next(row_names)) + '\t'.join(
str(matrix.get((i, j), 0))
for i in range(b1, e1))
for j in range(b2, e2)) + '\n')
else:
out.write('\n'.join('\t'.join(
str(matrix.get((i, j), 0)) for i in range(b1, e1))
for j in range(b2, e2)) + '\n')
out.close()
if opts.plot:
# transform matrix
matrix = array([
array([matrix.get((i, j), 0) for i in range(b1, e1)])
for j in range(b2, e2)
])
m = zeros_like(matrix)
for bad1 in bads1:
m[:, bad1] = 1
for bad2 in bads2:
m[bad2, :] = 1
matrix = ma.masked_array(matrix, m)
printime(' - Plotting: %s' % norm)
fnam = '%s_%s_%s%s%s.%s' % (
'nrm' if norm == 'norm' else norm[:3], name,
nicer(opts.reso, sep=''), ('_' + param_hash),
'_tri' if opts.triangular else '', opts.format)
out_plots[norm_string] = path.join(outdir, fnam)
pltbeg1 = 0 if start1 is None else start1
pltend1 = sections[regions[0]] if end1 is None else end1
pltbeg2 = 0 if start2 is None else start2
pltend2 = sections[regions[-1]] if end2 is None else end2
xlabel = '{}:{:,}-{:,}'.format(regions[0],
pltbeg1 if pltbeg1 else 1,
pltend1)
ylabel = '{}:{:,}-{:,}'.format(regions[-1],
pltbeg2 if pltbeg2 else 1,
pltend2)
section_pos = OrderedDict(
(k, section_pos[k]) for k in section_pos if k in regions)
transform = (log2 if opts.transform == 'log2' else
log if opts.transform == 'log' else lambda x: x)
tads = None
if opts.tad_def and not region2:
tads = load_tads_fromdb(opts)
if tads and start1:
tads = dict([
(t, tads[t]) for t in tads
if (int(tads[t]['start']) >= start1 // opts.reso
and int(tads[t]['end']) <= end1 // opts.reso)
])
for tad in tads:
tads[tad]['start'] -= start1 // opts.reso
tads[tad]['end'] -= start1 // opts.reso
ax1, _ = plot_HiC_matrix(
matrix,
triangular=opts.triangular,
vmin=vmin,
vmax=vmax,
cmap=opts.cmap,
figsize=opts.figsize,
transform=transform,
bad_color=opts.bad_color if norm != 'raw' else None,
tad_def=tads)
ax1.set_title('Region: %s, normalization: %s, resolution: %s' %
(name, norm, nicer(opts.reso)),
y=1.05)
format_HiC_axes(ax1,
start1,
end1,
start2,
end2,
opts.reso,
regions,
section_pos,
sections,
opts.xtick_rotation,
triangular=False)
if opts.interactive:
plt.show()
plt.close('all')
else:
tadbit_savefig(path.join(outdir, fnam))
if not opts.matrix and not opts.only_plot:
printime('Getting and writing matrices')
out_files.update(
write_matrix(mreads,
opts.reso,
load(open(biases, 'rb')) if biases else None,
outdir,
filter_exclude=opts.filter,
normalizations=opts.normalizations,
region1=region1,
start1=start1,
end1=end1,
region2=region2,
start2=start2,
end2=end2,
tmpdir=tmpdir,
append_to_tar=None,
ncpus=opts.cpus,
nchunks=opts.nchunks,
verbose=not opts.quiet,
extra=param_hash,
cooler=opts.cooler,
clean=clean,
chr_order=opts.chr_name))
if clean:
printime('Cleaning')
system('rm -rf %s ' % tmpdir)
if not opts.interactive:
printime('Saving to DB')
finish_time = time.localtime()
save_to_db(opts, launch_time, finish_time, out_files, out_plots)
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 main():
"""
main function
"""
opts = get_options()
filter_exclude = filters_to_bin(opts.filter)
tadbit_bam = opts.tadbit_bam
hicup_bam = opts.hicup_bam
map_folder = opts.map_folder
nreads = opts.nreads * 1_000_000
tag_dict = {
(1, 1): (67, 131),
(0, 0): (115, 179),
(1, 0): (99, 147),
(0, 1): (83, 163),
}
out = open(hicup_bam, 'w')
for seqs in get_mapped_chunk(map_folder, nreads):
bamfile = AlignmentFile(tadbit_bam, 'rb')
refs = bamfile.references
printime(f' - processing BAM (for {len(seqs) / 1_000_000}M reads)')
for r in bamfile.fetch(multiple_iterators=False):
if r.flag & filter_exclude:
continue
rid = r.qname
ridname = rid.split('#')[0]
pos1 = r.reference_start + 1
which, len1 = r.cigar[0]
tags = dict(r.tags)
if which == 6: # first read-end
s1, s2 = tags['S1'], tags['S2']
else:
s2, s1 = tags['S1'], tags['S2']
if s1 == 0:
pos1 = pos1 - len1 + 1
try:
seq, qal = seqs[ridname, pos1]
except KeyError:
continue
crm1 = r.reference_name
crm2 = refs[r.mrnm]
pos2 = r.mpos + 1
len2 = r.tlen
dist = 0 if crm1 != crm2 else abs(pos2 - pos1)
tags = dict(r.tags)
if s2 == 0:
pos2 = pos2 - len2 + 1
flag = tag_dict[s1, s2][0]
out.write((f'{r.qname}\t{flag}\t{crm1}\t{pos1}\t{len1}\t'
f'{len(seq)}M\t{crm2}\t{pos2}\t{dist}\t{seq}\t'
f'{qal}\tMD:Z:{len1}\tPG:Z:MarkDuplicates\tNM:i:0\t'
f'AS:i:{len1}\tXS:i:1\n'))
bamfile.close()
seqs.clear()
out.close()