def insert_sizes(fnam, savefig=None, nreads=None, max_size=99.9, axe=None,
xlog=False):
"""
Plots the distribution of dangling-ends lengths
:param fnam: input file name
:param None savefig: path where to store the output images.
:param 99.9 max_size: top percentage of distances to consider, within the
top 0.01% are usually found very long outliers.
:param False xlog: represent x axis in logarithmic scale
:returns: the median value and the percentile inputed as max_size.
"""
distr = {}
genome_seq = OrderedDict()
fhandler = open(fnam)
line = fhandler.next()
while line.startswith('#'):
if line.startswith('# CRM '):
crm, clen = line[6:].split()
genome_seq[crm] = int(clen)
line = fhandler.next()
des = []
if nreads:
nreads /= 2
try:
while True:
(crm1, pos1, dir1, _, re1, _,
crm2, pos2, dir2, _, re2) = line.strip().split('\t')[1:12]
if re1==re2 and crm1 == crm2 and dir1 != dir2:
pos1, pos2 = int(pos1), int(pos2)
if (pos2 > pos1) == int(dir1):
des.append(abs(pos2 - pos1))
if len(des) == nreads:
break
line = fhandler.next()
except StopIteration:
pass
fhandler.close()
ax = setup_plot(axe, figsize=(10, 5.5))
max_perc = np.percentile(des, max_size)
perc99 = np.percentile(des, 99)
perc01 = np.percentile(des, 1)
perc50 = np.percentile(des, 50)
perc95 = np.percentile(des, 95)
perc05 = np.percentile(des, 5)
desapan = ax.axvspan(perc95, perc99, facecolor='darkolivegreen', alpha=.3,
label='1-99%% DEs\n(%.0f-%.0f nts)' % (perc01, perc99))
ax.axvspan(perc01, perc05, facecolor='darkolivegreen', alpha=.3)
desapan = ax.axvspan(perc05, perc95, facecolor='darkseagreen', alpha=.3,
label='5-95%% DEs\n(%.0f-%.0f nts)' % (perc05, perc95))
deshist = ax.hist(des, bins=100, range=(0, max_perc),
alpha=.7, color='darkred', label='Dangling-ends')
ylims = ax.get_ylim()
plots = []
ax.set_xlabel('Genomic distance between reads')
ax.set_ylabel('Count')
ax.set_title('Distribution of dangling-ends ' +
'lenghts\n(median: %s, top %.1f%%, up to %0.f nts)' % (
perc50, max_size, max_perc))
if xlog:
ax.set_xscale('log')
ax.set_xlim((50, max_perc))
plt.subplots_adjust(left=0.1, right=0.75)
ax.legend(bbox_to_anchor=(1.4, 1), frameon=False)
if savefig:
tadbit_savefig(savefig)
elif not axe:
plt.show()
plt.close('all')
return perc50, max_perc
def insert_sizes(fnam, savefig=None, nreads=None, max_size=99.9, axe=None,
show=False, xlog=False, stats=('median', 'perc_max')):
"""
Plots the distribution of dangling-ends lengths
:param fnam: input file name
:param None savefig: path where to store the output images.
:param 99.9 max_size: top percentage of distances to consider, within the
top 0.01% are usually found very long outliers.
:param False xlog: represent x axis in logarithmic scale
:param ('median', 'perc_max') stats: returns this set of values calculated from the
distribution of insert/fragment sizes. Possible values are:
- 'median' median of the distribution
- 'perc_max' percentil defined by the other parameter 'max_size'
- 'first_deacay' starting from the median of the distribution to the
first window where 10 consecutive insert sizes are counted less than
a given value (this given value is equal to the sum of all
sizes divided by 100 000)
- 'MAD' Double Median Adjusted Deviation
:returns: the median value and the percentile inputed as max_size.
"""
distr = {}
genome_seq = OrderedDict()
fhandler = open(fnam)
line = fhandler.next()
while line.startswith('#'):
if line.startswith('# CRM '):
crm, clen = line[6:].split()
genome_seq[crm] = int(clen)
line = fhandler.next()
des = []
if nreads:
nreads /= 2
try:
while True:
(crm1, pos1, dir1, _, re1, _,
crm2, pos2, dir2, _, re2) = line.strip().split('\t')[1:12]
if re1==re2 and crm1 == crm2 and dir1 != dir2:
pos1, pos2 = int(pos1), int(pos2)
if (pos2 > pos1) == int(dir1):
des.append(abs(pos2 - pos1))
if len(des) == nreads:
break
line = fhandler.next()
except StopIteration:
pass
fhandler.close()
max_perc = np.percentile(des, max_size)
perc99 = np.percentile(des, 99)
perc01 = np.percentile(des, 1)
perc50 = np.percentile(des, 50)
perc95 = np.percentile(des, 95)
perc05 = np.percentile(des, 5)
to_return = {'median': perc50}
cutoff = len(des) / 100000.
count = 0
for v in xrange(int(perc50), int(max(des))):
if des.count(v) < cutoff:
count += 1
else:
count = 0
if count >= 10:
to_return['first_decay'] = v - 10
break
else:
raise Exception('ERROR: not found')
to_return['perc_max'] = max_perc
to_return['MAD'] = mad(des)
if not savefig and not axe and not show:
return [to_return[k] for k in stats]
ax = setup_plot(axe, figsize=(10, 5.5))
desapan = ax.axvspan(perc95, perc99, facecolor='darkolivegreen', alpha=.3,
label='1-99%% DEs\n(%.0f-%.0f nts)' % (perc01, perc99))
ax.axvspan(perc01, perc05, facecolor='darkolivegreen', alpha=.3)
desapan = ax.axvspan(perc05, perc95, facecolor='darkseagreen', alpha=.3,
label='5-95%% DEs\n(%.0f-%.0f nts)' % (perc05, perc95))
deshist = ax.hist(des, bins=100, range=(0, max_perc),
alpha=.7, color='darkred', label='Dangling-ends')
ylims = ax.get_ylim()
plots = []
ax.set_xlabel('Genomic distance between reads')
ax.set_ylabel('Count')
ax.set_title('Distribution of dangling-ends ' +
'lenghts\n(median: %s, top %.1f%%, up to %0.f nts)' % (
perc50, max_size, max_perc))
if xlog:
ax.set_xscale('log')
ax.set_xlim((50, max_perc))
plt.subplots_adjust(left=0.1, right=0.75)
ax.legend(bbox_to_anchor=(1.4, 1), frameon=False)
if savefig:
tadbit_savefig(savefig)
elif show and not axe:
plt.show()
plt.close('all')
return [to_return[k] for k in stats]
def insert_sizes(fnam, savefig=None, nreads=None, max_size=99.9, axe=None,
xlog=False):
"""
Plots the distribution of dangling-ends lengths
:param fnam: input file name
:param None savefig: path where to store the output images.
:param 99.9 max_size: top percentage of distances to consider, within the
top 0.01% are usually found very long outliers.
:param False xlog: represent x axis in logarithmic scale
"""
distr = {}
genome_seq = OrderedDict()
fhandler = open(fnam)
line = fhandler.next()
while line.startswith('#'):
if line.startswith('# CRM '):
crm, clen = line[6:].split()
genome_seq[crm] = int(clen)
line = fhandler.next()
des = []
if nreads:
nreads /= 2
try:
while True:
(crm1, pos1, dir1, _, re1, _,
crm2, pos2, dir2, _, re2) = line.strip().split('\t')[1:12]
if re1==re2 and crm1 == crm2 and dir1 != dir2:
pos1, pos2 = int(pos1), int(pos2)
if (pos2 > pos1) == int(dir1):
des.append(abs(pos2 - pos1))
if len(des) == nreads:
break
line = fhandler.next()
except StopIteration:
pass
fhandler.close()
ax = setup_plot(axe, figsize=(10, 5.5))
max_perc = np.percentile(des, max_size)
perc99 = np.percentile(des, 99)
perc01 = np.percentile(des, 1)
perc95 = np.percentile(des, 95)
perc05 = np.percentile(des, 5)
desapan = ax.axvspan(perc95, perc99, facecolor='darkolivegreen', alpha=.3,
label='1-99%% DEs\n(%.0f-%.0f nts)' % (perc01, perc99))
ax.axvspan(perc01, perc05, facecolor='darkolivegreen', alpha=.3)
desapan = ax.axvspan(perc05, perc95, facecolor='darkseagreen', alpha=.3,
label='5-95%% DEs\n(%.0f-%.0f nts)' % (perc05, perc95))
deshist = ax.hist(des, bins=100, range=(0, max_perc),
alpha=.7, color='darkred', label='Dangling-ends')
ylims = ax.get_ylim()
plots = []
ax.set_xlabel('Genomic distance between reads')
ax.set_ylabel('Count')
ax.set_title('Distribution of dangling-ends ' +
'lenghts\n(top %.1f%%, up to %0.f nts)' % (max_size, max_perc))
if xlog:
ax.set_xscale('log')
ax.set_xlim((50, max_perc))
plt.subplots_adjust(left=0.1, right=0.75)
ax.legend(bbox_to_anchor=(1.4, 1), frameon=False)
if savefig:
tadbit_savefig(savefig)
elif not axe:
plt.show()
plt.close('all')