def correlate_matrices(hic_data1, hic_data2, max_dist=10, intra=False,
savefig=None, show=False, savedata=None):
"""
Compare the iteractions of two Hi-C matrices at a given distance,
with spearman rank correlation
:param hic_data1: Hi-C-data object
:param hic_data2: Hi-C-data object
:param 1 resolution: to be used for scaling the plot
:param 10 max_dist: maximum distance from diagonal (e.g. 10 mean we will
not look further than 10 times the resolution)
:param None savefig: path to save the plot
:param False intra: only takes into account intra-chromosomal contacts
:param False show: displays the plot
:returns: list of correlations and list of genomic distances
"""
corr = []
dist = []
if (intra and hic_data1.sections and hic_data2.sections and
hic_data1.sections == hic_data2.sections):
for i in xrange(1, max_dist + 1):
diag1 = []
diag2 = []
for crm in hic_data1.section_pos:
for j in xrange(hic_data1.section_pos[crm][0],
hic_data1.section_pos[crm][1] - i):
diag1.append(hic_data1[j, i + j])
diag2.append(hic_data2[j, i + j])
corr.append(spearmanr(diag1, diag2)[0])
dist.append(i)
else:
if intra:
warn('WARNING: hic_dta does not contain chromosome coordinates, ' +
'intra set to False')
for i in xrange(1, max_dist + 1):
diag1 = []
diag2 = []
for j in xrange(len(hic_data1) - i):
diag1.append(hic_data1[j, i + j])
diag2.append(hic_data2[j, i + j])
corr.append(spearmanr(diag1, diag2)[0])
dist.append(i)
if show or savefig:
plt.plot(dist, corr, color='orange', linewidth=3, alpha=.8)
plt.xlabel('Genomic distance in bins')
plt.ylabel('Spearman rank correlation')
plt.xlim((0, dist[-1]))
if savefig:
tadbit_savefig(savefig)
if show:
plt.show()
plt.close('all')
if savedata:
out = open(savedata, 'w')
out.write('# genomic distance\tSpearman rank correlation\n')
for i in xrange(len(corr)):
out.write('%s\t%s\n' % (dist[i], corr[i]))
out.close()
return corr, dist
def do_3d_plot(nam, outfile, size, count, minmax, sigma=0, log=False):
fig = plt.figure(figsize=(12,8))
ax = fig.add_subplot(1, 1, 1, projection='3d')
beg = -size / 2
end = size / 2
X = np.arange(beg, end, 1)
Y = np.arange(beg, end, 1)
X, Y = np.meshgrid(X, Y)
Z = np.array([np.array([float(i) for i in l.split()]) for l in open(nam) if not l.startswith('#')])
plt.title(nam + '\nMean: %.3f, median: %.3f, standard-deviation: %.3f (N=%d)' % (np.mean(Z), np.median(Z), np.std(Z), count))
if sigma:
Z = ndimage.gaussian_filter(Z, sigma=sigma, order=0)
if log:
Z = np.log(Z)
zspan = minmax if minmax else np.max(np.abs(Z))
zmax = zspan
zmin = -zspan
else:
zspan = minmax if minmax else np.max(np.abs(Z - 1))
zmin = -zspan + 1
zmax = zspan + 1
cmap = 'coolwarm' # 'coolwarm'
_ = ax.contourf(X, Y, Z, zdir='z', offset=zmin,
cmap=cmap, vmin=zmin, vmax=zmax)
surf = ax.plot_surface(X, Y, Z, rstride=1, cstride=1, cmap=cmap,
linewidth=0, antialiased=True, alpha=1,
vmin=zmin, vmax=zmax, shade=True)
ax.set_zlim3d(zmin, zmax)
ax.view_init(elev=15, azim=25)
cb = fig.colorbar(surf, shrink=0.5, aspect=20)
cb.set_label('%sverage normalized interactions%s' %
('Log a' if log else 'A',
'\nSmoothed with $\sigma=%s$' % sigma))
tadbit_savefig(outfile)
def correlate_matrices(hic_data1, hic_data2, max_dist=10, intra=False,
savefig=None, show=False, savedata=None):
"""
Compare the iteractions of two Hi-C matrices at a given distance,
with spearman rank correlation
:param hic_data1: Hi-C-data object
:param hic_data2: Hi-C-data object
:param 1 resolution: to be used for scaling the plot
:param 10 max_dist: maximum distance from diagonal (e.g. 10 mean we will
not look further than 10 times the resolution)
:param None savefig: path to save the plot
:param False intra: only takes into account intra-chromosomal contacts
:param False show: displays the plot
:returns: list of correlations and list of genomic distances
"""
corr = []
dist = []
if (intra and hic_data1.sections and hic_data2.sections and
hic_data1.sections == hic_data2.sections):
for i in xrange(1, max_dist + 1):
diag1 = []
diag2 = []
for crm in hic_data1.section_pos:
for j in xrange(hic_data1.section_pos[crm][0],
hic_data1.section_pos[crm][1] - i):
diag1.append(hic_data1[j, i + j])
diag2.append(hic_data2[j, i + j])
corr.append(spearmanr(diag1, diag2)[0])
dist.append(i)
else:
if intra:
warn('WARNING: hic_dta does not contain chromosome coordinates, ' +
'intra set to False')
for i in xrange(1, max_dist + 1):
diag1 = []
diag2 = []
for j in xrange(len(hic_data1) - i):
diag1.append(hic_data1[j, i + j])
diag2.append(hic_data2[j, i + j])
corr.append(spearmanr(diag1, diag2)[0])
dist.append(i)
if show or savefig:
plt.plot(dist, corr, color='orange', linewidth=3, alpha=.8)
plt.xlabel('Genomic distance in bins')
plt.ylabel('Spearman rank correlation')
plt.xlim((0, dist[-1]))
if savefig:
tadbit_savefig(savefig)
if show:
plt.show()
plt.close('all')
if savedata:
out = open(savedata, 'w')
out.write('# genomic distance\tSpearman rank correlation\n')
for i in xrange(len(corr)):
out.write('%s\t%s\n' % (dist[i], corr[i]))
out.close()
return corr, dist
def objective_function(self, log=False, smooth=True,
axe=None, savefig=None):
"""
This function plots the objective function value per each Monte-Carlo
step.
:param False log: log plot
:param True smooth: curve smoothing
:param None axe: a matplotlib.axes.Axes object to define the plot
appearance
:param None savefig: path to a file where to save the image generated;
if None, the image will be shown using matplotlib GUI (the extension
of the file name will determine the desired format).
"""
show = False
if not axe:
fig = plt.figure(figsize=(7, 7))
axe = fig.add_subplot(111)
show = True
axe.patch.set_facecolor('lightgrey')
axe.patch.set_alpha(0.4)
axe.grid(ls='-', color='w', lw=1.5, alpha=0.6, which='major')
axe.grid(ls='-', color='w', lw=1, alpha=0.3, which='minor')
axe.set_axisbelow(True)
axe.minorticks_on() # always on, not only for log
# remove tick marks
axe.tick_params(axis='both', direction='out', top=False,
right=False, left=False, bottom=False)
axe.tick_params(axis='both', direction='out', top=False,
right=False, left=False, bottom=False,
which='minor')
else:
fig = axe.get_figure()
# text
plt.xlabel('Iteration number')
plt.ylabel('IMP Objective Function Value')
plt.title('Model ' + str(self['rand_init']))
# smooth
nrjz = self['log_objfun'][1:]
if smooth:
xnew = linspace(0, len(nrjz), 10000)
nrjz_smooth = spline(range(len(nrjz)), nrjz, xnew,
order=3)
axe.plot(xnew, nrjz_smooth, color='darkred')
else:
axe.plot(nrjz, color='darkred')
# plot
axe.plot(nrjz, color='darkred', marker='o', alpha=.5, ms=4, ls='None')
# log
if log:
axe.set_yscale('log')
if savefig:
tadbit_savefig(savefig)
elif show:
plt.show()
def objective_function(self, log=False, smooth=True,
axe=None, savefig=None):
"""
This function plots the objective function value per each Monte-Carlo
step.
:param False log: log plot
:param True smooth: curve smoothing
:param None axe: a matplotlib.axes.Axes object to define the plot
appearance
:param None savefig: path to a file where to save the image generated;
if None, the image will be shown using matplotlib GUI (the extension
of the file name will determine the desired format).
"""
show = False
if not axe:
fig = plt.figure(figsize=(7, 7))
axe = fig.add_subplot(111)
show = True
axe.patch.set_facecolor('lightgrey')
axe.patch.set_alpha(0.4)
axe.grid(ls='-', color='w', lw=1.5, alpha=0.6, which='major')
axe.grid(ls='-', color='w', lw=1, alpha=0.3, which='minor')
axe.set_axisbelow(True)
axe.minorticks_on() # always on, not only for log
# remove tick marks
axe.tick_params(axis='both', direction='out', top=False,
right=False, left=False, bottom=False)
axe.tick_params(axis='both', direction='out', top=False,
right=False, left=False, bottom=False,
which='minor')
else:
fig = axe.get_figure()
# text
plt.xlabel('Iteration number')
plt.ylabel('IMP Objective Function Value')
plt.title('Model ' + str(self['rand_init']))
# smooth
nrjz = self['log_objfun'][1:]
if smooth:
xnew = linspace(0, len(nrjz), 10000)
nrjz_smooth = spline(range(len(nrjz)), nrjz, xnew,
order=3)
axe.plot(xnew, nrjz_smooth, color='darkred')
else:
axe.plot(nrjz, color='darkred')
# plot
axe.plot(nrjz, color='darkred', marker='o', alpha=.5, ms=4, ls='None')
# log
if log:
axe.set_yscale('log')
if savefig:
tadbit_savefig(savefig)
elif show:
plt.show()
def plot_iterative_mapping(fnam1, fnam2, total_reads=None, axe=None, savefig=None):
"""
:param fnam: input file name
:param total_reads: total number of reads in the initial FASTQ file
:param None axe: a matplotlib.axes.Axes object to define the plot
appearance
:param None savefig: path to a file where to save the image generated;
if None, the image will be shown using matplotlib GUI (the extension
of the file name will determine the desired format).
:returns: a dictionary with the number of reads per mapped length
"""
count_by_len = {}
total_reads = total_reads or 1
if not axe:
fig=plt.figure()
_ = fig.add_subplot(111)
colors = ['olive', 'darkcyan']
for i, fnam in enumerate([fnam1, fnam2]):
fhandler = open(fnam)
line = fhandler.next()
while line.startswith('#'):
line = fhandler.next()
try:
count_by_len[i] = {}
while True:
_, length, _, _ = line.rsplit('\t', 3)
try:
count_by_len[i][int(length)] += 1
except KeyError:
count_by_len[i][int(length)] = 1
line = fhandler.next()
except StopIteration:
pass
fhandler.close()
lengths = sorted(count_by_len[i].keys())
for k in lengths[::-1]:
count_by_len[i][k] += sum([count_by_len[i][j]
for j in lengths if j < k])
plt.plot(lengths, [float(count_by_len[i][l]) / total_reads
for l in lengths],
label='read' + str(i + 1), linewidth=2, color=colors[i])
plt.xlabel('read length (bp)')
if total_reads != 1:
plt.ylabel('Proportion of mapped reads')
else:
plt.ylabel('Number of mapped reads')
plt.legend(loc=4)
if savefig:
tadbit_savefig(savefig)
elif not axe:
plt.show()
return count_by_len
def plot_genomic_distribution(fnam, first_read=True, resolution=10000,
genome_seq=None, axe=None, ylim=None, savefig=None):
"""
:param fnam: input file name
:param True first_read: map first read.
:param 100 resolution: group reads that are closer than this resolution
parameter
:param None axe: a matplotlib.axes.Axes object to define the plot
appearance
:param None savefig: path to a file where to save the image generated;
if None, the image will be shown using matplotlib GUI (the extension
of the file name will determine the desired format).
"""
distr = {}
idx1, idx2 = (1, 3) if first_read else (7, 9)
for line in open(fnam):
crm, pos = line.split()[idx1:idx2]
pos = int(pos) / resolution
try:
distr[crm][pos] += 1
except KeyError:
try:
distr[crm][pos] = 1
except KeyError:
distr[crm] = {pos: 1}
if not axe:
fig=plt.figure(figsize=(15, 3 * len(distr.keys())))
max_y = max([max(distr[c].values()) for c in distr])
max_x = max([len(distr[c].values()) for c in distr])
for i, crm in enumerate(genome_seq if genome_seq else distr):
plt.subplot(len(distr.keys()), 1, i + 1)
plt.plot(range(max(distr[crm])),
[distr[crm].get(j, 0) for j in xrange(max(distr[crm]))],
color='red', lw=1.5, alpha=0.7)
if genome_seq:
if ylim:
plt.vlines(len(genome_seq[crm]) / resolution, ylim[0], ylim[1])
else:
plt.vlines(len(genome_seq[crm]) / resolution, 0, max_y)
plt.xlim((0, max_x))
plt.ylim(ylim or (0, max_y))
plt.title(crm)
if savefig:
tadbit_savefig(savefig)
elif not axe:
plt.show()
def plot_distance_vs_interactions(fnam, min_diff=100, max_diff=1000000,
resolution=100, axe=None, savefig=None):
"""
:param fnam: input file name
:param 100 min_diff: lower limit kn genomic distance (usually equal to read
length)
:param 1000000 max_diff: upper limit in genomic distance to look for
:param 100 resolution: group reads that are closer than this resolution
parameter
:param None axe: a matplotlib.axes.Axes object to define the plot
appearance
:param None savefig: path to a file where to save the image generated;
if None, the image will be shown using matplotlib GUI (the extension
of the file name will determine the desired format).
"""
dist_intr = {}
for line in open(fnam):
_, cr1, ps1, _, _, _, _, cr2, ps2, _ = line.rsplit('\t', 9)
if cr1 != cr2:
continue
diff = resolution * (abs(int(ps1) - int(ps2)) / resolution)
if max_diff > diff > min_diff:
dist_intr.setdefault(diff, 0)
dist_intr[diff] += 1
for k in dist_intr.keys()[:]:
if dist_intr[k] <= 2:
del(dist_intr[k])
if not axe:
fig=plt.figure()
ax = fig.add_subplot(111)
x, y = zip(*sorted(dist_intr.items(), key=lambda x:x[0]))
plt.plot(x, y, 'k.')
# sigma = 10
# p_x = gaussian_filter1d(x, sigma)
# p_y = gaussian_filter1d(y, sigma)
# plot line of best fit
# plt.plot(p_x, p_y,color= 'darkgreen', lw=2, label='Gaussian fit')
plt.yscale('log')
plt.xscale('log')
plt.xlabel('Log genomic distance (binned by %d bp)' % resolution)
plt.ylabel('Log interaction count')
# plt.legend()
if savefig:
tadbit_savefig(savefig)
elif not axe:
plt.show()
def hic_map(data, genome_seq, biases=None, masked=None, resolution=100000,
savefig=None, show=False, savedata=None, focus=None):
if isinstance(data, str):
fnam = data
cumcs = {}
total = 0
for crm in genome_seq:
cumcs[crm] = total
total += len(genome_seq[crm]) / resolution + 1
# bin the data
data = [[0 for _ in xrange(total + 1)] for _ in xrange(total + 1)]
masked = masked or set()
for line in open(fnam):
read, cr1, ps1, _, _, _, _, cr2, ps2, _, _, _, _ = line.split()
if read in masked:
continue
ps1 = int(ps1) / resolution
ps2 = int(ps2) / resolution
try:
data[cumcs[cr1] + ps1][cumcs[cr2] + ps2] += 1
except:
break
else:
hic_data = data
beg, end = focus if focus else (0, len(hic_data))
beg -= 1 if focus else 0
if biases:
data = [[hic_data[len(hic_data) * i + j] / (biases[i] * biases[j])
for j in xrange(beg, end)]
for i in xrange(beg, end)]
else:
data = [[hic_data[len(hic_data) * i + j]
for j in xrange(beg, end)]
for i in xrange(beg, end)]
# do the plot
if show or savefig:
import numpy as np
plt.figure(figsize=(16, 12))
plt.imshow(np.log2(data), origin='lower', cmap='gist_earth',
interpolation='nearest')
plt.colorbar()
if savefig:
tadbit_savefig(savefig)
elif show:
plt.show()
if savedata:
out = open(savedata, 'w')
for line in data:
out.write('\t'.join([str(cell) for cell in line]) + '\n')
out.close()
def correlate_matrices(hic_data1, hic_data2, max_dist=10,
savefig=None, show=False, savedata=None):
"""
Compare the iteractions of two Hi-C matrices at a given distance,
with spearman rank correlation
:param hic_data1: Hi-C-data object
:param hic_data2: Hi-C-data object
:param 1 resolution: to be used for scaling the plot
:param 10 max_dist: maximum distance from diagonal (e.g. 10 mean we will
not look further than 10 times the resolution)
:param None savefig: path to save the plot
:param False show: displays the plot
:returns: list of correlations and list of genomic distances
"""
corr = []
dist = []
for i in xrange(1, max_dist + 1):
diag1 = []
diag2 = []
for j in xrange(len(hic_data1) - i):
diag1.append(hic_data1[j, i + j])
diag2.append(hic_data2[j, i + j])
corr.append(spearmanr(diag1, diag2)[0])
dist.append(i)
if show or savefig:
plt.plot(dist, corr, color='orange', linewidth=3, alpha=.8)
plt.xlabel('Genomic distance in bins')
plt.ylabel('Spearman rank correlation')
plt.xlim((0, dist[-1]))
if savefig:
tadbit_savefig(savefig)
if show:
plt.show()
plt.close('all')
if savedata:
out = open(savedata, 'w')
out.write('# genomic distance\tSpearman rank correlation\n')
for i in xrange(len(corr)):
out.write('%s\t%s\n' % (dist[i], corr[i]))
out.close()
return corr, dist
def quality_plot(fnam,
r_enz=None,
nreads=float('inf'),
axe=None,
savefig=None,
paired=False):
"""
Plots the sequencing quality of a given FASTQ file. If a restrinction enzyme
(RE) name is provided, can also represent the distribution of digested and
undigested RE sites and estimate an expected proportion of dangling-ends.
Proportion of dangling-ends is inferred by counting the number of times a
dangling-end site, is found at the beginning of any of the reads (divided by
the number of reads).
:param fnam: path to FASTQ file
:param None nreads: max number of reads to read, not necesary to read all
:param None savefig: path to a file where to save the image generated;
if None, the image will be shown using matplotlib GUI (the extension
of the file name will determine the desired format).
:param False paired: is input FASTQ contains both ends
:returns: the percentage of dangling-ends (sensu stricto) and the percentage of
reads with at least a ligation site.
"""
phred = dict([(c, i) for i, c in enumerate(
'!"#$%&\'()*+,-./0123456789:;<=>?@ABCDEFGHIJKLMNOPQRSTUVWXYZ[\]^_`abcdefghijklmnopqrstuvwxyz{|}~'
)])
if isinstance(r_enz, list):
r_enzs = r_enz
elif isinstance(r_enz, str):
r_enzs = [r_enz]
for k in RESTRICTION_ENZYMES.keys():
for i in range(len(r_enzs)):
if k.lower() == str(r_enz[i]).lower():
r_enz[i] = k
# else let it as None
quals = []
henes = []
sites = {}
fixes = {}
liges = {}
ligep = {}
tkw = dict(size=4, width=1.5)
if fnam.endswith('.gz'):
fhandler = gopen(fnam)
elif fnam.endswith('.dsrc'):
proc = Popen(['dsrc', 'd', '-t8', '-s', fnam], stdout=PIPE)
fhandler = proc.stdout
else:
fhandler = open(fnam)
if len(r_enzs) == 1 and r_enzs[0] is None:
if nreads:
while True:
try:
next(fhandler)
except EOFError:
break
seq = next(fhandler)
if 'N' in seq:
henes.extend([i for i, s in enumerate(seq) if s == 'N'])
next(fhandler)
line = next(fhandler)
quals.append([phred[i] for i in line.strip()])
if len(quals) > nreads:
break
else: # do this because it's faster
while True:
try:
next(fhandler)
except EOFError:
break
seq = next(fhandler)
if 'N' in seq:
henes.extend([i for i, s in enumerate(seq) if s == 'N'])
next(fhandler)
line = next(fhandler)
quals.append([phred[i] for i in line.strip()])
else:
r_sites = {}
d_sites = {}
for r_enz in r_enzs:
r_sites[r_enz] = RESTRICTION_ENZYMES[r_enz].replace('|', '')
d_sites[r_enz] = repaired(r_enz)
sites[r_enz] = [] # initialize dico to store undigested sites
fixes[r_enz] = [] # initialize dico to store digested sites
l_sites = religateds(r_enzs)
site = {}
fixe = {}
for r_enz in r_enzs:
site[r_enz] = re.compile(r_sites[r_enz])
fixe[r_enz] = re.compile(d_sites[r_enz])
# ligation sites should appear in lower case in the sequence
lige = {}
for k in l_sites:
liges[k] = [] # initialize dico to store sites
ligep[k] = 0 # initialize dico to store sites
l_sites[k] = l_sites[k].lower()
lige[k] = re.compile(l_sites[k])
while len(quals) <= nreads:
try:
next(fhandler)
except StopIteration:
break
seq = next(fhandler)
# ligation sites replaced by lower case to ease the search
for lig in l_sites.values():
seq = seq.replace(lig.upper(), lig)
for r_enz in r_enzs:
sites[r_enz].extend(
[m.start() for m in site[r_enz].finditer(seq)])
# TODO: you cannot have a repaired/fixed site in the middle of
# the sequence, this could be only checked at the beginning
fixes[r_enz].extend(
[m.start() for m in fixe[r_enz].finditer(seq)])
for k in lige: # for each paired of cut-site
liges[k].extend([m.start() for m in lige[k].finditer(seq)])
ligep[k] += l_sites[k] in seq
# store the number of Ns found in the sequences
if 'N' in seq:
henes.extend([i for i, s in enumerate(seq) if s == 'N'])
next(fhandler)
line = next(fhandler)
quals.append([phred[i] for i in line.strip()])
fhandler.close()
if not nreads:
nreads = len(quals)
quals = izip_longest(*quals, fillvalue=float('nan'))
meanquals, errorquals = zip(*[(nanmean(q), nanstd(q)) for q in quals])
max_seq_len = len(meanquals)
if axe:
ax = axe
fig = axe.get_figure()
ax2 = fig.add_subplot(212)
else: # configure plot
if len(r_enzs) == 1 and r_enzs[0] is None: # do both plots
_, ax = plt.subplots(1, 1, figsize=(15, 6))
else: # only do the quality_plot plot
_, (ax, ax2) = plt.subplots(2, 1, figsize=(15, 12))
ax.patch.set_facecolor('lightgrey')
ax.patch.set_alpha(0.4)
ax.grid(ls='-', color='w', lw=1.5, alpha=0.6, which='major')
ax.grid(ls='-', color='w', lw=1, alpha=0.3, which='minor')
ax.set_axisbelow(True)
# remove tick marks
ax.tick_params(axis='both',
direction='out',
top=False,
right=False,
left=False,
bottom=False)
ax.tick_params(axis='both',
direction='out',
top=False,
right=False,
left=False,
bottom=False,
which='minor')
ax.errorbar(range(max_seq_len),
meanquals,
linewidth=1,
elinewidth=1,
color='darkblue',
yerr=errorquals,
ecolor='orange')
ax.set_xlim((0, max_seq_len))
ax.set_xlabel('Nucleotidic position')
ax.set_ylabel('PHRED score')
ax.set_title('Sequencing Quality (%d reads)' % (nreads))
ax.yaxis.label.set_color('darkblue')
ax.tick_params(axis='y', colors='darkblue', **tkw)
axb = ax.twinx()
# quality_plot plot
axb.plot([henes.count(i) for i in range(max_seq_len)],
linewidth=1,
color='black',
linestyle='--')
axb.yaxis.label.set_color('black')
axb.tick_params(axis='y', colors='black', **tkw)
axb.set_ylabel('Number of "N" per position')
try: # no Ns found (yes... it happens)
axb.set_yscale('log')
axb.set_ylim((0, axb.get_ylim()[1] * 1000))
except ValueError:
axb.set_yscale('linear')
ax.set_ylim((0, ax.get_ylim()[1]))
ax.set_xlim((0, max_seq_len))
# Hi-C plot
if not (len(r_enzs) == 1 and r_enzs[0] is None):
ax.set_title('Sequencing Quality and deconvolution (%s %d reads)' %
(', '.join(map(str, r_enzs)), nreads))
ax.set_xlabel('')
plt.setp(ax.get_xticklabels(), visible=False)
ax2.patch.set_facecolor('lightgrey')
ax2.patch.set_alpha(0.4)
ax2.grid(ls='-', color='w', lw=1.5, alpha=0.6, which='major')
ax2.grid(ls='-', color='w', lw=1, alpha=0.3, which='minor')
ax2.set_axisbelow(True)
ax2.set_xlabel('Nucleotidic position')
# seq_len is the length of the line to plot. we don't want to plot
# if there is no room for the cut-site, or ligation site.
site_len = max((max([len(r_sites[k]) for k in r_sites]),
max([len(l_sites[k]) for k in l_sites]),
max([len(d_sites[k]) for k in d_sites])))
seq_len = max_seq_len - site_len
# transform dictionaries of positions into dictionaries of counts
for r_enz in sites:
sites[r_enz] = [sites[r_enz].count(k)
for k in range(seq_len)] # Undigested
fixes[r_enz] = [fixes[r_enz].count(k)
for k in range(seq_len)] # DE
for r1, r2 in liges:
liges[(r1,
r2)] = [liges[(r1, r2)].count(k)
for k in range(seq_len)] # OK
# in case the pattern of the repaired cut-site contains the target
# cut-site pattern. These sites were counted twice, once in the
# undigested, and once in the repaired. We remove them from the
# repaired:
for r_enz in r_enzs:
if d_sites[r_enz] in r_sites[r_enz]:
pos = r_sites[r_enz].find(d_sites[r_enz])
fixes[r_enz] = (fixes[r_enz][:pos] + [
fixes[r_enz][k] - sites[r_enz][k - pos]
for k in range(pos, seq_len)
])
# same for ligated sites
for r_enz1 in r_enzs:
for r_enz2 in r_enzs:
if d_sites[r_enz1] not in l_sites[(r_enz1, r_enz2)]:
continue
pos = l_sites[(r_enz1, r_enz2)].find(d_sites[r_enz1])
fixes[r_enz1] = (fixes[r_enz1][:pos] + [
fixes[r_enz1][k] - liges[(r_enz1, r_enz2)][k - pos]
for k in range(pos, seq_len)
])
# remove anything that could be in between the two read ends
if paired:
for k in sites:
sites[k][max_seq_len // 2 - site_len:max_seq_len //
2] = [float('nan')] * site_len
fixes[k][max_seq_len // 2 - site_len:max_seq_len //
2] = [float('nan')] * site_len
for k in liges:
liges[k][max_seq_len // 2 - site_len:max_seq_len //
2] = [float('nan')] * site_len
# plot undigested cut-sites
color = iter(plt.cm.Reds(linspace(0.3, 0.95, len(r_enzs))))
for r_enz in sites:
# print 'undigested', r_enz
# print sites[r_enz][:20]
ax2.plot(
sites[r_enz],
linewidth=2,
color=color.next(),
alpha=0.9,
label='Undigested RE site (%s: %s)' %
(r_enz, r_sites[r_enz]) if any([f > 0 for f in fixes[r_enz]])
else 'Undigested & Dangling-Ends (%s: %s)' %
(r_enz, r_sites[r_enz]))
ax2.set_ylabel('Undigested')
ax2.yaxis.label.set_color('darkred')
ax2.tick_params(axis='y', colors='darkred', **tkw)
lines, labels = ax2.get_legend_handles_labels()
ax3 = ax2.twinx()
color = iter(plt.cm.Blues(linspace(0.3, 0.95, len(liges))))
for r1, r2 in liges:
# print 'ligated', r1, r2
# print liges[(r1, r2)][:20]
ax3.plot(liges[(r1, r2)],
linewidth=2,
color=color.next(),
alpha=0.9,
label='Ligated (%s-%s: %s)' %
(r1, r2, l_sites[(r1, r2)].upper()))
ax3.yaxis.label.set_color('darkblue')
ax3.tick_params(axis='y', colors='darkblue', **tkw)
ax3.set_ylabel('Ligated')
tmp_lines, tmp_labels = ax3.get_legend_handles_labels()
lines.extend(tmp_lines)
labels.extend(tmp_labels)
color = iter(plt.cm.Greens(linspace(0.3, 0.95, len(r_enzs))))
for i, r_enz in enumerate(r_enzs):
if any([f > 0 for f in fixes[r_enz]]):
ax4 = ax2.twinx()
ax4.spines["right"].set_position(("axes", 1.07))
make_patch_spines_invisible(ax4)
ax4.spines["right"].set_visible(True)
# print 'repaired', r_enz
# print fixes[r_enz][:20]
ax4.plot(fixes[r_enz],
linewidth=2,
color=color.next(),
alpha=0.9,
label='Dangling-ends (%s: %s)' %
(r_enz, d_sites[r_enz]))
ax4.yaxis.label.set_color('darkgreen')
ax4.tick_params(axis='y', colors='darkgreen', **tkw)
ax4.set_ylabel('Dangling-ends')
tmp_lines, tmp_labels = ax4.get_legend_handles_labels()
lines.extend(tmp_lines)
labels.extend(tmp_labels)
else:
ax2.set_ylabel('Undigested & Dangling-ends')
ax2.set_xlim((0, max_seq_len))
# Count ligation sites
lig_cnt = {}
for k in liges:
lig_cnt[k] = (nansum(liges[k]) - liges[k][0] -
liges[k][max_seq_len // 2])
# Count undigested sites
sit_cnt = {}
for r_enz in r_enzs:
sit_cnt[r_enz] = (nansum(sites[r_enz]) - sites[r_enz][0] -
sites[r_enz][max_seq_len // 2])
# Count Dangling-Ends
des = {}
for r_enz in r_enzs:
if any([f > 0 for f in fixes[r_enz]]):
des[r_enz] = (
(100. *
(fixes[r_enz][0] +
(fixes[r_enz][(max_seq_len // 2)] if paired else 0))) //
nreads)
else:
des[r_enz] = (100. * (sites[r_enz][0] + (sites[r_enz][
(max_seq_len // 2)] if paired else 0))) // nreads
# Decorate plot
title = ''
for r_enz in r_enzs:
lcnt = float(
sum([
lig_cnt[(r_enz1, r_enz2)] * (2 if r_enz1 == r_enz2 else 1)
for r_enz1 in r_enzs for r_enz2 in r_enzs
if r_enz1 == r_enz or r_enz2 == r_enz
]))
title += (
'Percentage of digested sites (not considering Dangling-Ends) '
'%s: %.1f%%\n' % (r_enz, 100. * float(lcnt) /
(lcnt + sit_cnt[r_enz])))
for r_enz in r_enzs:
title += 'Percentage of dangling-ends %s: %.1f%%\n' % (r_enz,
des[r_enz])
for r_enz1 in r_enzs:
for r_enz2 in r_enzs:
title += (
'Percentage of reads with ligation site (%s-%s): %.1f%% \n'
% (r_enz1, r_enz2,
(ligep[(r_enz1, r_enz2)] * 100.) / nreads))
plt.title(title.strip(), size=10, ha='left', x=0)
plt.subplots_adjust(right=0.85)
ax2.legend(lines,
labels,
bbox_to_anchor=(0.75, 1.0),
loc=3,
borderaxespad=0.,
frameon=False,
fontsize=9)
plt.tight_layout()
if savefig:
tadbit_savefig(savefig)
plt.close('all')
elif not axe:
plt.show()
for k in ligep:
ligep[k] = (ligep[k] * 100.) / nreads
if len(r_enzs) == 1 and r_enzs[0] is None:
return {}, {}
return des, ligep
def run(opts):
check_options(opts)
launch_time = time.localtime()
param_hash = digest_parameters(opts, extra=['quiet'])
if opts.zrange:
vmin = float(opts.zrange.split(',')[0])
vmax = float(opts.zrange.split(',')[1])
else:
vmin = vmax = None
if opts.figsize:
opts.figsize = map(float, opts.figsize.split(','))
else:
vmin = vmax = None
clean = True # change for debug
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.')
biases = opts.biases
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 = 1500**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 full genome\n')
out_files = {}
out_plots = {}
if opts.matrix or opts.plot:
bamfile = AlignmentFile(mreads, 'rb')
sections = OrderedDict(zip(bamfile.references,
[x for x in bamfile.lengths]))
total = 0
section_pos = OrderedDict()
for crm in sections:
section_pos[crm] = (total, total + sections[crm])
total += sections[crm]
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)) if biases and norm != 'raw' else None,
normalization=norm,
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)
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' % (row_names.next()) +
'\t'.join(str(matrix.get((i, j), 0))
for i in xrange(b1, e1))
for j in xrange(b2, e2)) + '\n')
else:
out.write('\n'.join('\t'.join(str(matrix.get((i, j), 0))
for i in xrange(b1, e1))
for j in xrange(b2, e2)) + '\n')
out.close()
if opts.plot:
# transform matrix
matrix = array([array([matrix.get((i, j), 0)
for i in xrange(b1, e1)])
for j in xrange(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' % (
norm, 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)
ax1, _ = plot_HiC_matrix(
matrix, triangular=opts.triangular,
vmin=vmin, vmax=vmax, cmap=opts.cmap,
figsize=opts.figsize,
bad_color=opts.bad_color if norm != 'raw' else None)
ax1.set_title('Region: %s, normalization: %s, resolution: %s' % (
name, norm, nicer(opts.reso)), y=1.05)
_format_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)) 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, clean=clean))
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 quality_plot(fnam, r_enz=None, nreads=None, axe=None, savefig=None, paired=False):
"""
Plots the sequencing quality of a given FASTQ file. If a restrinction enzyme
(RE) name is provided, can also represent the distribution of digested and
undigested RE sites and estimate an expected proportion of dangling-ends.
Proportion of dangling-ends is inferred by counting the number of times a
dangling-end site, is found at the beginning of any of the reads (divided by
the number of reads).
:param fnam: path to FASTQ file
:param None nreads: max number of reads to read, not necesary to read all
:param None savefig: path to a file where to save the image generated;
if None, the image will be shown using matplotlib GUI (the extension
of the file name will determine the desired format).
:param False paired: is input FASTQ contains both ends
:returns: the percentage of dangling-ends (sensu stricto) and the percentage of
reads with at least a ligation site.
"""
phred = dict([(c, i) for i, c in enumerate(
'!"#$%&\'()*+,-./0123456789:;<=>?@ABCDEFGHIJKLMNOPQRSTUVWXYZ[\]^_`abcdefghijklmnopqrstuvwxyz{|}~')])
quals = []
henes = []
sites = []
fixes = []
liges = []
ligep = 0
tkw = dict(size=4, width=1.5)
if fnam.endswith('.gz'):
fhandler = gopen(fnam)
elif fnam.endswith('.dsrc'):
proc = Popen(['dsrc', 'd', '-t8', '-s', fnam], stdout=PIPE)
fhandler = proc.stdout
else:
fhandler = open(fnam)
if not r_enz:
if nreads:
while True:
try:
next(fhandler)
except EOFError:
break
seq = next(fhandler)
if 'N' in seq:
henes.extend([i for i, s in enumerate(seq) if s == 'N'])
next(fhandler)
line = next(fhandler)
quals.append([phred[i] for i in line.strip()])
if len(quals) > nreads:
break
else: # do this because it's faster
while True:
try:
next(fhandler)
except EOFError:
break
seq = next(fhandler)
if 'N' in seq:
henes.extend([i for i, s in enumerate(seq) if s == 'N'])
next(fhandler)
line = next(fhandler)
quals.append([phred[i] for i in line.strip()])
else:
r_site = RESTRICTION_ENZYMES[r_enz].replace('|', '')
l_site = religated(r_enz)
d_site = repaired(r_enz)
if r_site*2 == l_site:
# in case the religated site equals 2 restriction sites (like DnpII)
site = re.compile('(?<!%s)' % r_site + r_site + '(?!%s)' % r_site)
fixe = re.compile('(?<!%s)' % d_site + d_site + '(?!%s)' % d_site)
else:
site = re.compile(r_site)
fixe = re.compile(d_site)
lige = re.compile(l_site)
if nreads:
while True:
try:
next(fhandler)
except StopIteration:
break
seq = next(fhandler)
sites.extend([m.start() for m in site.finditer(seq)])
fixes.extend([m.start() for m in fixe.finditer(seq)])
liges.extend([m.start() for m in lige.finditer(seq)])
ligep += l_site in seq
if 'N' in seq:
henes.extend([i for i, s in enumerate(seq) if s == 'N'])
next(fhandler)
line = next(fhandler)
quals.append([phred[i] for i in line.strip()])
if len(quals) > nreads:
break
else: # do this because it's faster
while True:
try:
next(fhandler)
except StopIteration:
break
seq = next(fhandler)
sites.extend([m.start() for m in site.finditer(seq)])
fixes.extend([m.start() for m in fixe.finditer(seq)])
liges.extend([m.start() for m in lige.finditer(seq)])
ligep += l_site in seq
if 'N' in seq:
henes.extend([i for i, s in enumerate(seq) if s == 'N'])
next(fhandler)
line = next(fhandler)
quals.append([phred[i] for i in line.strip()])
fhandler.close()
if not nreads:
nreads = len(quals)
quals = zip(*quals)
meanquals = [np.mean(q) for q in quals]
errorquals = [np.std(q) for q in quals]
if axe:
ax = axe
fig = axe.get_figure()
ax2 = fig.add_subplot(212)
else:
if r_enz:
_, (ax, ax2) = plt.subplots(2,1, figsize=(15, 12))
else:
_, ax = plt.subplots(1,1, figsize=(15, 6))
ax.patch.set_facecolor('lightgrey')
ax.patch.set_alpha(0.4)
ax.grid(ls='-', color='w', lw=1.5, alpha=0.6, which='major')
ax.grid(ls='-', color='w', lw=1, alpha=0.3, which='minor')
ax.set_axisbelow(True)
# remove tick marks
ax.tick_params(axis='both', direction='out', top=False, right=False,
left=False, bottom=False)
ax.tick_params(axis='both', direction='out', top=False, right=False,
left=False, bottom=False, which='minor')
ax.errorbar(range(len(line.strip())), meanquals,
linewidth=1, elinewidth=1, color='darkblue',
yerr=errorquals, ecolor='orange')
ax.set_xlim((0, len(line)))
ax.set_xlabel('Nucleotidic position')
ax.set_ylabel('PHRED score')
ax.set_title('Sequencing Quality (%d reads)' % (nreads))
ax.yaxis.label.set_color('darkblue')
ax.tick_params(axis='y', colors='darkblue', **tkw)
axb = ax.twinx()
axb.plot([henes.count(i) for i in xrange(len(line))], linewidth=1,
color='black', linestyle='--')
axb.yaxis.label.set_color('black')
axb.tick_params(axis='y', colors='black', **tkw)
axb.set_ylabel('Number of "N" per position')
try: # no Ns found (yes... it happens)
axb.set_yscale('log')
axb.set_ylim((0, axb.get_ylim()[1] * 1000))
except ValueError:
axb.set_yscale('linear')
ax.set_ylim((0, ax.get_ylim()[1]))
ax.set_xlim((0, len(line)))
if r_enz:
ax.set_title('Sequencing Quality and deconvolution (%s %d reads)' % (
r_enz, nreads))
ax.set_xlabel('')
plt.setp(ax.get_xticklabels(), visible=False)
ax2.patch.set_facecolor('lightgrey')
ax2.patch.set_alpha(0.4)
ax2.grid(ls='-', color='w', lw=1.5, alpha=0.6, which='major')
ax2.grid(ls='-', color='w', lw=1, alpha=0.3, which='minor')
ax2.set_axisbelow(True)
ax2.set_xlabel('Nucleotidic position')
seq_len = len(line) - max((len(r_site), len(l_site), len(d_site)))
sites = [sites.count(k) for k in xrange(seq_len)] # Undigested
liges = [liges.count(k) for k in xrange(seq_len)] # OK
fixes = [fixes.count(k) for k in xrange(seq_len)] # DE
if d_site in r_site:
pos = r_site.find(d_site)
fixes = (fixes[:pos] +
[fixes[k] - sites[k-pos] for k in xrange(pos, seq_len)])
if d_site in l_site:
pos = l_site.find(d_site)
fixes = (fixes[:pos] +
[fixes[k] - liges[k-pos] for k in xrange(pos, seq_len)])
site_len = max((len(r_site), len(l_site), len(d_site)))
if paired:
sites[len(line) / 2 - site_len:
len(line) / 2] = [float('nan')] * site_len
liges[len(line) / 2 - site_len:
len(line) / 2] = [float('nan')] * site_len
fixes[len(line) / 2 - site_len:
len(line) / 2] = [float('nan')] * site_len
ax2.plot(sites, linewidth=2, color='darkred')
ax2.set_ylabel('Undigested RE site (%s)' % r_site)
ax2.yaxis.label.set_color('darkred')
ax2.tick_params(axis='y', colors='darkred', **tkw)
ax3 = ax2.twinx()
ax3.plot(liges, linewidth=2, color='darkblue')
ax3.yaxis.label.set_color('darkblue')
ax3.tick_params(axis='y', colors='darkblue', **tkw)
ax3.set_ylabel('Religated (%s)' % l_site)
if any([f > 0 for f in fixes]):
ax4 = ax2.twinx()
ax4.spines["right"].set_position(("axes", 1.07))
make_patch_spines_invisible(ax4)
ax4.spines["right"].set_visible(True)
ax4.plot(fixes, linewidth=2, color='darkorange')
ax4.yaxis.label.set_color('darkorange')
ax4.tick_params(axis='y', colors='darkorange', **tkw)
ax4.set_ylabel('Dangling-ends (%s)' % d_site)
else:
ax2.set_ylabel('RE site & Dangling-ends (%s)' % r_site)
ax2.set_xlim((0, len(line)))
lig_cnt = (np.nansum(liges) - liges[0] - liges[len(line) / 2])
sit_cnt = (np.nansum(sites) - sites[0] - sites[len(line) / 2])
des = ((100. * (fixes[0] + (fixes[(len(line) / 2)]
if paired else 0)))
/ nreads) if any([f > 0 for f in fixes]) else (
100. * (sites[0] + (sites[(len(line) / 2)] if paired else 0))) / nreads
plt.title(('Percentage of digested sites: %.0f%%, of dangling-ends: %.0f%%\n' +
'Percentage of reads with ligation site: %.0f%%') %(
(100. * lig_cnt) / (lig_cnt + sit_cnt),
des,
(ligep * 100.) / nreads))
plt.subplots_adjust(right=0.85)
if savefig:
tadbit_savefig(savefig)
plt.close('all')
elif not axe:
plt.show()
return des, (ligep * 100.) / nreads
def quality_plot(fnam, nreads=None, axe=None, savefig=None):
"""
Plot the qualities
:param fnam: path to FASTQ file
:param None nreads: max number of reads to read, not necesary to read all
:param None savefig: path to a file where to save the image generated;
if None, the image will be shown using matplotlib GUI (the extension
of the file name will determine the desired format).
"""
phred = '!"#$%&\'()*+,-./0123456789:;<=>?@ABCDEFGHIJKLMNOPQRSTUVWXYZ[\]^_`abcdefghijklmnopqrstuvwxyz{|}~'
quals = []
if fnam.endswith('.gz'):
fhandler = gopen(fnam)
else:
fhandler = open(fnam)
if nreads:
while True:
try:
next(fhandler)
except EOFError:
break
next(fhandler)
next(fhandler)
line = next(fhandler)
quals.append([phred.index(i) for i in line.strip()])
if len(quals) > nreads:
break
else: # do this because it's faster
while True:
try:
next(fhandler)
except EOFError:
break
next(fhandler)
next(fhandler)
next(fhandler)
line = next(fhandler)
quals.append([phred.index(i) for i in line.strip()])
fhandler.close()
quals = zip(*quals)
meanquals = [np.mean(q) for q in quals]
errorquals = [np.std(q) for q in quals]
if axe:
ax = axe
fig = axe.get_figure()
plt.clf()
else:
fig = plt.figure()
plt.clf()
ax = fig.add_subplot(111)
ax.patch.set_facecolor('lightgrey')
ax.patch.set_alpha(0.4)
ax.grid(ls='-', color='w', lw=1.5, alpha=0.6, which='major')
ax.grid(ls='-', color='w', lw=1, alpha=0.3, which='minor')
ax.set_axisbelow(True)
# remove tick marks
ax.tick_params(axis='both', direction='out', top=False, right=False,
left=False, bottom=False)
ax.tick_params(axis='both', direction='out', top=False, right=False,
left=False, bottom=False, which='minor')
plt.figure(figsize=(15, 7))
plt.errorbar(range(len(line.strip())), meanquals,
yerr=errorquals, ecolor='orange')
plt.xlim((0, len(line)))
plt.xlabel('Sequence')
plt.ylabel('PHRED score')
if savefig:
tadbit_savefig(savefig)
elif not axe:
plt.show()
def mmp_score(matrix, nrand=10, verbose=False, savefig=None):
"""
:param matrix: list of lists
:param 10 nrand: number of randomizations
:param None savefig: path where to save figure
:returns: 1- MMP score which ranges from 0 (bad) to 1 (good), and 2- the
expected correlation of the contact matrices of the modeled chromatin
with the original Hi-C data (plus the 3- lower and 4- upper values
expected in 95% of the cases)
"""
data = np.array([np.array([v for v in l]) for l in matrix])
if verbose:
sys.stdout.write(' - getting EigenVectors\n')
egval, _ = np.linalg.eigh(data)
# sort eigenvalues/vectors
idx = (-egval).argsort()
egval = egval[idx]
regvals = []
if verbose:
sys.stdout.write(' - randomization\n')
for i in xrange(int(nrand)):
if verbose:
sys.stdout.write('\r ' + str(i + 1) + ' / ' + str(nrand))
sys.stdout.flush()
regval, _ = np.linalg.eigh(randomize_matrix(data))
regval = [abs(j) for j in regval]
regval.sort(reverse=True)
regvals.append( regval)
if verbose:
sys.stdout.write('\n')
regvals = zip(*regvals)
rvmean = []
for rv in regvals:
rvmean.append(np.mean(rv))
total = sum(rvmean)/100
rvmean = [i/total for i in rvmean]
err = []
for rv in regvals:
rvstd = np.std(rv/total)
err.append(2 * rvstd)
zdata = sorted(np.log2([data[i][j] for i in xrange(len(data))
for j in xrange(i, len(data)) if data[i][j]]))
skewness = skew(zdata)
kurtness = kurtosis(zdata)
if savefig:
_ = plt.figure(figsize=(14, 8))
gs = gridspec.GridSpec(7, 5, wspace=0.5, hspace=1.5)
ax1 = plt.subplot(gs[: , 0:3])
ax2 = plt.subplot(gs[1:5 , 3: ])
ax3 = plt.subplot(gs[5:7 , 3: ])
img = ax2.imshow(np.log2(data), interpolation='none')
plt.colorbar(img, ax=ax2)
if savefig:
ax2.set_title('Original matrix', size=12)
ax2.tick_params(axis='both', which='major', labelsize=10)
ax2.set_xlabel('Bin')
ax2.set_ylabel('Bin')
normfit = sc_norm.pdf(zdata, np.mean(zdata), np.std(zdata))
_ = ax3.plot(zdata, normfit, ':o', color='grey', ms=3, alpha=.4,
markersize=.5)
ax3.tick_params(axis='both', which='major', labelsize=10)
ax3.hist(zdata, bins=20, density=True, alpha=0.7, color='r')
ax3.set_xlabel('Z-score')
ax3.set_ylabel('Frequency')
rcParams['xtick.direction'] = 'out'
rcParams['ytick.direction'] = 'out'
rcParams['axes.axisbelow'] = True
rcParams['xtick.direction'] = 'out'
rcParams['ytick.direction'] = 'out'
rcParams['axes.axisbelow'] = True
rcParams['axes.grid'] = True
rcParams['grid.color'] = 'w'
rcParams['grid.linestyle'] = '-'
rcParams['grid.linewidth'] = 2
# rcParams['grid.alpha'] = .3
ax1.minorticks_on()
ax1.grid(ls='-', color='w', alpha=.3, lw=2, which='major')
ax1.grid(ls='-', b=True, color='w', alpha=.3, lw=1, which='minor')
ax1.spines['top'].set_color('none')
ax1.spines['right'].set_color('none')
ax1.spines['bottom'].set_color('none')
ax1.spines['left'].set_color('none')
ax1.xaxis.set_ticks_position('bottom')
ax1.yaxis.set_ticks_position('left')
ax1.set_xscale('log')
try:
ax1.set_axis_bgcolor((.9,.9,.9))
except AttributeError:
ax1.set_facecolor((.9,.9,.9))
ax1.errorbar(range(1, 1 + len(rvmean)), rvmean, yerr=err, ecolor='red',
color='orange', lw=2,
label='%s randomizations' % (nrand))
total = sum(abs(egval)) / 100
egval = np.array(sorted([e/total for e in abs(egval)], reverse=True))
for i in xrange(len(rvmean)):
if rvmean[i] + err[i] > egval[i]:
break
signifidx = i
size = len(data)
sev = sum(egval[:signifidx]-rvmean[:signifidx])
if savefig:
ax1.plot(range(1, 1 + len(rvmean)), egval,
color='green', lw=2, label='Observed data')
ax1.fill_between(range(1, 1 + len(rvmean)), rvmean, egval,
where=(np.array(rvmean) + np.array(err))<egval,
facecolor='green', interpolate=True, alpha=0.2)
ax1.fill_between(range(1, 1 + len(rvmean)), rvmean, egval,
where=(np.array(rvmean) + np.array(err))>egval,
facecolor='red' , interpolate=True, alpha=0.2)
ax1.set_xlim((0,len(rvmean)))
ax1.set_ylim((0, max(max(rvmean), max(egval))))
ax1.legend(frameon=False, loc='upper right', prop={'size': 10})
ax1.set_xlabel('Log indexes of Eigenvalues')
ax1.set_ylabel('Eigenvalues (percentage of total)')
#plt.subplots_adjust(right=0.6)
#img = Image.open(opts.outdir + '/matrix_small.png')
#fig.figimage(img, 640, -160)
minv = float(min([i for d in data for i in d if i])) / 2
if minv == 0.5:
minv = 1./(len(data)**2)
mmp = -0.0002 * size + 0.0335 * skewness - 0.0229 * kurtness + 0.0069 * sev + 0.8126
if verbose:
sys.stdout.write('\n')
sys.stdout.write('\n Results\n')
sys.stdout.write(' -------\n\n')
if verbose:
sys.stdout.write(' MMP score: %.4f\n\n' % mmp)
ex_a1, ex_b1 = [0.6975926, 0.2548171]
supa1, supb1 = [0.69300732000423904, 0.29858572176099613]
lowa1, lowb1 = [0.70217788900976075, 0.211048473299004]
scc = (mmp - ex_b1 ) / ex_a1
scc_up1 = (mmp - supb1 ) / supa1
scc_lw1 = (mmp - lowb1 ) / lowa1
if verbose:
sys.stdout.write((' predicted dSCC is %.3f (%.3f-%.3f '
'68%% confidence)\n') % (scc , scc_up1 , scc_lw1 ))
supa75, supb75 = [0.69230778430383244, 0.30526310790548261]
lowa75, lowb75 = [0.70287742471016734, 0.20437108715451746]
scc_up75 = (mmp - supb75 ) / supa75
scc_lw75 = (mmp - lowb75 ) / lowa75
if verbose:
sys.stdout.write((' (%.3f-%.3f '
'75%% confidence)\n') % (scc_up75 , scc_lw75 ))
supa2, supb2 = [0.68855373600821357, 0.34109720480765293]
lowa2, lowb2 = [0.70663147300578644, 0.16853699025234709]
scc_up2 = (mmp - supb2 ) / supa2
scc_lw2 = (mmp - lowb2 ) / lowa2
if verbose:
sys.stdout.write((' (%.3f-%.3f '
'95%% confidence)\n') % (scc_up2 , scc_lw2 ))
if savefig:
# write the log
log = ''
log += ' 1- Matrix size (number of eigenvalues): %s\n' % (len(egval))
log += " 2- Skewness of the distribution: %0.3f\n" % (skewness)
log += " 3- Kurtosis of the distribution: %0.3f\n" % (kurtness)
log += " 4- Sum of differences signif EV real-rand: %0.3f\n\n" % (sev)
plt.figtext(0.62, 0.77, log, size='small')
log = "MMP score: %.3f\n" % (mmp)
log += "Predicted dSCC: %.3f (%.3f-%.3f at 95%% conf)\n" % (scc, scc_up2, scc_lw2)
plt.figtext(0.61, 0.87, log, size=12)
tadbit_savefig(savefig)
plt.close('all')
return mmp, scc, scc_up2 , scc_lw2
def run(opts):
check_options(opts)
launch_time = time.localtime()
param_hash = digest_parameters(opts, extra=['quiet'])
if opts.zrange:
vmin = float(opts.zrange.split(',')[0])
vmax = float(opts.zrange.split(',')[1])
else:
vmin = vmax = None
clean = True # change for debug
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.')
biases = opts.biases
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
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 full genome\n')
out_files = {}
out_plots = {}
if opts.matrix or opts.plot:
bamfile = AlignmentFile(mreads, 'rb')
sections = OrderedDict(
zip(bamfile.references, [x for x in bamfile.lengths]))
total = 0
section_pos = dict()
for crm in sections:
section_pos[crm] = (total, total + sections[crm])
total += sections[crm]
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)) if biases and norm != 'raw' else None,
normalization=norm,
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)
except NotImplementedError:
if norm == "raw&decay":
warn('WARNING: raw&decay normalization not implemeted 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).replace(' ', ''), ('_' + 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' %
(row_names.next()) + '\t'.join(
str(matrix.get((i, j), 0))
for i in xrange(b1, e1))
for j in xrange(b2, e2)) + '\n')
else:
out.write('\n'.join('\t'.join(
str(matrix.get((i, j), 0)) for i in xrange(b1, e1))
for j in xrange(b2, e2)) + '\n')
out.close()
if opts.plot:
cmap = plt.get_cmap(opts.cmap)
if norm != 'raw':
cmap.set_bad('grey', 1.)
printime(' - Plotting: %s' % norm)
fnam = '%s_%s_%s%s.%s' % (norm, name, nicer(opts.reso).replace(
' ', ''), ('_' + param_hash), opts.format)
out_plots[norm_string] = path.join(outdir, fnam)
if opts.interactive:
_ = plt.figure(figsize=(8, 7))
else:
_ = plt.figure(figsize=(16, 14))
# ax1 = plt.subplot(111)
ax1 = plt.axes([0.1, 0.1, 0.7, 0.8])
ax2 = plt.axes([0.82, 0.1, 0.07, 0.8])
matrix = array([
array([matrix.get((i, j), 0) for i in xrange(b1, e1)])
for j in xrange(b2, e2)
])
mini = np_min(matrix[nonzero(matrix)]) / 2.
matrix[matrix == 0] = mini
m = zeros_like(matrix)
for bad1 in bads1:
m[:, bad1] = 1
for bad2 in bads2:
m[bad2, :] = 1
matrix = log2(ma.masked_array(matrix, m))
ax1.imshow(matrix,
interpolation='None',
origin='lower',
cmap=cmap,
vmin=vmin,
vmax=vmax)
if len(regions) <= 2:
pltbeg1 = 0 if start1 is None else start1
pltend1 = sections[regions[0]] if end1 is None else end1
pltbeg2 = pltbeg1 if len(
regions) == 1 else 0 if start2 is None else start2
pltend2 = pltend1 if len(regions) == 1 else sections[
regions[-1]] if end2 is None else end2
ax1.set_xlabel('{}:{:,}-{:,}'.format(
regions[0], pltbeg1 if pltbeg1 else 1, pltend1))
ax1.set_ylabel('{}:{:,}-{:,}'.format(
regions[-1], pltbeg2 if pltbeg2 else 1, pltend2))
def format_xticks(tickstring, _=None):
tickstring = int(tickstring * opts.reso + pltbeg1)
return nicer(tickstring if tickstring else 1,
coma=True)
def format_yticks(tickstring, _=None):
tickstring = int(tickstring * opts.reso + pltbeg2)
return nicer(tickstring if tickstring else 1,
coma=True)
ax1.xaxis.set_major_formatter(FuncFormatter(format_xticks))
ax1.yaxis.set_major_formatter(FuncFormatter(format_yticks))
labels = ax1.get_xticklabels()
plt.setp(labels, rotation=-25, ha='left')
ax1.set_xlim(-0.5, len(matrix[0]) - 0.5)
ax1.set_ylim(-0.5, len(matrix) - 0.5)
else:
vals = [0]
keys = ['']
for crm in regions:
vals.append(section_pos[crm][0] / opts.reso)
keys.append(crm)
vals.append(section_pos[crm][1] / opts.reso)
ax1.set_yticks(vals)
ax1.set_yticklabels('')
ax1.set_yticks([
float(vals[i] + vals[i + 1]) / 2
for i in xrange(len(vals) - 1)
],
minor=True)
ax1.set_yticklabels(keys, minor=True)
for t in ax1.yaxis.get_minor_ticks():
t.tick1On = False
t.tick2On = False
ax1.set_xticks(vals)
ax1.set_xticklabels('')
ax1.set_xticks([
float(vals[i] + vals[i + 1]) / 2
for i in xrange(len(vals) - 1)
],
minor=True)
ax1.set_xticklabels(keys, minor=True)
for t in ax1.xaxis.get_minor_ticks():
t.tick1On = False
t.tick2On = False
ax1.set_xlabel('Chromosomes')
ax1.set_ylabel('Chromosomes')
ax1.set_xlim(-0.5, len(matrix[0]) - 0.5)
ax1.set_ylim(-0.5, len(matrix) - 0.5)
data = [i for d in matrix for i in d if isfinite(i)]
mindata = nanmin(data)
maxdata = nanmax(data)
gradient = linspace(maxdata, mindata,
max((len(matrix), len(matrix[0]))))
gradient = dstack((gradient, gradient))[0]
h = ax2.hist(data,
color='darkgrey',
linewidth=2,
orientation='horizontal',
bins=50,
histtype='step',
normed=True)
_ = ax2.imshow(gradient,
aspect='auto',
cmap=cmap,
extent=(0, max(h[0]), mindata, maxdata))
ax2.yaxis.tick_right()
ax2.yaxis.set_label_position("right")
ax2.set_xticks([])
ax1.set_title('Region: %s, normalization: %s, resolution: %s' %
(name, norm, nicer(opts.reso)))
ax2.set_ylabel('Hi-C Log2 interactions', rotation=-90)
ax2.set_xlabel('Count')
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)) 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,
clean=clean))
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 eig_correlate_matrices(hic_data1, hic_data2, nvect=6,
savefig=None, show=False, savedata=None, **kwargs):
"""
Compare the iteractions of two Hi-C matrices using their 6 first
eigenvectors, with spearman rank correlation
:param hic_data1: Hi-C-data object
:param hic_data2: Hi-C-data object
:param 6 nvect: number of eigenvectors to compare
:param None savefig: path to save the plot
:param False show: displays the plot
:param kwargs: any argument to pass to matplotlib imshow function
:returns: matrix of correlations
"""
data1 = hic_data1.get_matrix()
data2 = hic_data2.get_matrix()
# get the log
size = len(data1)
data1 = nozero_log(data1, np.log2)
data2 = nozero_log(data2, np.log2)
# get the eigenvectors
ev1, evect1 = eigh(data1)
ev2, evect2 = eigh(data2)
corr = [[0 for _ in xrange(nvect)] for _ in xrange(nvect)]
# sort eigenvectors according to their eigenvalues => first is last!!
sort_perm = ev1.argsort()
ev1.sort()
evect1 = evect1[sort_perm]
sort_perm = ev2.argsort()
ev2.sort()
evect2 = evect2[sort_perm]
# calculate Pearson correlation
for i in xrange(nvect):
for j in xrange(nvect):
corr[i][j] = abs(pearsonr(evect1[:,-i-1],
evect2[:,-j-1])[0])
# plot
axe = plt.axes([0.1, 0.1, 0.6, 0.8])
cbaxes = plt.axes([0.85, 0.1, 0.03, 0.8])
if show or savefig:
im = axe.imshow(corr, interpolation="nearest",origin='lower', **kwargs)
axe.set_xlabel('Eigen Vectors exp. 1')
axe.set_ylabel('Eigen Vectors exp. 2')
axe.set_xticks(range(nvect))
axe.set_yticks(range(nvect))
axe.set_xticklabels(range(1, nvect + 2))
axe.set_yticklabels(range(1, nvect + 2))
axe.xaxis.set_tick_params(length=0, width=0)
axe.yaxis.set_tick_params(length=0, width=0)
cbar = plt.colorbar(im, cax = cbaxes )
cbar.ax.set_ylabel('Pearson correlation', rotation=90*3,
verticalalignment='bottom')
axe2 = axe.twinx()
axe2.set_yticks(range(nvect))
axe2.set_yticklabels(['%.1f' % (e) for e in ev2[-nvect:][::-1]])
axe2.set_ylabel('corresponding Eigen Values exp. 2', rotation=90*3,
verticalalignment='bottom')
axe2.set_ylim((-0.5, nvect - 0.5))
axe2.yaxis.set_tick_params(length=0, width=0)
axe3 = axe.twiny()
axe3.set_xticks(range(nvect))
axe3.set_xticklabels(['%.1f' % (e) for e in ev1[-nvect:][::-1]])
axe3.set_xlabel('corresponding Eigen Values exp. 1')
axe3.set_xlim((-0.5, nvect - 0.5))
axe3.xaxis.set_tick_params(length=0, width=0)
axe.set_ylim((-0.5, nvect - 0.5))
axe.set_xlim((-0.5, nvect - 0.5))
if savefig:
tadbit_savefig(savefig)
if show:
plt.show()
plt.close('all')
if savedata:
out = open(savedata, 'w')
out.write('# ' + '\t'.join(['Eigen Vector %s'% i
for i in xrange(nvect)]) + '\n')
for i in xrange(nvect):
out.write('\t'.join([str(corr[i][j])
for j in xrange(nvect)]) + '\n')
out.close()
return corr
def eig_correlate_matrices(hic_data1, hic_data2, nvect=6,
savefig=None, show=False, savedata=None):
"""
Compare the iteractions of two Hi-C matrices using their 6 first
eigenvectors, with spearman rank correlation
:param hic_data1: Hi-C-data object
:param hic_data2: Hi-C-data object
:param 6 nvect: number of eigenvectors to compare
:param None savefig: path to save the plot
:param False show: displays the plot
:returns: matrix of correlations
"""
corr = []
ev1, evect1 = eigh(np.array([[hic_data1[i, j]
for j in xrange(len(hic_data1))]
for i in xrange(len(hic_data1))]))
ev2, evect2 = eigh(np.array([[hic_data2[i, j]
for j in xrange(len(hic_data2))]
for i in xrange(len(hic_data2))]))
corr = [[0 for _ in xrange(nvect)] for _ in xrange(nvect)]
sort_perm = ev1.argsort()
ev1.sort()
evect1 = evect1[sort_perm][::-1]
sort_perm = ev2.argsort()
ev2.sort()
evect2 = evect2[sort_perm][::-1]
for i in xrange(nvect):
for j in xrange(nvect):
corr[i][j] = abs(pearsonr(evect1[:,i],
evect2[:,j])[0])
axe = plt.axes([0.1, 0.1, 0.6, 0.8])
cbaxes = plt.axes([0.85, 0.1, 0.03, 0.8])
if show or savefig:
im = axe.imshow(corr, interpolation="nearest",origin='lower')
axe.set_xlabel('Eigen Vectors exp. 1')
axe.set_ylabel('Eigen Vectors exp. 2')
axe.set_xticklabels(range(nvect + 1), range(1, nvect + 2))
axe.set_yticklabels(range(nvect + 1), range(1, nvect + 2))
axe.xaxis.set_tick_params(length=0, width=0)
axe.yaxis.set_tick_params(length=0, width=0)
cbar = plt.colorbar(im, cax = cbaxes )
cbar.ax.set_ylabel('Pearson correlation', rotation=90*3,
verticalalignment='bottom')
axe2 = axe.twinx()
axe2.set_yticks(range(nvect))
axe2.set_yticklabels(['%.1f' % (e) for e in ev2[-nvect:][::-1]])
axe2.set_ylabel('corresponding Eigen Values exp. 2', rotation=90*3,
verticalalignment='bottom')
axe2.set_ylim((-0.5, nvect - 0.5))
axe2.yaxis.set_tick_params(length=0, width=0)
axe3 = axe.twiny()
axe3.set_xticks(range(nvect))
axe3.set_xticklabels(['%.1f' % (e) for e in ev1[-nvect:][::-1]])
axe3.set_xlabel('corresponding Eigen Values exp. 1')
axe3.set_xlim((-0.5, nvect - 0.5))
axe3.xaxis.set_tick_params(length=0, width=0)
axe.set_ylim((-0.5, nvect - 0.5))
axe.set_xlim((-0.5, nvect - 0.5))
if savefig:
tadbit_savefig(savefig)
if show:
plt.show()
plt.close('all')
if savedata:
out = open(savedata, 'w')
out.write('# ' + '\t'.join(['Eigen Vector %s'% i
for i in xrange(nvect)]) + '\n')
for i in xrange(nvect):
out.write('\t'.join([str(corr[i][j])
for j in xrange(nvect)]) + '\n')
out.close()
return corr
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')
def draw_map(
data,
genome_seq,
cumcs,
savefig,
show,
one=False,
clim=None,
cmap="jet",
decay=False,
perc=10,
name=None,
cistrans=None,
decay_resolution=10000,
normalized=False,
max_diff=None,
):
_ = plt.figure(figsize=(15.0, 12.5))
if not max_diff:
max_diff = len(data)
ax1 = plt.axes([0.34, 0.08, 0.6, 0.7205])
ax2 = plt.axes([0.07, 0.65, 0.21, 0.15])
if decay:
ax3 = plt.axes([0.07, 0.42, 0.21, 0.15])
plot_distance_vs_interactions(
data, genome_seq=genome_seq, axe=ax3, resolution=decay_resolution, max_diff=max_diff, normalized=normalized
)
ax4 = plt.axes([0.34, 0.805, 0.6, 0.04], sharex=ax1)
ax5 = plt.axes([0.34, 0.845, 0.6, 0.04], sharex=ax1)
ax6 = plt.axes([0.34, 0.885, 0.6, 0.04], sharex=ax1)
try:
minoridata = np.nanmin(data)
maxoridata = np.nanmax(data)
except AttributeError:
vals = [i for d in data for i in d if not np.isnan(i)]
minoridata = np.min(vals)
maxoridata = np.max(vals)
totaloridata = np.nansum([data[i][j] for i in xrange(len(data)) for j in xrange(i, len(data))])
data = nozero_log(data, np.log2)
vals = np.array([i for d in data for i in d])
vals = vals[np.isfinite(vals)]
mindata = np.nanmin(vals)
maxdata = np.nanmax(vals)
diff = maxdata - mindata
posI = 0.01 if not clim else (float(clim[0]) / diff) if clim[0] != None else 0.01
posF = 1.0 if not clim else (float(clim[1]) / diff) if clim[1] != None else 1.0
if cmap == "tadbit":
cuts = perc
cdict = {"red": [(0.0, 0.0, 0.0)], "green": [(0.0, 0.0, 0.0)], "blue": [(0.0, 0.5, 0.5)]}
prev_pos = 0
median = (np.median(vals) - mindata) / diff
for prc in np.linspace(posI, median, cuts / 2, endpoint=False):
try:
pos = (np.percentile(vals, prc * 100.0) - mindata) / diff
prc = ((prc - posI) / (median - posI)) + 1.0 / cuts
except ValueError:
pos = prc = 0
if prev_pos >= pos:
continue
cdict["red"].append([pos, prc, prc])
cdict["green"].append([pos, prc, prc])
cdict["blue"].append([pos, 1, 1])
prev_pos = pos
for prc in np.linspace(median + 1.0 / cuts, posF, cuts / 2, endpoint=False):
try:
pos = (np.percentile(vals, prc * 100.0) - mindata) / diff
prc = (prc - median) / (posF - median)
except ValueError:
pos = prc = 0
if prev_pos >= pos:
continue
cdict["red"].append([pos, 1.0, 1.0])
cdict["green"].append([pos, 1 - prc, 1 - prc])
cdict["blue"].append([pos, 1 - prc, 1 - prc])
prev_pos = pos
pos = (np.percentile(vals, 97.0) - mindata) / diff
cdict["red"].append([pos, 0.1, 0.1])
cdict["green"].append([pos, 0, 0])
cdict["blue"].append([pos, 0, 0])
cdict["red"].append([1.0, 1, 1])
cdict["green"].append([1.0, 1, 1])
cdict["blue"].append([1.0, 0, 0])
cmap = LinearSegmentedColormap(cmap, cdict)
clim = None
else:
cmap = plt.get_cmap(cmap)
cmap.set_bad("darkgrey", 1)
ax1.imshow(data, interpolation="none", cmap=cmap, vmin=clim[0] if clim else None, vmax=clim[1] if clim else None)
size = len(data)
for i in xrange(size):
for j in xrange(i, size):
if np.isnan(data[i][j]):
data[i][j] = 0
data[j][i] = 0
# data[j][i] = data[i][j]
evals, evect = eigh(data)
sort_perm = evals.argsort()
evect = evect[sort_perm]
data = [i for d in data for i in d if not np.isnan(i)]
gradient = np.linspace(np.nanmin(data), np.nanmax(data), size)
gradient = np.vstack((gradient, gradient))
h = ax2.hist(data, color="darkgrey", linewidth=2, bins=20, histtype="step", normed=True)
_ = ax2.imshow(gradient, aspect="auto", cmap=cmap, extent=(np.nanmin(data), np.nanmax(data), 0, max(h[0])))
if genome_seq:
for crm in genome_seq:
ax1.vlines(
[cumcs[crm][0] - 0.5, cumcs[crm][1] - 0.5],
cumcs[crm][0] - 0.5,
cumcs[crm][1] - 0.5,
color="w",
linestyle="-",
linewidth=1,
alpha=1,
)
ax1.hlines(
[cumcs[crm][1] - 0.5, cumcs[crm][0] - 0.5],
cumcs[crm][0] - 0.5,
cumcs[crm][1] - 0.5,
color="w",
linestyle="-",
linewidth=1,
alpha=1,
)
ax1.vlines(
[cumcs[crm][0] - 0.5, cumcs[crm][1] - 0.5],
cumcs[crm][0] - 0.5,
cumcs[crm][1] - 0.5,
color="k",
linestyle="--",
)
ax1.hlines(
[cumcs[crm][1] - 0.5, cumcs[crm][0] - 0.5],
cumcs[crm][0] - 0.5,
cumcs[crm][1] - 0.5,
color="k",
linestyle="--",
)
if not one:
vals = [0]
keys = [""]
for crm in genome_seq:
vals.append(cumcs[crm][0])
keys.append(crm)
vals.append(cumcs[crm][1])
ax1.set_yticks(vals)
ax1.set_yticklabels("")
ax1.set_yticks([float(vals[i] + vals[i + 1]) / 2 for i in xrange(len(vals) - 1)], minor=True)
ax1.set_yticklabels(keys, minor=True)
for t in ax1.yaxis.get_minor_ticks():
t.tick1On = False
t.tick2On = False
# totaloridata = ''.join([j + ('' if (i+1)%3 else ',') for i, j in enumerate(str(totaloridata)[::-1])])[::-1].strip(',')
# minoridata = ''.join([j + ('' if (i+1)%3 else ',') for i, j in enumerate(str(minoridata)[::-1])])[::-1].strip(',')
# maxoridata = ''.join([j + ('' if (i+1)%3 else ',') for i, j in enumerate(str(maxoridata)[::-1])])[::-1].strip(',')
plt.figtext(
0.05,
0.25,
"".join(
[
(name + "\n") if name else "",
"Number of interactions: %s\n" % str(totaloridata),
("" if np.isnan(cistrans) else ("Percentage of cis interactions: %.0f%%\n" % (cistrans * 100))),
"Min interactions: %s\n" % (minoridata),
"Max interactions: %s\n" % (maxoridata),
]
),
)
ax2.set_xlim((np.nanmin(data), np.nanmax(data)))
ax2.set_ylim((0, max(h[0])))
ax1.set_xlim((-0.5, size - 0.5))
ax1.set_ylim((-0.5, size - 0.5))
ax2.set_xlabel("log interaction count")
# we reduce the number of dots displayed.... we just want to see the shape
subdata = np.array(list(set([float(int(d * 100)) / 100 for d in data])))
try:
normfit = sc_norm.pdf(subdata, np.nanmean(data), np.nanstd(data))
except AttributeError:
normfit = sc_norm.pdf(subdata, np.mean(data), np.std(data))
ax2.plot(subdata, normfit, "w.", markersize=2.5, alpha=0.4)
ax2.plot(subdata, normfit, "k.", markersize=1.5, alpha=1)
ax2.set_title("skew: %.3f, kurtosis: %.3f" % (skew(data), kurtosis(data)))
ax4.vlines(range(size), 0, evect[:, -1], color="k")
ax4.hlines(0, 0, size, color="red")
ax4.set_ylabel("E1")
ax4.set_yticklabels([])
try:
ax5.vlines(range(size), 0, evect[:, -2], color="k")
except IndexError:
pass
ax5.hlines(0, 0, size, color="red")
ax5.set_ylabel("E2")
ax5.set_yticklabels([])
try:
ax6.vlines(range(size), 0, evect[:, -3], color="k")
except IndexError:
pass
ax6.hlines(0, 0, size, color="red")
ax6.set_ylabel("E3")
ax6.set_yticklabels([])
xticklabels = ax4.get_xticklabels() + ax5.get_xticklabels() + ax6.get_xticklabels()
plt.setp(xticklabels, visible=False)
if savefig:
tadbit_savefig(savefig)
elif show:
plt.show()
plt.close("all")
def quality_plot(fnam, r_enz=None, nreads=float('inf'), axe=None, savefig=None, paired=False):
"""
Plots the sequencing quality of a given FASTQ file. If a restrinction enzyme
(RE) name is provided, can also represent the distribution of digested and
undigested RE sites and estimate an expected proportion of dangling-ends.
Proportion of dangling-ends is inferred by counting the number of times a
dangling-end site, is found at the beginning of any of the reads (divided by
the number of reads).
:param fnam: path to FASTQ file
:param None nreads: max number of reads to read, not necesary to read all
:param None savefig: path to a file where to save the image generated;
if None, the image will be shown using matplotlib GUI (the extension
of the file name will determine the desired format).
:param False paired: is input FASTQ contains both ends
:returns: the percentage of dangling-ends (sensu stricto) and the percentage of
reads with at least a ligation site.
"""
phred = dict([(c, i) for i, c in enumerate(
'!"#$%&\'()*+,-./0123456789:;<=>?@ABCDEFGHIJKLMNOPQRSTUVWXYZ[\]^_`abcdefghijklmnopqrstuvwxyz{|}~')])
if isinstance(r_enz, list):
r_enzs = r_enz
elif isinstance(r_enz, str):
r_enzs = [r_enz]
for k in RESTRICTION_ENZYMES.keys():
for i in range(len(r_enzs)):
if k.lower() == r_enz[i].lower():
r_enz[i] = k
# else let it as None
quals = []
henes = []
sites = {}
fixes = {}
liges = {}
ligep = {}
tkw = dict(size=4, width=1.5)
if fnam.endswith('.gz'):
fhandler = gopen(fnam)
elif fnam.endswith('.dsrc'):
proc = Popen(['dsrc', 'd', '-t8', '-s', fnam], stdout=PIPE)
fhandler = proc.stdout
else:
fhandler = open(fnam)
if not r_enzs:
if nreads:
while True:
try:
next(fhandler)
except EOFError:
break
seq = next(fhandler)
if 'N' in seq:
henes.extend([i for i, s in enumerate(seq) if s == 'N'])
next(fhandler)
line = next(fhandler)
quals.append([phred[i] for i in line.strip()])
if len(quals) > nreads:
break
else: # do this because it's faster
while True:
try:
next(fhandler)
except EOFError:
break
seq = next(fhandler)
if 'N' in seq:
henes.extend([i for i, s in enumerate(seq) if s == 'N'])
next(fhandler)
line = next(fhandler)
quals.append([phred[i] for i in line.strip()])
else:
r_sites = {}
d_sites = {}
for r_enz in r_enzs:
r_sites[r_enz] = RESTRICTION_ENZYMES[r_enz].replace('|', '')
d_sites[r_enz] = repaired(r_enz)
sites[r_enz] = [] # initialize dico to store undigested sites
fixes[r_enz] = [] # initialize dico to store digested sites
l_sites = religateds(r_enzs)
site = {}
fixe = {}
for r_enz in r_enzs:
site[r_enz] = re.compile(r_sites[r_enz])
fixe[r_enz] = re.compile(d_sites[r_enz])
# ligation sites should appear in lower case in the sequence
lige = {}
for k in l_sites:
liges[k] = [] # initialize dico to store sites
ligep[k] = 0 # initialize dico to store sites
l_sites[k] = l_sites[k].lower()
lige[k] = re.compile(l_sites[k])
while len(quals) <= nreads:
try:
next(fhandler)
except StopIteration:
break
seq = next(fhandler)
# ligation sites replaced by lower case to ease the search
for lig in l_sites.values():
seq = seq.replace(lig.upper(), lig)
for r_enz in r_enzs:
sites[r_enz].extend([m.start() for m in site[r_enz].finditer(seq)])
# TODO: you cannot have a repaired/fixed site in the middle of
# the sequence, this could be only checked at the beginning
fixes[r_enz].extend([m.start() for m in fixe[r_enz].finditer(seq)])
for k in lige: # for each paired of cut-site
liges[k].extend([m.start() for m in lige[k].finditer(seq)])
ligep[k] += l_sites[k] in seq
# store the number of Ns found in the sequences
if 'N' in seq:
henes.extend([i for i, s in enumerate(seq) if s == 'N'])
next(fhandler)
line = next(fhandler)
quals.append([phred[i] for i in line.strip()])
fhandler.close()
if not nreads:
nreads = len(quals)
quals = izip_longest(*quals, fillvalue=float('nan'))
meanquals, errorquals = zip(*[(nanmean(q), nanstd(q)) for q in quals])
max_seq_len = len(meanquals)
if axe:
ax = axe
fig = axe.get_figure()
ax2 = fig.add_subplot(212)
else: # configure plot
if r_enz: # do both plots
_, (ax, ax2) = plt.subplots(2,1, figsize=(15, 12))
else: # only do the quality_plot plot
_, ax = plt.subplots(1,1, figsize=(15, 6))
ax.patch.set_facecolor('lightgrey')
ax.patch.set_alpha(0.4)
ax.grid(ls='-', color='w', lw=1.5, alpha=0.6, which='major')
ax.grid(ls='-', color='w', lw=1, alpha=0.3, which='minor')
ax.set_axisbelow(True)
# remove tick marks
ax.tick_params(axis='both', direction='out', top=False, right=False,
left=False, bottom=False)
ax.tick_params(axis='both', direction='out', top=False, right=False,
left=False, bottom=False, which='minor')
ax.errorbar(range(max_seq_len), meanquals,
linewidth=1, elinewidth=1, color='darkblue',
yerr=errorquals, ecolor='orange')
ax.set_xlim((0, max_seq_len))
ax.set_xlabel('Nucleotidic position')
ax.set_ylabel('PHRED score')
ax.set_title('Sequencing Quality (%d reads)' % (nreads))
ax.yaxis.label.set_color('darkblue')
ax.tick_params(axis='y', colors='darkblue', **tkw)
axb = ax.twinx()
# quality_plot plot
axb.plot([henes.count(i) for i in xrange(max_seq_len)], linewidth=1,
color='black', linestyle='--')
axb.yaxis.label.set_color('black')
axb.tick_params(axis='y', colors='black', **tkw)
axb.set_ylabel('Number of "N" per position')
try: # no Ns found (yes... it happens)
axb.set_yscale('log')
axb.set_ylim((0, axb.get_ylim()[1] * 1000))
except ValueError:
axb.set_yscale('linear')
ax.set_ylim((0, ax.get_ylim()[1]))
ax.set_xlim((0, max_seq_len))
# Hi-C plot
if r_enzs:
ax.set_title('Sequencing Quality and deconvolution (%s %d reads)' % (
', '.join(r_enzs), nreads))
ax.set_xlabel('')
plt.setp(ax.get_xticklabels(), visible=False)
ax2.patch.set_facecolor('lightgrey')
ax2.patch.set_alpha(0.4)
ax2.grid(ls='-', color='w', lw=1.5, alpha=0.6, which='major')
ax2.grid(ls='-', color='w', lw=1, alpha=0.3, which='minor')
ax2.set_axisbelow(True)
ax2.set_xlabel('Nucleotidic position')
# seq_len is the length of the line to plot. we don't want to plot
# if there is no room for the cut-site, or ligation site.
site_len = max((max([len(r_sites[k]) for k in r_sites]),
max([len(l_sites[k]) for k in l_sites]),
max([len(d_sites[k]) for k in d_sites])))
seq_len = max_seq_len - site_len
# transform dictionaries of positions into dictionaries of counts
for r_enz in sites:
sites[r_enz] = [sites[r_enz].count(k) for k in xrange(seq_len)] # Undigested
fixes[r_enz] = [fixes[r_enz].count(k) for k in xrange(seq_len)] # DE
for r1, r2 in liges:
liges[(r1, r2)] = [liges[(r1, r2)].count(k) for k in xrange(seq_len)] # OK
# in case the pattern of the repaired cut-site contains the target
# cut-site pattern. These sites were counted twice, once in the
# undigested, and once in the repaired. We remove them from the
# repaired:
for r_enz in r_enzs:
if d_sites[r_enz] in r_sites[r_enz]:
pos = r_sites[r_enz].find(d_sites[r_enz])
fixes[r_enz] = (fixes[r_enz][:pos] +
[fixes[r_enz][k] - sites[r_enz][k-pos]
for k in xrange(pos, seq_len)])
# same for ligated sites
for r_enz1 in r_enzs:
for r_enz2 in r_enzs:
if d_sites[r_enz1] not in l_sites[(r_enz1, r_enz2)]:
continue
pos = l_sites[(r_enz1, r_enz2)].find(d_sites[r_enz1])
fixes[r_enz1] = (fixes[r_enz1][:pos] +
[fixes[r_enz1][k] - liges[(r_enz1, r_enz2)][k - pos]
for k in xrange(pos, seq_len)])
# remove anything that could be in between the two read ends
if paired:
for k in sites:
sites[k][max_seq_len / 2 - site_len:
max_seq_len / 2] = [float('nan')] * site_len
fixes[k][max_seq_len / 2 - site_len:
max_seq_len / 2] = [float('nan')] * site_len
for k in liges:
liges[k][max_seq_len / 2 - site_len:
max_seq_len / 2] = [float('nan')] * site_len
# plot undigested cut-sites
color = iter(plt.cm.Reds(linspace(0.3, 0.95, len(r_enzs))))
for r_enz in sites:
# print 'undigested', r_enz
# print sites[r_enz][:20]
ax2.plot(sites[r_enz], linewidth=2, color = color.next(),
alpha=0.9,
label='Undigested RE site (%s: %s)' % (r_enz, r_sites[r_enz])
if any([f > 0 for f in fixes[r_enz]])
else 'Undigested & Dangling-Ends (%s: %s)' % (r_enz, r_sites[r_enz]))
ax2.set_ylabel('Undigested')
ax2.yaxis.label.set_color('darkred')
ax2.tick_params(axis='y', colors='darkred', **tkw)
lines, labels = ax2.get_legend_handles_labels()
ax3 = ax2.twinx()
color = iter(plt.cm.Blues(linspace(0.3, 0.95, len(liges))))
for r1, r2 in liges:
# print 'ligated', r1, r2
# print liges[(r1, r2)][:20]
ax3.plot(liges[(r1, r2)], linewidth=2, color=color.next(),
alpha=0.9,
label = 'Ligated (%s-%s: %s)' % (r1, r2, l_sites[(r1, r2)].upper()))
ax3.yaxis.label.set_color('darkblue')
ax3.tick_params(axis='y', colors='darkblue', **tkw)
ax3.set_ylabel('Ligated')
tmp_lines, tmp_labels = ax3.get_legend_handles_labels()
lines.extend(tmp_lines)
labels.extend(tmp_labels)
color = iter(plt.cm.Greens(linspace(0.3, 0.95, len(r_enzs))))
for i, r_enz in enumerate(r_enzs):
if any([f > 0 for f in fixes[r_enz]]):
ax4 = ax2.twinx()
ax4.spines["right"].set_position(("axes", 1.07))
make_patch_spines_invisible(ax4)
ax4.spines["right"].set_visible(True)
# print 'repaired', r_enz
# print fixes[r_enz][:20]
ax4.plot(fixes[r_enz], linewidth=2, color=color.next(),
alpha=0.9,
label='Dangling-ends (%s: %s)' % (r_enz, d_sites[r_enz]))
ax4.yaxis.label.set_color('darkgreen')
ax4.tick_params(axis='y', colors='darkgreen', **tkw)
ax4.set_ylabel('Dangling-ends')
tmp_lines, tmp_labels = ax4.get_legend_handles_labels()
lines.extend(tmp_lines)
labels.extend(tmp_labels)
else:
ax2.set_ylabel('Undigested & Dangling-ends')
ax2.set_xlim((0, max_seq_len))
# Count ligation sites
lig_cnt = {}
for k in liges:
lig_cnt[k] = (nansum(liges[k]) - liges[k][0] -
liges[k][max_seq_len / 2])
# Count undigested sites
sit_cnt = {}
for r_enz in r_enzs:
sit_cnt[r_enz] = (nansum(sites[r_enz]) - sites[r_enz][0] -
sites[r_enz][max_seq_len / 2])
# Count Dangling-Ends
des = {}
for r_enz in r_enzs:
if any([f > 0 for f in fixes[r_enz]]):
des[r_enz] = ((100. * (fixes[r_enz][0] + (fixes[r_enz][(max_seq_len / 2)]
if paired else 0))) / nreads)
else:
des[r_enz] = (100. * (sites[r_enz][0] + (sites[r_enz][(max_seq_len / 2)]
if paired else 0))) / nreads
# Decorate plot
title = ''
for r_enz in r_enzs:
lcnt = float(sum([lig_cnt[(r_enz1, r_enz2)] * (2 if r_enz1 == r_enz2 else 1)
for r_enz1 in r_enzs for r_enz2 in r_enzs
if r_enz1 == r_enz or r_enz2 == r_enz]))
title += ('Percentage of digested sites (not considering Dangling-Ends) '
'%s: %.1f%%\n' % (r_enz,
100. * float(lcnt) / (lcnt + sit_cnt[r_enz])))
for r_enz in r_enzs:
title += 'Percentage of dangling-ends %s: %.1f%%\n' % (r_enz, des[r_enz])
for r_enz1 in r_enzs:
for r_enz2 in r_enzs:
title += ('Percentage of reads with ligation site (%s-%s): %.1f%% \n' %
(r_enz1, r_enz2, (ligep[(r_enz1, r_enz2)] * 100.) / nreads))
plt.title(title.strip(), size=10, ha='left', x=0)
plt.subplots_adjust(right=0.85)
ax2.legend(lines, labels, bbox_to_anchor=(0.75, 1.0),
loc=3, borderaxespad=0., frameon=False, fontsize=9)
plt.tight_layout()
if savefig:
tadbit_savefig(savefig)
plt.close('all')
elif not axe:
plt.show()
for k in ligep:
ligep[k] = (ligep[k] * 100.) / nreads
return des, ligep
def plot_genomic_distribution(fnam, first_read=True, resolution=10000,
axe=None, ylim=None, savefig=None,
chr_names=None, nreads=None):
"""
:param fnam: input file name
:param True first_read: uses first read.
:param 100 resolution: group reads that are closer than this resolution
parameter
:param None axe: a matplotlib.axes.Axes object to define the plot
appearance
:param None savefig: path to a file where to save the image generated;
if None, the image will be shown using matplotlib GUI (the extension
of the file name will determine the desired format).
:param None chr_names: can pass a list of chromosome names in case only some
them the need to be plotted (this option may last even more than default)
"""
distr = {}
idx1, idx2 = (1, 3) if first_read else (7, 9)
genome_seq = OrderedDict()
fhandler = open(fnam)
line = fhandler.next()
if chr_names:
chr_names = set(chr_names)
cond1 = lambda x: x not in chr_names
else:
cond1 = lambda x: False
if nreads:
cond2 = lambda x: x >= nreads
else:
cond2 = lambda x: False
cond = lambda x, y: cond1(x) and cond2(y)
count = 0
while line.startswith('#'):
if line.startswith('# CRM '):
crm, clen = line[6:].split('\t')
genome_seq[crm] = int(clen)
line = fhandler.next()
try:
while True:
crm, pos = line.strip().split('\t')[idx1:idx2]
count += 1
if cond(crm, count):
line = fhandler.next()
if cond2(count):
break
continue
pos = int(pos) / resolution
try:
distr[crm][pos] += 1
except KeyError:
try:
distr[crm][pos] = 1
except KeyError:
distr[crm] = {pos: 1}
line = fhandler.next()
except StopIteration:
pass
fhandler.close()
if not axe:
_ = plt.figure(figsize=(15, 3 + 3 * len(distr.keys())))
max_y = max([max(distr[c].values()) for c in distr])
max_x = max([len(distr[c].values()) for c in distr])
ncrms = len(genome_seq if genome_seq else distr)
for i, crm in enumerate(chr_names if chr_names else genome_seq
if genome_seq else distr):
plt.subplot(ncrms, 1, i + 1)
try:
plt.plot(range(max(distr[crm])),
[distr[crm].get(j, 0) for j in xrange(max(distr[crm]))],
color='red', lw=1.5, alpha=0.7)
except KeyError:
pass
if ylim:
plt.vlines(genome_seq[crm] / resolution, ylim[0], ylim[1])
else:
plt.vlines(genome_seq[crm] / resolution, 0, max_y)
plt.xlim((0, max_x))
plt.ylim(ylim or (0, max_y))
plt.title(crm)
if savefig:
tadbit_savefig(savefig)
elif not axe:
plt.show()
plt.close('all')
def draw_map(data, genome_seq, cumcs, savefig, show, one=False, clim=None,
cmap='jet', decay=False, perc=10, name=None, cistrans=None,
decay_resolution=10000, normalized=False, max_diff=None):
_ = plt.figure(figsize=(15.,12.5))
if not max_diff:
max_diff = len(data)
ax1 = plt.axes([0.34, 0.08, 0.6, 0.7205])
ax2 = plt.axes([0.07, 0.65, 0.21, 0.15])
if decay:
ax3 = plt.axes([0.07, 0.42, 0.21, 0.15])
plot_distance_vs_interactions(data, genome_seq=genome_seq, axe=ax3,
resolution=decay_resolution,
max_diff=max_diff, normalized=normalized)
ax4 = plt.axes([0.34, 0.805, 0.6, 0.04], sharex=ax1)
ax5 = plt.axes([0.34, 0.845, 0.6, 0.04], sharex=ax1)
ax6 = plt.axes([0.34, 0.885, 0.6, 0.04], sharex=ax1)
try:
minoridata = np.nanmin(data)
maxoridata = np.nanmax(data)
except AttributeError:
vals = [i for d in data for i in d if not np.isnan(i)]
minoridata = np.min(vals)
maxoridata = np.max(vals)
totaloridata = np.nansum([data[i][j] for i in xrange(len(data))
for j in xrange(i, len(data[i]))]) # may not be square
data = nozero_log(data, np.log2)
vals = np.array([i for d in data for i in d])
vals = vals[np.isfinite(vals)]
mindata = np.nanmin(vals)
maxdata = np.nanmax(vals)
diff = maxdata - mindata
posI = 0.01 if not clim else (float(clim[0]) / diff) if clim[0] != None else 0.01
posF = 1.0 if not clim else (float(clim[1]) / diff) if clim[1] != None else 1.0
if cmap == 'tadbit':
cuts = perc
cdict = {'red' : [(0.0, 0.0, 0.0)],
'green': [(0.0, 0.0, 0.0)],
'blue' : [(0.0, 0.5, 0.5)]}
prev_pos = 0
median = (np.median(vals) - mindata) / diff
for prc in np.linspace(posI, median, cuts / 2, endpoint=False):
try:
pos = (np.percentile(vals, prc * 100.) - mindata) / diff
prc = ((prc - posI) / (median - posI)) + 1. / cuts
except ValueError:
pos = prc = 0
if prev_pos >= pos:
continue
cdict['red' ].append([pos, prc, prc])
cdict['green'].append([pos, prc, prc])
cdict['blue' ].append([pos, 1, 1])
prev_pos = pos
for prc in np.linspace(median + 1. / cuts, posF, cuts / 2, endpoint=False):
try:
pos = (np.percentile(vals, prc * 100.) - mindata) / diff
prc = ((prc - median) / (posF - median))
except ValueError:
pos = prc = 0
if prev_pos >= pos:
continue
cdict['red' ].append([pos, 1.0, 1.0])
cdict['green'].append([pos, 1 - prc, 1 - prc])
cdict['blue' ].append([pos, 1 - prc, 1 - prc])
prev_pos = pos
pos = (np.percentile(vals ,97.) - mindata) / diff
cdict['red' ].append([pos, 0.1, 0.1])
cdict['green'].append([pos, 0, 0])
cdict['blue' ].append([pos, 0, 0])
cdict['red' ].append([1.0, 1, 1])
cdict['green'].append([1.0, 1, 1])
cdict['blue' ].append([1.0, 0, 0])
cmap = LinearSegmentedColormap(cmap, cdict)
clim = None
else:
cmap = plt.get_cmap(cmap)
cmap.set_bad('darkgrey', 1)
ax1.imshow(data, interpolation='none',
cmap=cmap, vmin=clim[0] if clim else None, vmax=clim[1] if clim else None)
size1 = len(data)
size2 = len(data[0])
if size1 == size2:
for i in xrange(size1):
for j in xrange(i, size2):
if np.isnan(data[i][j]):
data[i][j] = 0
data[j][i] = 0
else:
for i in xrange(size1):
for j in xrange(size2):
if np.isnan(data[i][j]):
data[i][j] = 0
#data[j][i] = data[i][j]
try:
evals, evect = eigh(data)
sort_perm = evals.argsort()
evect = evect[sort_perm]
except:
evals, evect = None, None
data = [i for d in data for i in d if not np.isnan(i)]
gradient = np.linspace(np.nanmin(data),
np.nanmax(data), max(size1, size2))
gradient = np.vstack((gradient, gradient))
h = ax2.hist(data, color='darkgrey', linewidth=2,
bins=20, histtype='step', normed=True)
_ = ax2.imshow(gradient, aspect='auto', cmap=cmap,
extent=(np.nanmin(data), np.nanmax(data) , 0, max(h[0])))
if genome_seq:
for crm in genome_seq:
ax1.vlines([cumcs[crm][0]-.5, cumcs[crm][1]-.5], cumcs[crm][0]-.5, cumcs[crm][1]-.5,
color='w', linestyle='-', linewidth=1, alpha=1)
ax1.hlines([cumcs[crm][1]-.5, cumcs[crm][0]-.5], cumcs[crm][0]-.5, cumcs[crm][1]-.5,
color='w', linestyle='-', linewidth=1, alpha=1)
ax1.vlines([cumcs[crm][0]-.5, cumcs[crm][1]-.5], cumcs[crm][0]-.5, cumcs[crm][1]-.5,
color='k', linestyle='--')
ax1.hlines([cumcs[crm][1]-.5, cumcs[crm][0]-.5], cumcs[crm][0]-.5, cumcs[crm][1]-.5,
color='k', linestyle='--')
if not one:
vals = [0]
keys = ['']
for crm in genome_seq:
vals.append(cumcs[crm][0])
keys.append(crm)
vals.append(cumcs[crm][1])
ax1.set_yticks(vals)
ax1.set_yticklabels('')
ax1.set_yticks([float(vals[i]+vals[i+1])/2
for i in xrange(len(vals) - 1)], minor=True)
ax1.set_yticklabels(keys, minor=True)
for t in ax1.yaxis.get_minor_ticks():
t.tick1On = False
t.tick2On = False
# totaloridata = ''.join([j + ('' if (i+1)%3 else ',') for i, j in enumerate(str(totaloridata)[::-1])])[::-1].strip(',')
# minoridata = ''.join([j + ('' if (i+1)%3 else ',') for i, j in enumerate(str(minoridata)[::-1])])[::-1].strip(',')
# maxoridata = ''.join([j + ('' if (i+1)%3 else ',') for i, j in enumerate(str(maxoridata)[::-1])])[::-1].strip(',')
plt.figtext(0.05,0.25, ''.join([
(name + '\n') if name else '',
'Number of interactions: %s\n' % str(totaloridata),
('' if np.isnan(cistrans) else
('Percentage of cis interactions: %.0f%%\n' % (cistrans*100))),
'Min interactions: %s\n' % (minoridata),
'Max interactions: %s\n' % (maxoridata)]))
ax2.set_xlim((np.nanmin(data), np.nanmax(data)))
ax2.set_ylim((0, max(h[0])))
ax1.set_xlim ((-0.5, size1 - .5))
ax1.set_ylim ((-0.5, size2 - .5))
ax2.set_xlabel('log interaction count')
# we reduce the number of dots displayed.... we just want to see the shape
subdata = np.array(list(set([float(int(d*100))/100 for d in data])))
try:
normfit = sc_norm.pdf(subdata, np.nanmean(data), np.nanstd(data))
except AttributeError:
normfit = sc_norm.pdf(subdata, np.mean(data), np.std(data))
ax2.plot(subdata, normfit, 'w.', markersize=2.5, alpha=.4)
ax2.plot(subdata, normfit, 'k.', markersize=1.5, alpha=1)
ax2.set_title('skew: %.3f, kurtosis: %.3f' % (skew(data),
kurtosis(data)))
try:
ax4.vlines(range(size1), 0, evect[:,-1], color='k')
except (TypeError, IndexError):
pass
ax4.hlines(0, 0, size2, color='red')
ax4.set_ylabel('E1')
ax4.set_yticklabels([])
try:
ax5.vlines(range(size1), 0, evect[:,-2], color='k')
except (TypeError, IndexError):
pass
ax5.hlines(0, 0, size2, color='red')
ax5.set_ylabel('E2')
ax5.set_yticklabels([])
try:
ax6.vlines(range(size1), 0, evect[:,-3], color='k')
except (TypeError, IndexError):
pass
ax6.hlines(0, 0, size2, color='red')
ax6.set_ylabel('E3')
ax6.set_yticklabels([])
xticklabels = ax4.get_xticklabels() + ax5.get_xticklabels() + ax6.get_xticklabels()
plt.setp(xticklabels, visible=False)
if savefig:
tadbit_savefig(savefig)
elif show:
plt.show()
plt.close('all')
def eig_correlate_matrices(hic_data1, hic_data2, nvect=6,
savefig=None, show=False, savedata=None, **kwargs):
"""
Compare the iteractions of two Hi-C matrices using their 6 first
eigenvectors, with spearman rank correlation
:param hic_data1: Hi-C-data object
:param hic_data2: Hi-C-data object
:param 6 nvect: number of eigenvectors to compare
:param None savefig: path to save the plot
:param False show: displays the plot
:param kwargs: any argument to pass to matplotlib imshow function
:returns: matrix of correlations
"""
data1 = hic_data1.get_matrix()
data2 = hic_data2.get_matrix()
# get the log
size = len(data1)
data1 = nozero_log(data1, np.log2)
data2 = nozero_log(data2, np.log2)
# get the eigenvectors
ev1, evect1 = eigh(data1)
ev2, evect2 = eigh(data2)
corr = [[0 for _ in xrange(nvect)] for _ in xrange(nvect)]
# sort eigenvectors according to their eigenvalues => first is last!!
sort_perm = ev1.argsort()
ev1.sort()
evect1 = evect1[sort_perm]
sort_perm = ev2.argsort()
ev2.sort()
evect2 = evect2[sort_perm]
# calculate Pearson correlation
for i in xrange(nvect):
for j in xrange(nvect):
corr[i][j] = abs(pearsonr(evect1[:,-i-1],
evect2[:,-j-1])[0])
# plot
axe = plt.axes([0.1, 0.1, 0.6, 0.8])
cbaxes = plt.axes([0.85, 0.1, 0.03, 0.8])
if show or savefig:
im = axe.imshow(corr, interpolation="nearest",origin='lower', **kwargs)
axe.set_xlabel('Eigen Vectors exp. 1')
axe.set_ylabel('Eigen Vectors exp. 2')
axe.set_xticks(range(nvect))
axe.set_yticks(range(nvect))
axe.set_xticklabels(range(1, nvect + 2))
axe.set_yticklabels(range(1, nvect + 2))
axe.xaxis.set_tick_params(length=0, width=0)
axe.yaxis.set_tick_params(length=0, width=0)
cbar = plt.colorbar(im, cax = cbaxes )
cbar.ax.set_ylabel('Pearson correlation', rotation=90*3,
verticalalignment='bottom')
axe2 = axe.twinx()
axe2.set_yticks(range(nvect))
axe2.set_yticklabels(['%.1f' % (e) for e in ev2[-nvect:][::-1]])
axe2.set_ylabel('corresponding Eigen Values exp. 2', rotation=90*3,
verticalalignment='bottom')
axe2.set_ylim((-0.5, nvect - 0.5))
axe2.yaxis.set_tick_params(length=0, width=0)
axe3 = axe.twiny()
axe3.set_xticks(range(nvect))
axe3.set_xticklabels(['%.1f' % (e) for e in ev1[-nvect:][::-1]])
axe3.set_xlabel('corresponding Eigen Values exp. 1')
axe3.set_xlim((-0.5, nvect - 0.5))
axe3.xaxis.set_tick_params(length=0, width=0)
axe.set_ylim((-0.5, nvect - 0.5))
axe.set_xlim((-0.5, nvect - 0.5))
if savefig:
tadbit_savefig(savefig)
if show:
plt.show()
plt.close('all')
if savedata:
out = open(savedata, 'w')
out.write('# ' + '\t'.join(['Eigen Vector %s'% i
for i in xrange(nvect)]) + '\n')
for i in xrange(nvect):
out.write('\t'.join([str(corr[i][j])
for j in xrange(nvect)]) + '\n')
out.close()
return corr
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 mmp_score(matrix, nrand=10, verbose=False, savefig=None):
"""
:param matrix: list of lists
:param 10 nrand: number of randomizations
:param None savefig: path where to save figure
:returns: 1- MMP score which ranges from 0 (bad) to 1 (good), and 2- the
expected correlation of the contact matrices of the modeled chromatin
with the original Hi-C data (plus the 3- lower and 4- upper values
expected in 95% of the cases)
"""
data = np.array([np.array([v for v in l]) for l in matrix])
if verbose:
sys.stdout.write(' - getting EigenVectors\n')
egval, _ = np.linalg.eigh(data)
# sort eigenvalues/vectors
idx = (-egval).argsort()
egval = egval[idx]
regvals = []
if verbose:
sys.stdout.write(' - randomization\n')
for i in xrange(int(nrand)):
if verbose:
sys.stdout.write('\r ' + str(i + 1) + ' / ' + str(nrand))
sys.stdout.flush()
regval, _ = np.linalg.eigh(randomize_matrix(data))
regval = [abs(j) for j in regval]
regval.sort(reverse=True)
regvals.append(regval)
if verbose:
sys.stdout.write('\n')
regvals = zip(*regvals)
rvmean = []
for rv in regvals:
rvmean.append(np.mean(rv))
total = sum(rvmean) / 100
rvmean = [i / total for i in rvmean]
err = []
for rv in regvals:
rvstd = np.std(rv / total)
err.append(2 * rvstd)
zdata = sorted(
np.log2([
data[i][j] for i in xrange(len(data))
for j in xrange(i, len(data)) if data[i][j]
]))
skewness = skew(zdata)
kurtness = kurtosis(zdata)
if savefig:
_ = plt.figure(figsize=(14, 8))
gs = gridspec.GridSpec(7, 5, wspace=0.5, hspace=1.5)
ax1 = plt.subplot(gs[:, 0:3])
ax2 = plt.subplot(gs[1:5, 3:])
ax3 = plt.subplot(gs[5:7, 3:])
img = ax2.imshow(np.log2(data), interpolation='none')
plt.colorbar(img, ax=ax2)
if savefig:
ax2.set_title('Original matrix', size=12)
ax2.tick_params(axis='both', which='major', labelsize=10)
ax2.set_xlabel('Bin')
ax2.set_ylabel('Bin')
normfit = sc_norm.pdf(zdata, np.mean(zdata), np.std(zdata))
_ = ax3.plot(zdata,
normfit,
':o',
color='grey',
ms=3,
alpha=.4,
markersize=.5)
ax3.tick_params(axis='both', which='major', labelsize=10)
ax3.hist(zdata, bins=20, normed=True, alpha=0.7, color='r')
ax3.set_xlabel('Z-score')
ax3.set_ylabel('Frequency')
rcParams['xtick.direction'] = 'out'
rcParams['ytick.direction'] = 'out'
rcParams['axes.axisbelow'] = True
rcParams['xtick.direction'] = 'out'
rcParams['ytick.direction'] = 'out'
rcParams['axes.axisbelow'] = True
rcParams['axes.grid'] = True
rcParams['grid.color'] = 'w'
rcParams['grid.linestyle'] = '-'
rcParams['grid.linewidth'] = 2
# rcParams['grid.alpha'] = .3
ax1.minorticks_on()
ax1.grid(ls='-', color='w', alpha=.3, lw=2, which='major')
ax1.grid(ls='-', b=True, color='w', alpha=.3, lw=1, which='minor')
ax1.spines['top'].set_color('none')
ax1.spines['right'].set_color('none')
ax1.spines['bottom'].set_color('none')
ax1.spines['left'].set_color('none')
ax1.xaxis.set_ticks_position('bottom')
ax1.yaxis.set_ticks_position('left')
ax1.set_xscale('log')
ax1.set_axis_bgcolor((.9, .9, .9))
ax1.errorbar(range(1, 1 + len(rvmean)),
rvmean,
yerr=err,
ecolor='red',
color='orange',
lw=2,
label='%s randomizations' % (nrand))
total = sum(abs(egval)) / 100
egval = np.array(sorted([e / total for e in abs(egval)], reverse=True))
for i in xrange(len(rvmean)):
if rvmean[i] + err[i] > egval[i]:
break
signifidx = i
size = len(data)
sev = sum(egval[:signifidx] - rvmean[:signifidx])
if savefig:
ax1.plot(range(1, 1 + len(rvmean)),
egval,
color='green',
lw=2,
label='Observed data')
ax1.fill_between(range(1, 1 + len(rvmean)),
rvmean,
egval,
where=(np.array(rvmean) + np.array(err)) < egval,
facecolor='green',
interpolate=True,
alpha=0.2)
ax1.fill_between(range(1, 1 + len(rvmean)),
rvmean,
egval,
where=(np.array(rvmean) + np.array(err)) > egval,
facecolor='red',
interpolate=True,
alpha=0.2)
ax1.set_xlim((0, len(rvmean)))
ax1.set_ylim((0, max(max(rvmean), max(egval))))
ax1.legend(frameon=False, loc='upper right', prop={'size': 10})
ax1.set_xlabel('Log indexes of Eigenvalues')
ax1.set_ylabel('Eigenvalues (percentage of total)')
#plt.subplots_adjust(right=0.6)
#img = Image.open(opts.outdir + '/matrix_small.png')
#fig.figimage(img, 640, -160)
minv = float(min([i for d in data for i in d if i])) / 2
if minv == 0.5:
minv = 1. / (len(data)**2)
mmp = -0.0002 * size + 0.0335 * skewness - 0.0229 * kurtness + 0.0069 * sev + 0.8126
if verbose:
sys.stdout.write('\n')
sys.stdout.write('\n Results\n')
sys.stdout.write(' -------\n\n')
if verbose:
sys.stdout.write(' MMP score: %.4f\n\n' % mmp)
ex_a1, ex_b1 = [0.6975926, 0.2548171]
supa1, supb1 = [0.69300732000423904, 0.29858572176099613]
lowa1, lowb1 = [0.70217788900976075, 0.211048473299004]
scc = (mmp - ex_b1) / ex_a1
scc_up1 = (mmp - supb1) / supa1
scc_lw1 = (mmp - lowb1) / lowa1
if verbose:
sys.stdout.write((' predicted dSCC is %.3f (%.3f-%.3f '
'68%% confidence)\n') % (scc, scc_up1, scc_lw1))
supa75, supb75 = [0.69230778430383244, 0.30526310790548261]
lowa75, lowb75 = [0.70287742471016734, 0.20437108715451746]
scc_up75 = (mmp - supb75) / supa75
scc_lw75 = (mmp - lowb75) / lowa75
if verbose:
sys.stdout.write((' (%.3f-%.3f '
'75%% confidence)\n') % (scc_up75, scc_lw75))
supa2, supb2 = [0.68855373600821357, 0.34109720480765293]
lowa2, lowb2 = [0.70663147300578644, 0.16853699025234709]
scc_up2 = (mmp - supb2) / supa2
scc_lw2 = (mmp - lowb2) / lowa2
if verbose:
sys.stdout.write((' (%.3f-%.3f '
'95%% confidence)\n') % (scc_up2, scc_lw2))
if savefig:
# write the log
log = ''
log += ' 1- Matrix size (number of eigenvalues): %s\n' % (
len(egval))
log += " 2- Skewness of the distribution: %0.3f\n" % (skewness)
log += " 3- Kurtosis of the distribution: %0.3f\n" % (kurtness)
log += " 4- Sum of differences signif EV real-rand: %0.3f\n\n" % (
sev)
plt.figtext(0.62, 0.77, log, size='small')
log = "MMP score: %.3f\n" % (mmp)
log += "Predicted dSCC: %.3f (%.3f-%.3f at 95%% conf)\n" % (
scc, scc_up2, scc_lw2)
plt.figtext(0.61, 0.87, log, size=12)
tadbit_savefig(savefig)
plt.close('all')
return mmp, scc, scc_up2, scc_lw2
def correlate_matrices(hic_data1, hic_data2, max_dist=10, intra=False, axe=None,
savefig=None, show=False, savedata=None,
normalized=False, remove_bad_columns=True, **kwargs):
"""
Compare the iteractions of two Hi-C matrices at a given distance,
with spearman rank correlation
:param hic_data1: Hi-C-data object
:param hic_data2: Hi-C-data object
:param 1 resolution: to be used for scaling the plot
:param 10 max_dist: maximum distance from diagonal (e.g. 10 mean we will
not look further than 10 times the resolution)
:param None savefig: path to save the plot
:param False intra: only takes into account intra-chromosomal contacts
:param False show: displays the plot
:param False normalized: use normalized data
:param True remove_bads: computes the union of bad columns between samples
and exclude them from the comparison
:returns: list of correlations and list of genomic distances
"""
corrs = []
dists = []
if normalized:
get_the_guy1 = lambda i, j: (hic_data1[j, i] / hic_data1.bias[i] /
hic_data1.bias[j])
get_the_guy2 = lambda i, j: (hic_data2[j, i] / hic_data2.bias[i] /
hic_data2.bias[j])
else:
get_the_guy1 = lambda i, j: hic_data1[j, i]
get_the_guy2 = lambda i, j: hic_data2[j, i]
if remove_bad_columns:
# union of bad columns
bads = hic_data1.bads.copy()
bads.update(hic_data2.bads)
if (intra and hic_data1.sections and hic_data2.sections and
hic_data1.sections == hic_data2.sections):
for dist in xrange(1, max_dist + 1):
diag1 = []
diag2 = []
for crm in hic_data1.section_pos:
for j in xrange(hic_data1.section_pos[crm][0],
hic_data1.section_pos[crm][1] - dist):
i = j + dist
if j in bads or i in bads:
continue
diag1.append(get_the_guy1(i, j))
diag2.append(get_the_guy2(i, j))
corrs.append(spearmanr(diag1, diag2)[0])
dists.append(dist)
else:
if intra:
warn('WARNING: hic_dta does not contain chromosome coordinates, ' +
'intra set to False')
for dist in xrange(1, max_dist + 1):
diag1 = []
diag2 = []
for j in xrange(len(hic_data1) - dist):
i = j + dist
if j in bads or i in bads:
continue
diag1.append(get_the_guy1(i, j))
diag2.append(get_the_guy2(i, j))
corrs.append(spearmanr(diag1, diag2)[0])
dists.append(dist)
if show or savefig or axe:
if not axe:
fig = plt.figure()
axe = fig.add_subplot(111)
given_axe = False
else:
given_axe = True
axe.plot(dists, corrs, color='orange', linewidth=3, alpha=.8)
axe.set_xlabel('Genomic distance in bins')
axe.set_ylabel('Spearman rank correlation')
axe.set_xlim((0, dists[-1]))
if savefig:
tadbit_savefig(savefig)
if show:
plt.show()
if not given_axe:
plt.close('all')
if savedata:
out = open(savedata, 'w')
out.write('# genomic distance\tSpearman rank correlation\n')
for i in xrange(len(corrs)):
out.write('%s\t%s\n' % (dists[i], corrs[i]))
out.close()
if kwargs.get('get_bads', False):
return corrs, dists, bads
else:
return corrs, dists
def filter_by_zero_count(matrx, draw_hist=False, savefig=None):
"""
fits the distribution of Hi-C interaction count by column in the matrix to
a polynomial. Then searches for the first possible
"""
nbins = 100
# get sum of columns
cols = []
for c in sorted(matrx, key=sum):
cols.append(len(c) - c.count(0))
cols = np.array(cols)
if draw_hist:
plt.figure(figsize=(9, 9))
median = np.median(cols)
# mad = np.median([abs(median - c ) for c in cols])
best =(None, None, None, None)
# bin the sum of columns
xmin = min(cols)
xmax = max(cols)
y = np.linspace(xmin, xmax, nbins)
hist = np.digitize(cols, y)
x = [sum(hist == i) for i in range(1, nbins + 1)]
if draw_hist:
hist = plt.hist(cols, bins=100, alpha=.3, color='grey')
xp = range(0, cols[-1])
# check if the binning is correct
# we want at list half of the bins with some data
while list(x).count(0) > 2*len(x)/3:
cols = cols[:-1]
xmin = min(cols)
xmax = max(cols)
y = np.linspace(xmin, xmax, nbins)
hist = np.digitize(cols, y)
x = [sum(hist == i) for i in range(1, nbins + 1)]
if draw_hist:
plt.clf()
hist = plt.hist(cols, bins=100, alpha=.3, color='grey')
xp = range(0, cols[-1])
# find best polynomial fit in a given range
for order in range(7, 14):
z = np.polyfit(y, x, order)
zpp = np.polyder(z, m=1)
roots = np.roots(np.polyder(z))
# check that we are concave down, otherwise take next root
pente = np.polyval(zpp, abs(roots[-2] - roots[-1]) / 2 + roots[-1])
if pente > 0:
root = roots[-1]
else:
root = roots[-2]
# root must be higher than zero
if root <= 0:
continue
# and lower than the median
if root >= median:
continue
p = np.poly1d(z)
R2 = get_r2(p, x, y)
if best[0] < R2:
best = (R2, order, p, z, root)
p, z, root = best[2:]
if draw_hist:
a = plt.plot(xp, p(xp), "--", color='k')
b = plt.vlines(root, 0, plt.ylim()[1], colors='r', linestyles='dashed')
try:
plt.legend(a + [b], ['polyfit \n%s' % (
''.join([sub('e([-+][0-9]+)', 'e^{\\1}',
'$%s%.1fx^%s$' % ('+' if j>0 else '', j,
'{' + str(i) + '}'))
for i, j in enumerate(list(p)[::-1])])),
'first solution of polynomial derivation'],
fontsize='x-small')
except TypeError:
plt.legend(a + [b], ['polyfit \n%s' % (
''.join([sub('e([-+][0-9]+)', 'e^{\\1}',
'$%s%.1fx^%s$' % ('+' if j>0 else '', j,
'{' + str(i) + '}'))
for i, j in enumerate(list(p)[::-1])])),
'first solution of polynomial derivation'])
plt.ylim(0, plt.ylim()[1])
if savefig:
tadbit_savefig(savefig)
else:
plt.show()
# label as bad the columns with sums lower than the root
bads = {}
for i, col in enumerate(matrx):
if sum(col) < root:
bads[i] = None
# now stored in Experiment._zeros, used for getting more accurate z-scores
return bads
def visualize(self, names=None, tad=None, focus=None, paint_tads=False,
axe=None, show=True, logarithm=True, normalized=False,
relative=True, decorate=True, savefig=None, clim=None,
scale=(8, 6), cmap='jet'):
"""
Visualize the matrix of Hi-C interactions of a given experiment
:param None names: name of the experiment to visualize, or list of
experiment names. If None, all experiments will be shown
:param None tad: a given TAD in the form:
::
{'start': start,
'end' : end,
'brk' : end,
'score': score}
**Alternatively** a list of the TADs can be passed (all the TADs
between the first and last one passed will be showed. Thus, passing
more than two TADs might be superfluous)
:param None focus: a tuple with the start and end positions of the
region to visualize
:param False paint_tads: draw a box around the TADs defined for this
experiment
:param None axe: an axe object from matplotlib can be passed in order
to customize the picture
:param True show: either to pop-up matplotlib image or not
:param True logarithm: show the logarithm values
:param True normalized: show the normalized data (weights might have
been calculated previously). *Note: white rows/columns may appear in
the matrix displayed; these rows correspond to filtered rows (see*
:func:`pytadbit.utils.hic_filtering.hic_filtering_for_modelling` *)*
:param True relative: color scale is relative to the whole matrix of
data, not only to the region displayed
:param True decorate: draws color bar, title and axes labels
:param None savefig: path to a file where to save the image generated;
if None, the image will be shown using matplotlib GUI (the extension
of the file name will determine the desired format).
:param None clim: tuple with minimum and maximum value range for color
scale. I.e. clim=(-4, 10)
:param 'jet' cmap: color map from matplotlib. Can also be a
preconfigured cmap object.
"""
if names == None:
names = [xpr.name for xpr in self.experiments]
if not isinstance(names, list) and not isinstance(names, tuple):
names = [names]
cols = 1
rows = 1
else:
sqrtxpr = sqrt(len(names))
cols = int(round(sqrtxpr + (0.0 if int(sqrtxpr)==sqrtxpr else .5)))
rows = int(sqrtxpr+.5)
notaxe = axe == None
if not scale:
scale = (8, 6)
if notaxe and len(names) != 1:
fig = plt.figure(figsize=(scale[0] * cols, scale[1] * rows))
for i in range(rows):
for j in range(cols):
if i * cols + j >= len(names):
break
if notaxe and len(names) != 1:
axe = fig.add_subplot(
rows, cols, i * cols + j + 1)
if (isinstance(names[i * cols + j], tuple) or
isinstance(names[i * cols + j], list)):
if not axe:
fig = plt.figure(figsize=(scale[0] * cols, scale[1] * rows))
axe = fig.add_subplot(
rows, cols, i * cols + j + 1)
xpr1 = self.get_experiment(names[i * cols + j][0])
xpr2 = self.get_experiment(names[i * cols + j][1])
img = xpr1.view(tad=tad, focus=focus, paint_tads=paint_tads,
axe=axe, show=False, logarithm=logarithm,
normalized=normalized, relative=relative,
decorate=decorate, savefig=False,
where='up', clim=clim, cmap=cmap)
img = xpr2.view(tad=tad, focus=focus, paint_tads=paint_tads,
axe=axe, show=False, logarithm=logarithm,
normalized=normalized, relative=relative,
decorate=False, savefig=False, where='down',
clim=clim or img.get_clim(), cmap=cmap)
#axe = axe.twinx()
#axe.set_aspect('equal',adjustable='box-forced',anchor='NE')
if decorate:
plt.text(1.01, .5,
'Chromosome %s experiment %s' % (
self.name, xpr2.name),
rotation=-90, va='center', size='large',
ha='left', transform=axe.transAxes)
else:
xper = self.get_experiment(names[i * cols + j])
if not xper.hic_data and not xper.norm:
continue
xper.view(tad=tad, focus=focus, paint_tads=paint_tads,
axe=axe, show=False, logarithm=logarithm,
normalized=normalized, relative=relative,
decorate=decorate, savefig=False, clim=clim,
cmap=cmap)
if savefig:
tadbit_savefig(savefig)
if show:
plt.show()
def plot_distance_vs_interactions(data, min_diff=1, max_diff=1000, show=False,
genome_seq=None, resolution=None, axe=None,
savefig=None, normalized=False):
"""
:param data: input file name, or HiC_data object or list of lists
:param 10 min_diff: lower limit (in number of bins)
:param 1000 max_diff: upper limit (in number of bins) to look for
:param 100 resolution: group reads that are closer than this resolution
parameter
:param None axe: a matplotlib.axes.Axes object to define the plot
appearance
:param None savefig: path to a file where to save the image generated;
if None, the image will be shown using matplotlib GUI (the extension
of the file name will determine the desired format).
:returns: slope, intercept and R square of each of the 3 correlations
"""
resolution = resolution or 1
dist_intr = dict([(i, 0) for i in xrange(min_diff, max_diff)])
if isinstance(data, str):
fhandler = open(data)
line = fhandler.next()
while line.startswith('#'):
line = fhandler.next()
try:
while True:
_, cr1, ps1, _, _, _, _, cr2, ps2, _ = line.split('\t', 9)
if cr1 != cr2:
line = fhandler.next()
continue
diff = abs(int(ps1) / resolution - int(ps2) / resolution)
if max_diff > diff >= min_diff:
dist_intr[diff] += 1
line = fhandler.next()
except StopIteration:
pass
fhandler.close()
elif isinstance(data, HiC_data):
if normalized:
get_data = lambda x, y: data[x, y] / data.bias[x] / data.bias[y]
else:
get_data = lambda x, y: data[x, y]
max_diff = min(len(data), max_diff)
if data.section_pos:
for crm in data.section_pos:
for diff in xrange(min_diff, min(
(max_diff, 1 + data.chromosomes[crm]))):
for i in xrange(data.section_pos[crm][0],
data.section_pos[crm][1] - diff):
dist_intr[diff] += get_data(i, i + diff)
else:
for diff in xrange(min_diff, max_diff):
for i in xrange(len(data) - diff):
if not np.isnan(data[i, i + diff]):
dist_intr[diff] += get_data(i, diff)
else:
if genome_seq:
max_diff = min(max(genome_seq.values()), max_diff)
cnt = 0
for crm in genome_seq:
for diff in xrange(min_diff, min(
(max_diff, genome_seq[crm]))):
for i in xrange(cnt, cnt + genome_seq[crm] - diff):
if not np.isnan(data[i][i + diff]):
dist_intr[diff] += data[i][i + diff]
cnt += genome_seq[crm]
else:
max_diff = min(len(data), max_diff)
for diff in xrange(min_diff, max_diff):
for i in xrange(len(data) - diff):
if not np.isnan(data[i][i + diff]):
dist_intr[diff] += data[i][i + diff]
if not axe:
fig=plt.figure()
axe = fig.add_subplot(111)
# remove last part of the plot in case no interaction is count... reduce max_dist
for diff in xrange(max_diff - 1, min_diff, -1):
try:
if not dist_intr[diff]:
del(dist_intr[diff])
max_diff -=1
continue
except KeyError:
max_diff -=1
continue
break
xp, yp = zip(*sorted(dist_intr.items(), key=lambda x:x[0]))
x = []
y = []
for k in xrange(len(xp)):
if yp[k]:
x.append(xp[k])
y.append(yp[k])
axe.plot(x, y, 'k.')
best = (float('-inf'), 0, 0, 0, 0, 0, 0, 0, 0, 0)
logx = np.log(x)
logy = np.log(y)
ntries = 100
# set k for better fit
# for k in xrange(1, ntries/5, ntries/5/5):
if resolution == 1:
k = 1
for i in xrange(3, ntries-2-k):
v1 = i * len(x) / ntries
try:
a1, b1, r21, _, _ = linregress(logx[ :v1], logy[ :v1])
except ValueError:
a1 = b1 = r21 = 0
r21 *= r21
for j in xrange(i + 1 + k, ntries - 2 - k):
v2 = j * len(x) / ntries
try:
a2, b2, r22, _, _ = linregress(logx[v1+k:v2], logy[v1+k:v2])
a3, b3, r23, _, _ = linregress(logx[v2+k: ], logy[v2+k: ])
except ValueError:
a2 = b2 = r22 = 0
a3 = b3 = r23 = 0
r2 = r21 + r22**2 + r23**2
if r2 > best[0]:
best = (r2, v1, v2, a1, a2, a3,
b1, b2, b3, k)
# plot line of best fit
(v1, v2,
a1, a2, a3,
b1, b2, b3, k) = best[1:]
yfit1 = lambda xx: np.exp(b1 + a1*np.array (np.log(xx)))
yfit2 = lambda xx: np.exp(b2 + a2*np.array (np.log(xx)))
yfit3 = lambda xx: np.exp(b3 + a3*np.array (np.log(xx)))
axe.plot(x[ :v1], yfit1(x[ :v1] ), color= 'yellow', lw=2,
label = r'$\alpha_{%s}=%.2f$' % (
'0-0.7 \mathrm{ Mb}' if resolution != 1 else '1', a1))
#label = r'$\alpha_1=%.2f$ (0-%d)' % (a1, x[v1]))
axe.plot(x[v1+k:v2], yfit2(x[v1+k:v2]), color= 'orange', lw=2,
label = r'$\alpha_{%s}=%.2f$' % (
'0.7-10 \mathrm{ Mb}' if resolution != 1 else '2', a2))
# label = r'$\alpha_2=%.2f$ (%d-%d)' % (a2, x[v1], x[v2]))
axe.plot(x[v2+k: ], yfit3(x[v2+k: ] ), color= 'red' , lw=2,
label = r'$\alpha_{%s}=%.2f$' % (
'10 \mathrm{ Mb}-\infty' if resolution != 1 else '3', a3))
# label = r'$\alpha_3=%.2f$ (%d-$\infty$)' % (a3, x[v2+k]))
else:
# from 0.7 Mb
v1 = 700000 / resolution
# to 10 Mb
v2 = 10000000 / resolution
try:
a1, b1, r21, _, _ = linregress(logx[ :v1], logy[ :v1])
except ValueError:
a1, b1, r21 = 0, 0, 0
try:
a2, b2, r22, _, _ = linregress(logx[v1:v2], logy[v1:v2])
except ValueError:
a2, b2, r22 = 0, 0, 0
try:
a3, b3, r23, _, _ = linregress(logx[v2: ], logy[v2: ])
except ValueError:
a3, b3, r23 = 0, 0, 0
yfit1 = lambda xx: np.exp(b1 + a1*np.array (np.log(xx)))
yfit2 = lambda xx: np.exp(b2 + a2*np.array (np.log(xx)))
yfit3 = lambda xx: np.exp(b3 + a3*np.array (np.log(xx)))
axe.plot(x[ :v1], yfit1(x[ :v1] ), color= 'yellow', lw=2,
label = r'$\alpha_{%s}=%.2f$' % (
'0-0.7 \mathrm{ Mb}' if resolution != 1 else '1', a1))
#label = r'$\alpha_1=%.2f$ (0-%d)' % (a1, x[v1]))
axe.plot(x[v1:v2], yfit2(x[v1:v2]), color= 'orange', lw=2,
label = r'$\alpha_{%s}=%.2f$' % (
'0.7-10 \mathrm{ Mb}' if resolution != 1 else '2', a2))
# label = r'$\alpha_2=%.2f$ (%d-%d)' % (a2, x[v1], x[v2]))
axe.plot(x[v2: ], yfit3(x[v2: ] ), color= 'red' , lw=2,
label = r'$\alpha_{%s}=%.2f$' % (
'10 \mathrm{ Mb}-\infty' if resolution != 1 else '3', a3))
# label = r'$\alpha_3=%.2f$ (%d-$\infty$)' % (a3, x[v2+k]))
axe.set_ylabel('Log interaction count')
axe.set_xlabel('Log genomic distance (resolution: %s)' % nicer(resolution))
axe.legend(loc='lower left', frameon=False)
axe.set_xscale('log')
axe.set_yscale('log')
axe.set_xlim((min_diff, max_diff))
try:
axe.set_ylim((0, max(y)))
except ValueError:
pass
if savefig:
tadbit_savefig(savefig)
plt.close('all')
elif show==True:
plt.show()
plt.close('all')
return (a1, b1, r21), (a2, b2, r22), (a3, b3, r23)
def filter_by_cis_percentage(cisprc,
beg=0.3,
end=0.8,
sigma=2,
verbose=False,
size=None,
min_perc=None,
max_perc=None,
savefig=None):
"""
Define artifactual columns with either too low or too high counts of
interactions by compraing their percentage of cis interactions
(inter-chromosomal).
:param cisprc: dictionary with counts of cis-percentage by bin number.
Values of the dictionary are tuple with,m as first element the number
of cis interactions and as second element the total number of
interactions.
:param 0.3 beg: proportion of bins to be considered as possibly having low
counts
:param 0.8 end: proportion of bins to be considered as possibly having high
counts
:param 2 sigma: number of standard deviations used to define lower and upper
ranges in the varaition of the percentage of cis interactions
:param None size: size of the genome, inumber of bins (otherwise inferred
from cisprc dictionary)
:param None sevefig: path to save image of the distribution of cis
percentages and total counts by bin.
:returns: dictionary of bins to be filtered out (with either too low or too
high counts of interactions).
"""
sorted_sum, indices = list(zip(*sorted((cisprc[i][1], i) for i in cisprc)))
sorted_prc = [float(cisprc[i][0]) / cisprc[i][1] for i in indices]
size = (max(indices) + 1) if not size else size
win_size = _best_window_size(sorted_prc, size, beg, end, verbose=verbose)
# define confidance bands, compute median plus/minus one standard deviation
errors_pos = []
errors_neg = []
for k in range(0, size, 1):
vals = sorted_prc[k:k + win_size]
std = np.std(vals)
med = np.median(vals)
errors_pos.append(med + std * sigma)
errors_neg.append(med - std * sigma)
# calculate median and variation of median plus/minus one standard deviation
# for values between percentile 10 and 90 of the distribution of the
# percentage of cis interactions
# - for median plus one standard deviation
std_err_pos = np.std(errors_pos[int(size * beg):int(size * end)])
med_err_pos = np.median(errors_pos[int(size * beg):int(size * end)])
# - for median minus one standard deviation
std_err_neg = np.std(errors_neg[int(size * beg):int(size * end)])
med_err_neg = np.median(errors_neg[int(size * beg):int(size * end)])
# define cutoffs, values of cis percentage plus 1 stddev should be between
# the general median +/- 2 stddev of the distribution of the cis percentage
# plus 1 stddev. Same on the side of median cis percentage minus 1 stddev
beg_pos = med_err_pos - std_err_pos * sigma
end_pos = med_err_pos + std_err_pos * sigma
beg_neg = med_err_neg - std_err_neg * sigma
end_neg = med_err_neg + std_err_neg * sigma
cutoffL = None
passed = 0
consecutive = 10
for cutoffL, (p, n) in enumerate(zip(errors_pos, errors_neg)):
# print '%6.4f %6.4f %6.4f %6.4f %6.4f %6.4f' % (beg_pos, p, end_pos, beg_neg, n, end_neg)
if (beg_pos < p < end_pos) and (beg_neg < n < end_neg):
if passed >= consecutive:
break
passed += 1
else:
passed = 0
else:
if min_perc is None:
raise Exception('ERROR: left cutoff not found!!!\n'
' define it by hand with min_perc')
else:
cutoffL = min_perc / 100. * size + consecutive
cutoffL -= consecutive # rescale, we asked for XX consecutive
# right
cutoffR = None
passed = 0
for cutoffR, (p, n) in enumerate(list(zip(errors_pos, errors_neg))[::-1]):
cutoffR = size - cutoffR
# print '%6.4f %6.4f %6.4f %6.4f %6.4f %6.4f' % (beg_pos, p, end_pos, beg_neg, n, end_neg)
if (beg_pos < p < end_pos) and (beg_neg < n < end_neg):
if passed >= consecutive:
break
passed += 1
else:
passed = 0
else:
if max_perc is None:
raise Exception('ERROR: right cutoff not found!!!\n'
' define it by hand with max_perc')
else:
cutoffR = max_perc / 100. * size - consecutive
cutoffR += consecutive # rescale, we asked for XX consecutive
if min_perc:
cutoffL = min_perc / 100. * size
if max_perc:
cutoffR = max_perc / 100. * size
min_count = sorted_sum[int(cutoffL)]
try:
max_count = sorted_sum[int(cutoffR)]
except IndexError: # all good
max_count = sorted_sum[-1] + 1
if verbose:
print(' * Lower cutoff applied until bin number: %d' %
(cutoffL))
print(
' * too few interactions defined as less than %9d interactions'
% (min_count))
print(' * Upper cutoff applied until bin number: %d' %
(cutoffR))
print(
' * too much interactions defined as more than %9d interactions'
% (max_count))
# plot
if savefig:
if verbose:
print(' -> Making plot...')
fig = plt.figure(figsize=(20, 11))
ax1 = fig.add_subplot(111)
plt.subplots_adjust(left=0.25, bottom=0.2)
line1 = ax1.plot([
float(cisprc.get(i, [0, 0])[0]) / cisprc.get(i, [1, 1])[1]
for i in indices
],
'.',
color='grey',
alpha=0.2,
label='cis interactions ratio by bin',
zorder=1)
line2 = ax1.plot(list(range(0, len(indices), 20)), [
sum(
float(cisprc.get(j, [0, 0])[0]) / cisprc.get(j, [1, 1])[1]
for j in indices[k:k + win_size]) / win_size
for k in range(0, len(indices), 20)
],
'.',
color='k',
alpha=0.3,
label='cis interactions ratio by %d bin' % win_size,
zorder=1)
for k, (p, n) in enumerate(
zip(errors_pos[::size // 100], errors_neg[::size // 100])):
ax1.vlines(k * (size // 100), (p + n) // 2,
p,
color='red',
alpha=0.6)
ax1.vlines(k * (size // 100),
n, (p + n) // 2,
color='blue',
alpha=0.6)
ax1.plot(list(range(0, size, size // 100)),
errors_neg[::size // 100],
'b^',
mec='blue',
alpha=0.5)
ax1.plot(list(range(0, size, size // 100)),
errors_pos[::size // 100],
'rv',
mec='red',
alpha=0.5)
ax1.fill_between([0, size],
beg_pos,
end_pos,
color='red',
alpha=0.3,
zorder=2)
ax1.text(-size / 15., (end_pos + beg_pos) / 2,
'Confidance band for\nupper stddev of median',
color='red',
ha='right',
va='center')
ax1.fill_between([0, size],
beg_neg,
end_neg,
color='blue',
alpha=0.3,
zorder=2)
ax1.text(-size / 15., (end_neg + beg_neg) / 2,
'Confidance band for\nlower stddev of median',
color='blue',
ha='right',
va='center')
ax1.set_ylim((0, 1.1))
ax1.set_ylabel('Ratio of cis interactions ratio')
ax1.fill_betweenx([0, 1.1], cutoffL, cutoffR, color='green', alpha=0.2)
ax1.text(
(cutoffR + cutoffL) / 2,
-0.1,
('Kept bins, top and bottom deviations from median cis-ratio\n' +
'should be inside their respective confidance bands'),
ha='center',
color='green')
ax2 = fig.add_subplot(111, sharex=ax1, frameon=False)
line3 = ax2.plot(sorted_sum,
'rx',
alpha=0.4,
label='Log sum of interactions by bin')
ax2.set_yscale('log')
ax2.yaxis.tick_right()
ax2.yaxis.set_label_position("right")
ax2.set_ylabel('Log interaction counts')
lns = line1 + line2 + line3
labs = [l.get_label() for l in lns]
ax2.legend(lns, labs, loc=0, bbox_to_anchor=(0, 0), frameon=False)
ax3 = fig.add_subplot(111, frameon=False)
ax3.xaxis.tick_top()
ax3.set_xticks(list(range(100)), minor=True)
ax3.set_xticks(list(range(0, 100, 5)), minor=False)
ax3.set_yticks([])
ax3.set_xticklabels([])
for p in range(5, 100, 5):
ax3.text(p, 99, '%d%%' % p, va='top', ha='left', size=9)
ax3.tick_params(direction='in', axis='x', which='both')
ax3.set_xlim(0, 100)
ax3.set_ylim(0, 100)
ax3.grid(which='major')
ax3.grid(which='minor', alpha=0.5)
if min_perc:
plt.title('Setting from %.2f%% to %.2f%%' %
(100 * float(cutoffL) / len(indices),
100 * float(cutoffR) / len(indices)))
else:
plt.title('Keeping from %.2f%% to %.2f%%' %
(100 * float(cutoffL) / len(indices),
100 * float(cutoffR) / len(indices)))
ax1.set_xlim((0, len(indices)))
tadbit_savefig(savefig)
plt.close('all')
badcol = {}
countL = 0
countZ = 0
countU = 0
for c in range(size):
if cisprc.get(c, [0, 0])[1] < min_count:
badcol[c] = cisprc.get(c, [0, 0])[1]
countL += 1
if not c in cisprc:
countZ += 1
elif cisprc[c][
1] > max_count: # don't need get here, already cought in previous condition
badcol[c] = cisprc.get(c, [0, 0])[1]
countU += 1
print(
' => %d BAD bins (%d/%d/%d null/low/high counts) of %d (%.1f%%)' %
(len(badcol), countZ, countL, countU, size,
float(len(badcol)) / size * 100))
return badcol
def plot_genomic_distribution(fnam, first_read=True, resolution=10000,
axe=None, ylim=None, savefig=None, show=False,
savedata=None, chr_names=None, nreads=None):
"""
:param fnam: input file name
:param True first_read: uses first read.
:param 100 resolution: group reads that are closer than this resolution
parameter
:param None axe: a matplotlib.axes.Axes object to define the plot
appearance
:param None savefig: path to a file where to save the image generated;
if None, the image will be shown using matplotlib GUI (the extension
of the file name will determine the desired format).
:param None savedata: path where to store the output read counts per bin.
:param None chr_names: can pass a list of chromosome names in case only some
them the need to be plotted (this option may last even more than default)
"""
distr = {}
idx1, idx2 = (1, 3) if first_read else (7, 9)
genome_seq = OrderedDict()
fhandler = open(fnam)
line = fhandler.next()
if chr_names:
chr_names = set(chr_names)
cond1 = lambda x: x not in chr_names
else:
cond1 = lambda x: False
if nreads:
cond2 = lambda x: x >= nreads
else:
cond2 = lambda x: False
cond = lambda x, y: cond1(x) and cond2(y)
count = 0
while line.startswith('#'):
if line.startswith('# CRM '):
crm, clen = line[6:].split('\t')
genome_seq[crm] = int(clen)
line = fhandler.next()
try:
while True:
crm, pos = line.strip().split('\t')[idx1:idx2]
count += 1
if cond(crm, count):
line = fhandler.next()
if cond2(count):
break
continue
pos = int(pos) / resolution
try:
distr[crm][pos] += 1
except KeyError:
try:
distr[crm][pos] = 1
except KeyError:
distr[crm] = {pos: 1}
line = fhandler.next()
except StopIteration:
pass
fhandler.close()
if not axe:
_ = plt.figure(figsize=(15, 1 + 3 * len(
chr_names if chr_names else distr.keys())))
max_y = max([max(distr[c].values()) for c in distr])
max_x = max([len(distr[c].values()) for c in distr])
ncrms = len(chr_names if chr_names else genome_seq if genome_seq else distr)
data = {}
for i, crm in enumerate(chr_names if chr_names else genome_seq
if genome_seq else distr):
try:
data[crm] = [distr[crm].get(j, 0) for j in xrange(max(distr[crm]))]
if savefig:
plt.subplot(ncrms, 1, i + 1)
plt.plot(range(max(distr[crm])), data[crm],
color='red', lw=1.5, alpha=0.7)
except KeyError:
pass
if savefig:
if ylim:
plt.vlines(genome_seq[crm] / resolution, ylim[0], ylim[1])
else:
plt.vlines(genome_seq[crm] / resolution, 0, max_y)
plt.xlim((0, max_x))
plt.ylim(ylim or (0, max_y))
plt.title(crm)
if savefig:
tadbit_savefig(savefig)
plt.close('all')
elif show:
plt.show()
if savedata:
out = open(savedata, 'w')
out.write('# CRM\tstart-end\tcount\n')
out.write('\n'.join('%s\t%d-%d\t%d' % (c, (i * resolution) + 1, ((i + 1) * resolution), v) for c in data
for i, v in enumerate(data[c])))
out.write('\n')
out.close()
def filter_by_mean(matrx,
draw_hist=False,
silent=False,
bads=None,
savefig=None):
"""
fits the distribution of Hi-C interaction count by column in the matrix to
a polynomial. Then searches for the first possible
"""
nbins = 100
if not bads:
bads = {}
# get sum of columns
cols = []
size = len(matrx)
for c in sorted(
[[matrx.get(i + j * size, 0) for j in xrange(size) if not j in bads]
for i in xrange(size) if not i in bads],
key=sum):
cols.append(sum(c))
cols = np.array(cols)
if draw_hist:
plt.figure(figsize=(9, 9))
try:
percentile = np.percentile(cols, 5)
except IndexError:
warn('WARNING: no columns to filter out')
return bads
# mad = np.median([abs(median - c ) for c in cols])
best = (None, None, None, None)
# bin the sum of columns
xmin = min(cols)
xmax = max(cols)
y = np.linspace(xmin, xmax, nbins)
hist = np.digitize(cols, y)
x = [sum(hist == i) for i in range(1, nbins + 1)]
if draw_hist:
hist = plt.hist(cols, bins=100, alpha=.3, color='grey')
xp = range(0, int(cols[-1]))
# check if the binning is correct
# we want at list half of the bins with some data
try:
cnt = 0
while list(x).count(0) > len(x) / 2:
cnt += 1
cols = cols[:-1]
xmin = min(cols)
xmax = max(cols)
y = np.linspace(xmin, xmax, nbins)
hist = np.digitize(cols, y)
x = [sum(hist == i) for i in range(1, nbins + 1)]
if draw_hist:
plt.clf()
hist = plt.hist(cols, bins=100, alpha=.3, color='grey')
xp = range(0, int(cols[-1]))
if cnt > 10000:
raise ValueError
# find best polynomial fit in a given range
for order in range(6, 18):
z = np.polyfit(y, x, order)
zp = np.polyder(z, m=1)
roots = np.roots(np.polyder(z))
# check that we are concave down, otherwise take next root
pente = np.polyval(zp, abs(roots[-2] - roots[-1]) / 2 + roots[-1])
if pente > 0:
root = roots[-1]
else:
root = roots[-2]
# root must be higher than zero
if root <= 0:
continue
# and lower than the median
if root >= percentile:
continue
p = np.poly1d(z)
R2 = get_r2(p, x, y)
# try to avoid very large orders by weigthing negatively their fit
if order > 13:
R2 -= float(order) / 30
if best[0] < R2:
best = (R2, order, p, z, root)
try:
p, z, root = best[2:]
if draw_hist:
xlims = plt.xlim()
ylims = plt.ylim()
a = plt.plot(xp, p(xp), "--", color='k')
b = plt.vlines(root,
0,
plt.ylim()[1],
colors='r',
linestyles='dashed')
# c = plt.vlines(median - mad * 1.5, 0, 110, colors='g',
# linestyles='dashed')
try:
plt.legend(a + [b], [
'polyfit \n%s' % (''.join([
sub(
'e([-+][0-9]+)', 'e^{\\1}', '$%s%.1fx^%s$' %
('+' if j > 0 else '', j, '{' + str(i) + '}'))
for i, j in enumerate(list(p)[::-1])
])), 'first solution of polynomial derivation'
],
fontsize='x-small')
except TypeError:
plt.legend(a + [b], [
'polyfit \n%s' % (''.join([
sub(
'e([-+][0-9]+)', 'e^{\\1}', '$%s%.1fx^%s$' %
('+' if j > 0 else '', j, '{' + str(i) + '}'))
for i, j in enumerate(list(p)[::-1])
])), 'first solution of polynomial derivation'
])
# plt.legend(a+[b]+[c], ['polyfit \n{}'.format (
# ''.join([sub('e([-+][0-9]+)', 'e^{\\1}',
# '${}{:.1}x^{}$'.format ('+' if j>0 else '', j,
# '{' + str(i) + '}'))
# for i, j in enumerate(list(p)[::-1])])),
# 'first solution of polynomial derivation',
# 'median - (1.5 * median absolute deviation)'],
# fontsize='x-small')
plt.ylim([0, ylims[1]])
plt.xlim(xlims)
plt.xlabel('Sum of interactions')
plt.xlabel('Number of columns with a given value')
if savefig:
tadbit_savefig(savefig)
else:
plt.show()
# label as bad the columns with sums lower than the root
for i, col in enumerate(
[[matrx.get(i + j * size, 0) for j in xrange(size)]
for i in xrange(size)]):
if sum(col) < root:
bads[i] = sum(col)
# now stored in Experiment._zeros, used for getting more accurate z-scores
if bads and not silent:
stderr.write(
('\nWARNING: removing columns having less than %s ' +
'counts:\n %s\n') % (round(root, 3), ' '.join([
'%5s' % str(i + 1) + ('' if (j + 1) % 20 else '\n')
for j, i in enumerate(sorted(bads.keys()))
])))
except:
if not silent:
stderr.write('WARNING: Too many zeroes to filter columns.' +
' SKIPPING...\n')
if draw_hist:
plt.xlabel('Sum of interactions')
plt.xlabel('Number of columns with a given value')
if savefig:
tadbit_savefig(savefig)
else:
plt.show()
except ValueError:
if not silent:
stderr.write('WARNING: Too few data to filter columns based on ' +
'mean value.\n')
if draw_hist:
plt.close('all')
return bads
def eig_correlate_matrices(hic_data1, hic_data2, nvect=6, normalized=False,
savefig=None, show=False, savedata=None,
remove_bad_columns=True, **kwargs):
"""
Compare the iteractions of two Hi-C matrices using their 6 first
eigenvectors, with pearson correlation
:param hic_data1: Hi-C-data object
:param hic_data2: Hi-C-data object
:param 6 nvect: number of eigenvectors to compare
:param None savefig: path to save the plot
:param False show: displays the plot
:param False normalized: use normalized data
:param True remove_bads: computes the union of bad columns between samples
and exclude them from the comparison
:param kwargs: any argument to pass to matplotlib imshow function
:returns: matrix of correlations
"""
data1 = hic_data1.get_matrix(normalized=normalized)
data2 = hic_data2.get_matrix(normalized=normalized)
## reduce matrices to remove bad columns
if remove_bad_columns:
# union of bad columns
bads = hic_data1.bads.copy()
bads.update(hic_data2.bads)
# remove them form both matrices
for bad in sorted(bads, reverse=True):
del(data1[bad])
del(data2[bad])
for i in xrange(len(data1)):
_ = data1[i].pop(bad)
_ = data2[i].pop(bad)
# get the log
size = len(data1)
data1 = nozero_log(data1, np.log2)
data2 = nozero_log(data2, np.log2)
# get the eigenvectors
ev1, evect1 = eigh(data1)
ev2, evect2 = eigh(data2)
corr = [[0 for _ in xrange(nvect)] for _ in xrange(nvect)]
# sort eigenvectors according to their eigenvalues => first is last!!
sort_perm = ev1.argsort()
ev1.sort()
evect1 = evect1[sort_perm]
sort_perm = ev2.argsort()
ev2.sort()
evect2 = evect2[sort_perm]
# calculate Pearson correlation
for i in xrange(nvect):
for j in xrange(nvect):
corr[i][j] = abs(pearsonr(evect1[:,-i-1],
evect2[:,-j-1])[0])
# plot
axe = plt.axes([0.1, 0.1, 0.6, 0.8])
cbaxes = plt.axes([0.85, 0.1, 0.03, 0.8])
if show or savefig:
im = axe.imshow(corr, interpolation="nearest",origin='lower', **kwargs)
axe.set_xlabel('Eigen Vectors exp. 1')
axe.set_ylabel('Eigen Vectors exp. 2')
axe.set_xticks(range(nvect))
axe.set_yticks(range(nvect))
axe.set_xticklabels(range(1, nvect + 2))
axe.set_yticklabels(range(1, nvect + 2))
axe.xaxis.set_tick_params(length=0, width=0)
axe.yaxis.set_tick_params(length=0, width=0)
cbar = plt.colorbar(im, cax = cbaxes )
cbar.ax.set_ylabel('Pearson correlation', rotation=90*3,
verticalalignment='bottom')
axe2 = axe.twinx()
axe2.set_yticks(range(nvect))
axe2.set_yticklabels(['%.1f' % (e) for e in ev2[-nvect:][::-1]])
axe2.set_ylabel('corresponding Eigen Values exp. 2', rotation=90*3,
verticalalignment='bottom')
axe2.set_ylim((-0.5, nvect - 0.5))
axe2.yaxis.set_tick_params(length=0, width=0)
axe3 = axe.twiny()
axe3.set_xticks(range(nvect))
axe3.set_xticklabels(['%.1f' % (e) for e in ev1[-nvect:][::-1]])
axe3.set_xlabel('corresponding Eigen Values exp. 1')
axe3.set_xlim((-0.5, nvect - 0.5))
axe3.xaxis.set_tick_params(length=0, width=0)
axe.set_ylim((-0.5, nvect - 0.5))
axe.set_xlim((-0.5, nvect - 0.5))
if savefig:
tadbit_savefig(savefig)
if show:
plt.show()
plt.close('all')
if savedata:
out = open(savedata, 'w')
out.write('# ' + '\t'.join(['Eigen Vector %s'% i
for i in xrange(nvect)]) + '\n')
for i in xrange(nvect):
out.write('\t'.join([str(corr[i][j])
for j in xrange(nvect)]) + '\n')
out.close()
if kwargs.get('get_bads', False):
return corr, bads
else:
return corr
def quality_plot(fnam, r_enz=None, nreads=None, axe=None, savefig=None, paired=False):
"""
Plots the sequencing quality of a given FASTQ file. If a restrinction enzyme
(RE) name is provided, can also represent the distribution of digested and
undigested RE sites and estimate an expected proportion of dangling-ends.
Proportion of dangling-ends is inferred by counting the number of times a
dangling-end site, is found at the beginning of any of the reads (divided by
the number of reads).
:param fnam: path to FASTQ file
:param None nreads: max number of reads to read, not necesary to read all
:param None savefig: path to a file where to save the image generated;
if None, the image will be shown using matplotlib GUI (the extension
of the file name will determine the desired format).
:param False paired: is input FASTQ contains both ends
:returns: the percentage of dangling-ends (sensu stricto) and the percentage of
reads with at least a ligation site.
"""
phred = dict([(c, i) for i, c in enumerate(
'!"#$%&\'()*+,-./0123456789:;<=>?@ABCDEFGHIJKLMNOPQRSTUVWXYZ[\]^_`abcdefghijklmnopqrstuvwxyz{|}~')])
quals = []
henes = []
sites = []
fixes = []
liges = []
ligep = 0
tkw = dict(size=4, width=1.5)
if fnam.endswith('.gz'):
fhandler = gopen(fnam)
else:
fhandler = open(fnam)
if not r_enz:
if nreads:
while True:
try:
next(fhandler)
except EOFError:
break
seq = next(fhandler)
if 'N' in seq:
henes.extend([i for i, s in enumerate(seq) if s == 'N'])
next(fhandler)
line = next(fhandler)
quals.append([phred[i] for i in line.strip()])
if len(quals) > nreads:
break
else: # do this because it's faster
while True:
try:
next(fhandler)
except EOFError:
break
seq = next(fhandler)
if 'N' in seq:
henes.extend([i for i, s in enumerate(seq) if s == 'N'])
next(fhandler)
line = next(fhandler)
quals.append([phred[i] for i in line.strip()])
else:
r_site = RESTRICTION_ENZYMES[r_enz].replace('|', '')
l_site = religated(r_enz)
d_site = repaired(r_enz)
if r_site*2 == l_site:
# in case the religated site equals 2 restriction sites (like DnpII)
site = re.compile('(?<!%s)' % r_site + r_site + '(?!%s)' % r_site)
fixe = re.compile('(?<!%s)' % d_site + d_site + '(?!%s)' % d_site)
else:
site = re.compile(r_site)
fixe = re.compile(d_site)
lige = re.compile(l_site)
if nreads:
while True:
try:
next(fhandler)
except StopIteration:
break
seq = next(fhandler)
sites.extend([m.start() for m in site.finditer(seq)])
fixes.extend([m.start() for m in fixe.finditer(seq)])
liges.extend([m.start() for m in lige.finditer(seq)])
ligep += l_site in seq
if 'N' in seq:
henes.extend([i for i, s in enumerate(seq) if s == 'N'])
next(fhandler)
line = next(fhandler)
quals.append([phred[i] for i in line.strip()])
if len(quals) > nreads:
break
else: # do this because it's faster
while True:
try:
next(fhandler)
except StopIteration:
break
seq = next(fhandler)
sites.extend([m.start() for m in site.finditer(seq)])
fixes.extend([m.start() for m in fixe.finditer(seq)])
liges.extend([m.start() for m in lige.finditer(seq)])
ligep += l_site in seq
if 'N' in seq:
henes.extend([i for i, s in enumerate(seq) if s == 'N'])
next(fhandler)
line = next(fhandler)
quals.append([phred[i] for i in line.strip()])
fhandler.close()
if not nreads:
nreads = len(quals)
quals = zip(*quals)
meanquals = [np.mean(q) for q in quals]
errorquals = [np.std(q) for q in quals]
if axe:
ax = axe
fig = axe.get_figure()
ax2 = fig.add_subplot(212)
else:
if r_enz:
_, (ax, ax2) = plt.subplots(2,1, figsize=(15, 12))
else:
_, ax = plt.subplots(1,1, figsize=(15, 6))
ax.patch.set_facecolor('lightgrey')
ax.patch.set_alpha(0.4)
ax.grid(ls='-', color='w', lw=1.5, alpha=0.6, which='major')
ax.grid(ls='-', color='w', lw=1, alpha=0.3, which='minor')
ax.set_axisbelow(True)
# remove tick marks
ax.tick_params(axis='both', direction='out', top=False, right=False,
left=False, bottom=False)
ax.tick_params(axis='both', direction='out', top=False, right=False,
left=False, bottom=False, which='minor')
ax.errorbar(range(len(line.strip())), meanquals,
linewidth=1, elinewidth=1, color='darkblue',
yerr=errorquals, ecolor='orange')
ax.set_xlim((0, len(line)))
ax.set_xlabel('Nucleotidic position')
ax.set_ylabel('PHRED score')
ax.set_title('Sequencing Quality (%d reads)' % (nreads))
ax.yaxis.label.set_color('darkblue')
ax.tick_params(axis='y', colors='darkblue', **tkw)
axb = ax.twinx()
axb.plot([henes.count(i) for i in xrange(len(line))], linewidth=1,
color='black', linestyle='--')
axb.yaxis.label.set_color('black')
axb.tick_params(axis='y', colors='black', **tkw)
axb.set_ylabel('Number of "N" per position')
try: # no Ns found (yes... it happens)
axb.set_yscale('log')
axb.set_ylim((0, axb.get_ylim()[1] * 1000))
except ValueError:
axb.set_yscale('linear')
ax.set_ylim((0, ax.get_ylim()[1]))
ax.set_xlim((0, len(line)))
if r_enz:
ax.set_title('Sequencing Quality and deconvolution (%s %d reads)' % (
r_enz, nreads))
ax.set_xlabel('')
plt.setp(ax.get_xticklabels(), visible=False)
ax2.patch.set_facecolor('lightgrey')
ax2.patch.set_alpha(0.4)
ax2.grid(ls='-', color='w', lw=1.5, alpha=0.6, which='major')
ax2.grid(ls='-', color='w', lw=1, alpha=0.3, which='minor')
ax2.set_axisbelow(True)
ax2.set_xlabel('Nucleotidic position')
seq_len = len(line) - max((len(r_site), len(l_site), len(d_site)))
sites = [sites.count(k) for k in xrange(seq_len)] # Undigested
liges = [liges.count(k) for k in xrange(seq_len)] # OK
fixes = [fixes.count(k) for k in xrange(seq_len)] # DE
if d_site in r_site:
pos = r_site.find(d_site)
fixes = (fixes[:pos] +
[fixes[k] - sites[k-pos] for k in xrange(pos, seq_len)])
if d_site in l_site:
pos = l_site.find(d_site)
fixes = (fixes[:pos] +
[fixes[k] - liges[k-pos] for k in xrange(pos, seq_len)])
site_len = max((len(r_site), len(l_site), len(d_site)))
if paired:
sites[len(line) / 2 - site_len:
len(line) / 2] = [float('nan')] * site_len
liges[len(line) / 2 - site_len:
len(line) / 2] = [float('nan')] * site_len
fixes[len(line) / 2 - site_len:
len(line) / 2] = [float('nan')] * site_len
ax2.plot(sites, linewidth=2, color='darkred')
ax2.set_ylabel('Undigested RE site (%s)' % r_site)
ax2.yaxis.label.set_color('darkred')
ax2.tick_params(axis='y', colors='darkred', **tkw)
ax3 = ax2.twinx()
ax3.plot(liges, linewidth=2, color='darkblue')
ax3.yaxis.label.set_color('darkblue')
ax3.tick_params(axis='y', colors='darkblue', **tkw)
ax3.set_ylabel('Religated (%s)' % l_site)
if any([f > 0 for f in fixes]):
ax4 = ax2.twinx()
ax4.spines["right"].set_position(("axes", 1.07))
make_patch_spines_invisible(ax4)
ax4.spines["right"].set_visible(True)
ax4.plot(fixes, linewidth=2, color='darkorange')
ax4.yaxis.label.set_color('darkorange')
ax4.tick_params(axis='y', colors='darkorange', **tkw)
ax4.set_ylabel('Dangling-ends (%s)' % d_site)
else:
ax2.set_ylabel('RE site & Dangling-ends (%s)' % r_site)
ax2.set_xlim((0, len(line)))
lig_cnt = (np.nansum(liges) - liges[0] - liges[len(line) / 2])
sit_cnt = (np.nansum(sites) - sites[0] - sites[len(line) / 2])
des = ((100. * (fixes[0] + (fixes[(len(line) / 2)]
if paired else 0)))
/ nreads) if any([f > 0 for f in fixes]) else (
100. * (sites[0] + (sites[(len(line) / 2)] if paired else 0))) / nreads
plt.title(('Percentage of digested sites: %.0f%%, of dangling-ends: %.0f%%\n' +
'Percentage of reads with ligation site: %.0f%%') %(
(100. * lig_cnt) / (lig_cnt + sit_cnt),
des,
(ligep * 100.) / nreads))
plt.subplots_adjust(right=0.85)
if savefig:
tadbit_savefig(savefig)
plt.close('all')
elif not axe:
plt.show()
return des, (ligep * 100.) / nreads
def draw(self, focus=None, extras=None, ymax=None, ali_colors=('grey',),
normalized=True, savefig=None, shape='ellipse'):
"""
Draw alignments as a plot.
:param None focus: can pass a tuple (bin_start, bin_stop) to display the
alignment between these genomic bins
:param None extras: list of coordinates (genomic bin) where to draw a
red cross
:param None ymax: limit the y axis up to a given value
:param ('grey', ): successive colors for alignment
:param True normalized: normalized Hi-C count are plotted instead of raw
data.
:param 'ellipse' shape: which kind of shape to use as schematic
representation of TADs. Implemented: 'ellipse', 'rectangle',
'triangle'
:param None savefig: path to a file where to save the image generated;
if None, the image will be shown using matplotlib GUI (the extension
of the file name will determine the desired format).
"""
from matplotlib.cm import jet
from matplotlib import pyplot as plt
experiments = self.__experiments
maxres = max([e.resolution for e in experiments])
facts = [maxres / e.resolution for e in experiments]
siz = experiments[0].size
if focus:
figsiz = 4 + (focus[1] - focus[0]) / 30
else:
figsiz = 4 + siz / 30
fig, axes = plt.subplots(nrows=len(experiments),
sharex=True, sharey=True,
figsize=(figsiz, 1 + len(experiments) * 1.8))
fig.subplots_adjust(hspace=0)
maxys = []
for iex, xpr in enumerate(experiments):
if not xpr.name in self:
continue
_tad_density_plot(xpr, maxys=maxys, normalized=normalized,
fact_res=facts[iex], axe=axes[iex],
extras=extras, shape=shape, focus=focus)
# draw alignment columns
start = focus[0] if focus else 1
end = focus[1] if focus else xpr.tads[max(xpr.tads)]['end']
maxy = (ymax or max(maxys)) + 0.4
maxxs = []
for iex in range(len(experiments)):
starting = focus[0] if focus else 1
ending = (focus[1] if focus
else experiments[iex].tads.values()[-1]['end'])
axes[iex].hlines(1, 1, end, 'k', lw=1.5)
axes[iex].set_ylim((0, maxy))
maxxs.append(ending / facts[iex])
axes[iex].text(starting + 1, float(maxy) / 20,
experiments[iex].name, {'ha': 'left',
'va': 'bottom'})
axes[iex].set_yticks([float(i) / 2
for i in range(1, int(maxy + .5) * 2)])
if ymax:
axes[iex].set_ylim((0, ymax))
axes[iex].set_xlim(xmin=starting, xmax=max(maxxs))
pos = {'ha': 'center', 'va': 'bottom'}
for i, col in enumerate(self.itercolumns()):
ends = sorted([(t['end'], j) for j, t in enumerate(col) if t['end']])
beg = (ends[0][0] + 0.9) / facts[ends[0][1]]
end = (ends[-1][0] + 1.1) / facts[ends[-1][1]]
if focus:
if beg < focus[0] or end > focus[1]:
continue
axes[0].text(beg + float(end - beg) / 2, maxy + float(maxy) / 20,
str(i + 1), pos,
rotation=90, size='small')
for iex, tad in enumerate(col):
if not tad['end']:
continue
axes[iex].axvspan(beg-.2, end+.2, alpha=0.2,
color=ali_colors[i%(len(ali_colors))])
axes[iex].set_xlabel('Genomic bin')
tit1 = fig.suptitle("TAD borders' alignment", size='x-large')
tit2 = axes[0].set_title("Alignment column number")
tit2.set_y(1.3)
plt.subplots_adjust(top=0.76)
# This was for color bar instead of legend
# ax1 = fig.add_axes([0.9 + 0.3/figsiz, 0.05, 0.2/figsiz, 0.9])
# cb1 = colorbar.ColorbarBase(ax1, cmap=jet,
# norm=colors.Normalize(vmin=0., vmax=1.))
# cb1.set_label('Border prediction score')
# cb1.ax.set_yticklabels([str(i)for i in range(1, 11)])
fig.set_facecolor('white')
plots = []
for scr in xrange(1, 11):
plots += plt.plot((100,),(100,), marker=6, ms=9,
color=jet(float(scr) / 10), mec='none')
try:
axes[-1].legend(plots,
[str(scr) for scr in xrange(1, 11)],
numpoints=1, title='Border scores',
fontsize='small', loc='lower left',
bbox_to_anchor=(1, 0.5))
except TypeError:
axes[-1].legend(plots,
[str(scr) for scr in xrange(1, 11)],
numpoints=1, title='Border scores',
loc='lower left',
bbox_to_anchor=(1, 0.5))
if savefig:
tadbit_savefig(savefig)
else:
plt.show()
def draw_map(data, genome_seq, cumcs, savefig, show, one=False, clim=None,
cmap='jet', decay=False, perc=10, name=None, cistrans=None,
decay_resolution=10000, normalized=False, max_diff=None):
_ = plt.figure(figsize=(15.,12.5))
if not max_diff:
max_diff = len(data)
ax1 = plt.axes([0.34, 0.08, 0.6, 0.7205])
ax2 = plt.axes([0.07, 0.65, 0.21, 0.15])
if decay:
ax3 = plt.axes([0.07, 0.42, 0.21, 0.15])
plot_distance_vs_interactions(data, genome_seq=genome_seq, axe=ax3,
resolution=decay_resolution,
max_diff=max_diff, normalized=normalized)
ax4 = plt.axes([0.34, 0.805, 0.6, 0.04], sharex=ax1)
ax5 = plt.axes([0.34, 0.845, 0.6, 0.04], sharex=ax1)
ax6 = plt.axes([0.34, 0.885, 0.6, 0.04], sharex=ax1)
try:
minoridata = np.nanmin(data)
maxoridata = np.nanmax(data)
except AttributeError:
vals = [i for d in data for i in d if not np.isnan(i)]
minoridata = np.min(vals)
maxoridata = np.max(vals)
totaloridata = np.nansum([data[i][j] for i in xrange(len(data))
for j in xrange(i, len(data))])
data = nozero_log(data, np.log2)
vals = np.array([i for d in data for i in d])
vals = vals[np.isfinite(vals)]
mindata = np.nanmin(vals)
maxdata = np.nanmax(vals)
diff = maxdata - mindata
posI = 0.01 if not clim else (float(clim[0]) / diff) if clim[0] != None else 0.01
posF = 1.0 if not clim else (float(clim[1]) / diff) if clim[1] != None else 1.0
if cmap == 'tadbit':
cuts = perc
cdict = {'red' : [(0.0, 0.0, 0.0)],
'green': [(0.0, 0.0, 0.0)],
'blue' : [(0.0, 0.5, 0.5)]}
prev_pos = 0
median = (np.median(vals) - mindata) / diff
for prc in np.linspace(posI, median, cuts / 2, endpoint=False):
try:
pos = (np.percentile(vals, prc * 100.) - mindata) / diff
prc = ((prc - posI) / (median - posI)) + 1. / cuts
except ValueError:
pos = prc = 0
if prev_pos >= pos:
continue
cdict['red' ].append([pos, prc, prc])
cdict['green'].append([pos, prc, prc])
cdict['blue' ].append([pos, 1, 1])
prev_pos = pos
for prc in np.linspace(median + 1. / cuts, posF, cuts / 2, endpoint=False):
try:
pos = (np.percentile(vals, prc * 100.) - mindata) / diff
prc = ((prc - median) / (posF - median))
except ValueError:
pos = prc = 0
if prev_pos >= pos:
continue
cdict['red' ].append([pos, 1.0, 1.0])
cdict['green'].append([pos, 1 - prc, 1 - prc])
cdict['blue' ].append([pos, 1 - prc, 1 - prc])
prev_pos = pos
pos = (np.percentile(vals ,97.) - mindata) / diff
cdict['red' ].append([pos, 0.1, 0.1])
cdict['green'].append([pos, 0, 0])
cdict['blue' ].append([pos, 0, 0])
cdict['red' ].append([1.0, 1, 1])
cdict['green'].append([1.0, 1, 1])
cdict['blue' ].append([1.0, 0, 0])
cmap = LinearSegmentedColormap(cmap, cdict)
clim = None
else:
cmap = plt.get_cmap(cmap)
cmap.set_bad('darkgrey', 1)
ax1.imshow(data, interpolation='none',
cmap=cmap, vmin=clim[0] if clim else None, vmax=clim[1] if clim else None)
size = len(data)
for i in xrange(size):
for j in xrange(i, size):
if np.isnan(data[i][j]):
data[i][j] = 0
data[j][i] = 0
#data[j][i] = data[i][j]
evals, evect = eigh(data)
sort_perm = evals.argsort()
evect = evect[sort_perm]
data = [i for d in data for i in d if not np.isnan(i)]
gradient = np.linspace(np.nanmin(data),
np.nanmax(data), size)
gradient = np.vstack((gradient, gradient))
h = ax2.hist(data, color='darkgrey', linewidth=2,
bins=20, histtype='step', normed=True)
_ = ax2.imshow(gradient, aspect='auto', cmap=cmap,
extent=(np.nanmin(data), np.nanmax(data) , 0, max(h[0])))
if genome_seq:
for crm in genome_seq:
ax1.vlines([cumcs[crm][0]-.5, cumcs[crm][1]-.5], cumcs[crm][0]-.5, cumcs[crm][1]-.5,
color='w', linestyle='-', linewidth=1, alpha=1)
ax1.hlines([cumcs[crm][1]-.5, cumcs[crm][0]-.5], cumcs[crm][0]-.5, cumcs[crm][1]-.5,
color='w', linestyle='-', linewidth=1, alpha=1)
ax1.vlines([cumcs[crm][0]-.5, cumcs[crm][1]-.5], cumcs[crm][0]-.5, cumcs[crm][1]-.5,
color='k', linestyle='--')
ax1.hlines([cumcs[crm][1]-.5, cumcs[crm][0]-.5], cumcs[crm][0]-.5, cumcs[crm][1]-.5,
color='k', linestyle='--')
if not one:
vals = [0]
keys = ['']
for crm in genome_seq:
vals.append(cumcs[crm][0])
keys.append(crm)
vals.append(cumcs[crm][1])
ax1.set_yticks(vals)
ax1.set_yticklabels('')
ax1.set_yticks([float(vals[i]+vals[i+1])/2
for i in xrange(len(vals) - 1)], minor=True)
ax1.set_yticklabels(keys, minor=True)
for t in ax1.yaxis.get_minor_ticks():
t.tick1On = False
t.tick2On = False
# totaloridata = ''.join([j + ('' if (i+1)%3 else ',') for i, j in enumerate(str(totaloridata)[::-1])])[::-1].strip(',')
# minoridata = ''.join([j + ('' if (i+1)%3 else ',') for i, j in enumerate(str(minoridata)[::-1])])[::-1].strip(',')
# maxoridata = ''.join([j + ('' if (i+1)%3 else ',') for i, j in enumerate(str(maxoridata)[::-1])])[::-1].strip(',')
plt.figtext(0.05,0.25, ''.join([
(name + '\n') if name else '',
'Number of interactions: %s\n' % str(totaloridata),
('' if np.isnan(cistrans) else
('Percentage of cis interactions: %.0f%%\n' % (cistrans*100))),
'Min interactions: %s\n' % (minoridata),
'Max interactions: %s\n' % (maxoridata)]))
ax2.set_xlim((np.nanmin(data), np.nanmax(data)))
ax2.set_ylim((0, max(h[0])))
ax1.set_xlim ((-0.5, size - .5))
ax1.set_ylim ((-0.5, size - .5))
ax2.set_xlabel('log interaction count')
# we reduce the number of dots displayed.... we just want to see the shape
subdata = np.array(list(set([float(int(d*100))/100 for d in data])))
try:
normfit = sc_norm.pdf(subdata, np.nanmean(data), np.nanstd(data))
except AttributeError:
normfit = sc_norm.pdf(subdata, np.mean(data), np.std(data))
ax2.plot(subdata, normfit, 'w.', markersize=2.5, alpha=.4)
ax2.plot(subdata, normfit, 'k.', markersize=1.5, alpha=1)
ax2.set_title('skew: %.3f, kurtosis: %.3f' % (skew(data),
kurtosis(data)))
ax4.vlines(range(size), 0, evect[:,-1], color='k')
ax4.hlines(0, 0, size, color='red')
ax4.set_ylabel('E1')
ax4.set_yticklabels([])
try:
ax5.vlines(range(size), 0, evect[:,-2], color='k')
except IndexError:
pass
ax5.hlines(0, 0, size, color='red')
ax5.set_ylabel('E2')
ax5.set_yticklabels([])
try:
ax6.vlines(range(size), 0, evect[:,-3], color='k')
except IndexError:
pass
ax6.hlines(0, 0, size, color='red')
ax6.set_ylabel('E3')
ax6.set_yticklabels([])
xticklabels = ax4.get_xticklabels() + ax5.get_xticklabels() + ax6.get_xticklabels()
plt.setp(xticklabels, visible=False)
if savefig:
tadbit_savefig(savefig)
elif show:
plt.show()
plt.close('all')
def visualize(self, names=None, tad=None, focus=None, paint_tads=False,
axe=None, show=True, logarithm=True, normalized=False,
relative=True, decorate=True, savefig=None, clim=None,
scale=(8, 6), cmap='jet'):
"""
Visualize the matrix of Hi-C interactions of a given experiment
:param None names: name of the experiment to visualize, or list of
experiment names. If None, all experiments will be shown
:param None tad: a given TAD in the form:
::
{'start': start,
'end' : end,
'brk' : end,
'score': score}
**Alternatively** a list of the TADs can be passed (all the TADs
between the first and last one passed will be showed. Thus, passing
more than two TADs might be superfluous)
:param None focus: a tuple with the start and end positions of the
region to visualize
:param False paint_tads: draw a box around the TADs defined for this
experiment
:param None axe: an axe object from matplotlib can be passed in order
to customize the picture
:param True show: either to pop-up matplotlib image or not
:param True logarithm: show the logarithm values
:param True normalized: show the normalized data (weights might have
been calculated previously). *Note: white rows/columns may appear in
the matrix displayed; these rows correspond to filtered rows (see*
:func:`pytadbit.utils.hic_filtering.hic_filtering_for_modelling` *)*
:param True relative: color scale is relative to the whole matrix of
data, not only to the region displayed
:param True decorate: draws color bar, title and axes labels
:param None savefig: path to a file where to save the image generated;
if None, the image will be shown using matplotlib GUI (the extension
of the file name will determine the desired format).
:param None clim: tuple with minimum and maximum value range for color
scale. I.e. clim=(-4, 10)
"""
if names == None:
names = [xpr.name for xpr in self.experiments]
if not isinstance(names, list) and not isinstance(names, tuple):
names = [names]
cols = 1
rows = 1
else:
sqrtxpr = sqrt(len(names))
cols = int(round(sqrtxpr + (0.0 if int(sqrtxpr)==sqrtxpr else .5)))
rows = int(sqrtxpr+.5)
notaxe = axe == None
if not scale:
scale = (8, 6)
if notaxe and len(names) != 1:
fig = plt.figure(figsize=(scale[0] * cols, scale[1] * rows))
for i in xrange(rows):
for j in xrange(cols):
if i * cols + j >= len(names):
break
if notaxe and len(names) != 1:
axe = fig.add_subplot(
rows, cols, i * cols + j + 1)
if (isinstance(names[i * cols + j], tuple) or
isinstance(names[i * cols + j], list)):
if not axe:
fig = plt.figure(figsize=(scale[0] * cols, scale[1] * rows))
axe = fig.add_subplot(
rows, cols, i * cols + j + 1)
xpr1 = self.get_experiment(names[i * cols + j][0])
xpr2 = self.get_experiment(names[i * cols + j][1])
img = xpr1.view(tad=tad, focus=focus, paint_tads=paint_tads,
axe=axe, show=False, logarithm=logarithm,
normalized=normalized, relative=relative,
decorate=decorate, savefig=False,
where='up', clim=clim, cmap=cmap)
img = xpr2.view(tad=tad, focus=focus, paint_tads=paint_tads,
axe=axe, show=False, logarithm=logarithm,
normalized=normalized, relative=relative,
decorate=False, savefig=False, where='down',
clim=clim or img.get_clim(), cmap=cmap)
#axe = axe.twinx()
#axe.set_aspect('equal',adjustable='box-forced',anchor='NE')
if decorate:
plt.text(1.01, .5,
'Chromosome %s experiment %s' % (
self.name, xpr2.name),
rotation=-90, va='center', size='large',
ha='left', transform=axe.transAxes)
else:
xper = self.get_experiment(names[i * cols + j])
if not xper.hic_data and not xper.norm:
continue
xper.view(tad=tad, focus=focus, paint_tads=paint_tads,
axe=axe, show=False, logarithm=logarithm,
normalized=normalized, relative=relative,
decorate=decorate, savefig=False, clim=clim,
cmap=cmap)
if savefig:
tadbit_savefig(savefig)
if show:
plt.show()
def plot_distance_vs_interactions(data, min_diff=1, max_diff=1000, show=False,
genome_seq=None, resolution=None, axe=None,
savefig=None, normalized=False):
"""
:param data: input file name, or HiC_data object or list of lists
:param 10 min_diff: lower limit (in number of bins)
:param 1000 max_diff: upper limit (in number of bins) to look for
:param 100 resolution: group reads that are closer than this resolution
parameter
:param None axe: a matplotlib.axes.Axes object to define the plot
appearance
:param None savefig: path to a file where to save the image generated;
if None, the image will be shown using matplotlib GUI (the extension
of the file name will determine the desired format).
"""
resolution = resolution or 1
dist_intr = dict([(i, 0) for i in xrange(min_diff, max_diff)])
if isinstance(data, str):
fhandler = open(data)
line = fhandler.next()
while line.startswith('#'):
line = fhandler.next()
try:
while True:
_, cr1, ps1, _, _, _, _, cr2, ps2, _ = line.split('\t', 9)
if cr1 != cr2:
line = fhandler.next()
continue
diff = abs(int(ps1) / resolution - int(ps2) / resolution)
if max_diff > diff >= min_diff:
dist_intr[diff] += 1
line = fhandler.next()
except StopIteration:
pass
fhandler.close()
elif isinstance(data, HiC_data):
if normalized:
get_data = lambda x, y: data[x, y] / data.bias[x] / data.bias[y]
else:
get_data = lambda x, y: data[x, y]
max_diff = min(len(data), max_diff)
if data.section_pos:
for crm in data.section_pos:
for diff in xrange(min_diff, min(
(max_diff, 1 + data.chromosomes[crm]))):
for i in xrange(data.section_pos[crm][0],
data.section_pos[crm][1] - diff):
dist_intr[diff] += get_data(i, i + diff)
else:
for diff in xrange(min_diff, max_diff):
for i in xrange(len(data) - diff):
if not np.isnan(data[i, i + diff]):
dist_intr[diff] += get_data(i, diff)
else:
if genome_seq:
max_diff = min(max(genome_seq.values()), max_diff)
cnt = 0
for crm in genome_seq:
for diff in xrange(min_diff, min(
(max_diff, genome_seq[crm]))):
for i in xrange(cnt, cnt + genome_seq[crm] - diff):
if not np.isnan(data[i][i + diff]):
dist_intr[diff] += data[i][i + diff]
cnt += genome_seq[crm]
else:
max_diff = min(len(data), max_diff)
for diff in xrange(min_diff, max_diff):
for i in xrange(len(data) - diff):
if not np.isnan(data[i][i + diff]):
dist_intr[diff] += data[i][i + diff]
if not axe:
fig=plt.figure()
axe = fig.add_subplot(111)
# remove last part of the plot in case no interaction is count... reduce max_dist
for diff in xrange(max_diff - 1, min_diff, -1):
try:
if not dist_intr[diff]:
del(dist_intr[diff])
max_diff -=1
continue
except KeyError:
max_diff -=1
continue
break
xp, yp = zip(*sorted(dist_intr.items(), key=lambda x:x[0]))
x = []
y = []
for k in xrange(len(xp)):
if yp[k]:
x.append(xp[k])
y.append(yp[k])
axe.plot(x, y, 'k.')
best = (float('-inf'), 0, 0, 0, 0, 0, 0, 0, 0, 0)
logx = np.log(x)
logy = np.log(y)
ntries = 100
# set k for better fit
# for k in xrange(1, ntries/5, ntries/5/5):
if resolution == 1:
k = 1
for i in xrange(3, ntries-2-k):
v1 = i * len(x) / ntries
try:
a1, b1, r21, _, _ = linregress(logx[ :v1], logy[ :v1])
except ValueError:
a1 = b1 = r21 = 0
r21 *= r21
for j in xrange(i + 1 + k, ntries - 2 - k):
v2 = j * len(x) / ntries
try:
a2, b2, r22, _, _ = linregress(logx[v1+k:v2], logy[v1+k:v2])
a3, b3, r23, _, _ = linregress(logx[v2+k: ], logy[v2+k: ])
except ValueError:
a2 = b2 = r22 = 0
a3 = b3 = r23 = 0
r2 = r21 + r22**2 + r23**2
if r2 > best[0]:
best = (r2, v1, v2, a1, a2, a3,
b1, b2, b3, k)
# plot line of best fit
(v1, v2,
a1, a2, a3,
b1, b2, b3, k) = best[1:]
yfit1 = lambda xx: np.exp(b1 + a1*np.array (np.log(xx)))
yfit2 = lambda xx: np.exp(b2 + a2*np.array (np.log(xx)))
yfit3 = lambda xx: np.exp(b3 + a3*np.array (np.log(xx)))
axe.plot(x[ :v1], yfit1(x[ :v1] ), color= 'yellow', lw=2,
label = r'$\alpha_{%s}=%.2f$' % (
'0-0.7 \mathrm{ Mb}' if resolution != 1 else '1', a1))
#label = r'$\alpha_1=%.2f$ (0-%d)' % (a1, x[v1]))
axe.plot(x[v1+k:v2], yfit2(x[v1+k:v2]), color= 'orange', lw=2,
label = r'$\alpha_{%s}=%.2f$' % (
'0.7-10 \mathrm{ Mb}' if resolution != 1 else '2', a2))
# label = r'$\alpha_2=%.2f$ (%d-%d)' % (a2, x[v1], x[v2]))
axe.plot(x[v2+k: ], yfit3(x[v2+k: ] ), color= 'red' , lw=2,
label = r'$\alpha_{%s}=%.2f$' % (
'10 \mathrm{ Mb}-\infty' if resolution != 1 else '3', a3))
# label = r'$\alpha_3=%.2f$ (%d-$\infty$)' % (a3, x[v2+k]))
else:
# from 0.7 Mb
v1 = 700000 / resolution
# to 10 Mb
v2 = 10000000 / resolution
try:
a1, b1, r21, _, _ = linregress(logx[ :v1], logy[ :v1])
except ValueError:
a1, b1, r21 = 0, 0, 0
try:
a2, b2, r22, _, _ = linregress(logx[v1:v2], logy[v1:v2])
except ValueError:
a2, b2, r22 = 0, 0, 0
try:
a3, b3, r23, _, _ = linregress(logx[v2: ], logy[v2: ])
except ValueError:
a3, b3, r23 = 0, 0, 0
yfit1 = lambda xx: np.exp(b1 + a1*np.array (np.log(xx)))
yfit2 = lambda xx: np.exp(b2 + a2*np.array (np.log(xx)))
yfit3 = lambda xx: np.exp(b3 + a3*np.array (np.log(xx)))
axe.plot(x[ :v1], yfit1(x[ :v1] ), color= 'yellow', lw=2,
label = r'$\alpha_{%s}=%.2f$' % (
'0-0.7 \mathrm{ Mb}' if resolution != 1 else '1', a1))
#label = r'$\alpha_1=%.2f$ (0-%d)' % (a1, x[v1]))
axe.plot(x[v1:v2], yfit2(x[v1:v2]), color= 'orange', lw=2,
label = r'$\alpha_{%s}=%.2f$' % (
'0.7-10 \mathrm{ Mb}' if resolution != 1 else '2', a2))
# label = r'$\alpha_2=%.2f$ (%d-%d)' % (a2, x[v1], x[v2]))
axe.plot(x[v2: ], yfit3(x[v2: ] ), color= 'red' , lw=2,
label = r'$\alpha_{%s}=%.2f$' % (
'10 \mathrm{ Mb}-\infty' if resolution != 1 else '3', a3))
# label = r'$\alpha_3=%.2f$ (%d-$\infty$)' % (a3, x[v2+k]))
axe.set_ylabel('Log interaction count')
axe.set_xlabel('Log genomic distance (resolution: %s)' % nicer(resolution))
axe.legend(loc='lower left', frameon=False)
axe.set_xscale('log')
axe.set_yscale('log')
axe.set_xlim((min_diff, max_diff))
try:
axe.set_ylim((0, max(y)))
except ValueError:
pass
if savefig:
tadbit_savefig(savefig)
plt.close('all')
elif show==True:
plt.show()
plt.close('all')
def filter_by_mean(matrx, draw_hist=False, silent=False, bads=None, savefig=None):
"""
fits the distribution of Hi-C interaction count by column in the matrix to
a polynomial. Then searches for the first possible
"""
nbins = 100
if not bads:
bads = {}
# get sum of columns
cols = []
size = len(matrx)
for c in sorted([[matrx.get(i+j*size, 0) for j in xrange(size) if not j in bads]
for i in xrange(size) if not i in bads], key=sum):
cols.append(sum(c))
cols = np.array(cols)
if draw_hist:
plt.figure(figsize=(9, 9))
try:
percentile = np.percentile(cols, 5)
except IndexError:
warn('WARNING: no columns to filter out')
return bads
# mad = np.median([abs(median - c ) for c in cols])
best =(None, None, None, None)
# bin the sum of columns
xmin = min(cols)
xmax = max(cols)
y = np.linspace(xmin, xmax, nbins)
hist = np.digitize(cols, y)
x = [sum(hist == i) for i in range(1, nbins + 1)]
if draw_hist:
hist = plt.hist(cols, bins=100, alpha=.3, color='grey')
xp = range(0, int(cols[-1]))
# check if the binning is correct
# we want at list half of the bins with some data
try:
cnt = 0
while list(x).count(0) > len(x)/2:
cnt += 1
cols = cols[:-1]
xmin = min(cols)
xmax = max(cols)
y = np.linspace(xmin, xmax, nbins)
hist = np.digitize(cols, y)
x = [sum(hist == i) for i in range(1, nbins + 1)]
if draw_hist:
plt.clf()
hist = plt.hist(cols, bins=100, alpha=.3, color='grey')
xp = range(0, int(cols[-1]))
if cnt > 10000:
raise ValueError
# find best polynomial fit in a given range
for order in range(6, 18):
z = np.polyfit(y, x, order)
zp = np.polyder(z, m=1)
roots = np.roots(np.polyder(z))
# check that we are concave down, otherwise take next root
pente = np.polyval(zp, abs(roots[-2] - roots[-1]) / 2 + roots[-1])
if pente > 0:
root = roots[-1]
else:
root = roots[-2]
# root must be higher than zero
if root <= 0:
continue
# and lower than the median
if root >= percentile:
continue
p = np.poly1d(z)
R2 = get_r2(p, x, y)
# try to avoid very large orders by weigthing negatively their fit
if order > 13:
R2 -= float(order)/30
if best[0] < R2:
best = (R2, order, p, z, root)
try:
p, z, root = best[2:]
if draw_hist:
xlims = plt.xlim()
ylims = plt.ylim()
a = plt.plot(xp, p(xp), "--", color='k')
b = plt.vlines(root, 0, plt.ylim()[1], colors='r', linestyles='dashed')
# c = plt.vlines(median - mad * 1.5, 0, 110, colors='g',
# linestyles='dashed')
try:
plt.legend(a+[b], ['polyfit \n%s' % (
''.join([sub('e([-+][0-9]+)', 'e^{\\1}',
'$%s%.1fx^%s$' % ('+' if j>0 else '', j,
'{' + str(i) + '}'))
for i, j in enumerate(list(p)[::-1])])),
'first solution of polynomial derivation'],
fontsize='x-small')
except TypeError:
plt.legend(a+[b], ['polyfit \n%s' % (
''.join([sub('e([-+][0-9]+)', 'e^{\\1}',
'$%s%.1fx^%s$' % ('+' if j>0 else '', j,
'{' + str(i) + '}'))
for i, j in enumerate(list(p)[::-1])])),
'first solution of polynomial derivation'])
# plt.legend(a+[b]+[c], ['polyfit \n{}'.format (
# ''.join([sub('e([-+][0-9]+)', 'e^{\\1}',
# '${}{:.1}x^{}$'.format ('+' if j>0 else '', j,
# '{' + str(i) + '}'))
# for i, j in enumerate(list(p)[::-1])])),
# 'first solution of polynomial derivation',
# 'median - (1.5 * median absolute deviation)'],
# fontsize='x-small')
plt.ylim([0, ylims[1]])
plt.xlim(xlims)
plt.xlabel('Sum of interactions')
plt.xlabel('Number of columns with a given value')
if savefig:
tadbit_savefig(savefig)
else:
plt.show()
# label as bad the columns with sums lower than the root
for i, col in enumerate([[matrx.get(i+j*size, 0)
for j in xrange(size)]
for i in xrange(size)]):
if sum(col) < root:
bads[i] = sum(col)
# now stored in Experiment._zeros, used for getting more accurate z-scores
if bads and not silent:
stderr.write(('\nWARNING: removing columns having less than %s ' +
'counts:\n %s\n') % (
round(root, 3), ' '.join(
['%5s'%str(i + 1) + (''if (j + 1) % 20 else '\n')
for j, i in enumerate(sorted(bads.keys()))])))
except:
if not silent:
stderr.write('WARNING: Too many zeroes to filter columns.' +
' SKIPPING...\n')
if draw_hist:
plt.xlabel('Sum of interactions')
plt.xlabel('Number of columns with a given value')
if savefig:
tadbit_savefig(savefig)
else:
plt.show()
except ValueError:
if not silent:
stderr.write('WARNING: Too few data to filter columns based on ' +
'mean value.\n')
if draw_hist:
plt.close('all')
return bads
def plot_iterative_mapping(fnam1, fnam2, total_reads=None, axe=None, savefig=None):
"""
:param fnam: input file name
:param total_reads: total number of reads in the initial FASTQ file
:param None axe: a matplotlib.axes.Axes object to define the plot
appearance
:param None savefig: path to a file where to save the image generated;
if None, the image will be shown using matplotlib GUI (the extension
of the file name will determine the desired format).
:returns: a dictionary with the number of reads per mapped length
"""
count_by_len = {}
total_reads = total_reads or 1
if not axe:
fig=plt.figure()
_ = fig.add_subplot(111)
colors = ['olive', 'darkcyan']
iteration = False
for i, fnam in enumerate([fnam1, fnam2]):
fhandler = open(fnam)
line = fhandler.next()
count_by_len[i] = {}
while line.startswith('#'):
if line.startswith('# MAPPED '):
itr, num = line.split()[2:]
count_by_len[i][int(itr)] = int(num)
line = fhandler.next()
if not count_by_len[i]:
iteration = True
try:
while True:
_, length, _, _ = line.rsplit('\t', 3)
try:
count_by_len[i][int(length)] += 1
except KeyError:
count_by_len[i][int(length)] = 1
line = fhandler.next()
except StopIteration:
pass
fhandler.close()
lengths = sorted(count_by_len[i].keys())
for k in lengths[::-1]:
count_by_len[i][k] += sum([count_by_len[i][j]
for j in lengths if j < k])
plt.plot(lengths, [float(count_by_len[i][l]) / total_reads
for l in lengths],
label='read' + str(i + 1), linewidth=2, color=colors[i])
if iteration:
plt.xlabel('read length (bp)')
else:
plt.xlabel('Iteration number')
if total_reads != 1:
plt.ylabel('Proportion of mapped reads')
else:
plt.ylabel('Number of mapped reads')
plt.legend(loc=4)
if savefig:
tadbit_savefig(savefig)
elif not axe:
plt.show()
plt.close('all')
return count_by_len
def draw(self, focus=None, extras=None, ymax=None, ali_colors=('grey',),
normalized=True, savefig=None, shape='ellipse'):
"""
Draw alignments as a plot.
:param None focus: can pass a tuple (bin_start, bin_stop) to display the
alignment between these genomic bins
:param None extras: list of coordinates (genomic bin) where to draw a
red cross
:param None ymax: limit the y axis up to a given value
:param ('grey', ): successive colors for alignment
:param True normalized: normalized Hi-C count are plotted instead of raw
data.
:param 'ellipse' shape: which kind of shape to use as schematic
representation of TADs. Implemented: 'ellipse', 'rectangle',
'triangle'
:param None savefig: path to a file where to save the image generated;
if None, the image will be shown using matplotlib GUI (the extension
of the file name will determine the desired format).
"""
from matplotlib.cm import jet
from matplotlib import pyplot as plt
experiments = self.__experiments
maxres = max([e.resolution for e in experiments])
facts = [maxres / e.resolution for e in experiments]
siz = experiments[0].size
if focus:
figsiz = 4 + (focus[1] - focus[0]) / 30
else:
figsiz = 4 + siz / 30
fig, axes = plt.subplots(nrows=len(experiments),
sharex=True, sharey=True,
figsize=(figsiz, 1 + len(experiments) * 1.8))
fig.subplots_adjust(hspace=0)
maxys = []
for iex, xpr in enumerate(experiments):
if not xpr.name in self:
continue
_tad_density_plot(xpr, maxys=maxys, normalized=normalized,
fact_res=facts[iex], axe=axes[iex],
extras=extras, shape=shape, focus=focus)
# draw alignment columns
start = focus[0] if focus else 1
end = focus[1] if focus else xpr.tads[max(xpr.tads)]['end']
maxy = (ymax or max(maxys)) + 0.4
maxxs = []
for iex in range(len(experiments)):
starting = focus[0] if focus else 1
ending = (focus[1] if focus
else experiments[iex].tads.values()[-1]['end'])
axes[iex].hlines(1, 1, end, 'k', lw=1.5)
axes[iex].set_ylim((0, maxy))
maxxs.append(ending / facts[iex])
axes[iex].text(starting + 1, float(maxy) / 20,
experiments[iex].name, {'ha': 'left',
'va': 'bottom'})
axes[iex].set_yticks([float(i) / 2
for i in range(1, int(maxy + .5) * 2)])
if ymax:
axes[iex].set_ylim((0, ymax))
axes[iex].set_xlim(xmin=starting, xmax=max(maxxs))
pos = {'ha': 'center', 'va': 'bottom'}
for i, col in enumerate(self.itercolumns()):
ends = sorted([(t['end'], j) for j, t in enumerate(col) if t['end']])
beg = (ends[0][0] + 0.9) / facts[ends[0][1]]
end = (ends[-1][0] + 1.1) / facts[ends[-1][1]]
if focus:
if beg < focus[0] or end > focus[1]:
continue
axes[0].text(beg + float(end - beg) / 2, maxy + float(maxy) / 20,
str(i + 1), pos,
rotation=90, size='small')
for iex, tad in enumerate(col):
if not tad['end']:
continue
axes[iex].axvspan(beg-.2, end+.2, alpha=0.2,
color=ali_colors[i%(len(ali_colors))])
axes[iex].set_xlabel('Genomic bin')
tit1 = fig.suptitle("TAD borders' alignment", size='x-large')
tit2 = axes[0].set_title("Alignment column number")
tit2.set_y(1.3)
plt.subplots_adjust(top=0.76)
# This was for color bar instead of legend
# ax1 = fig.add_axes([0.9 + 0.3/figsiz, 0.05, 0.2/figsiz, 0.9])
# cb1 = colorbar.ColorbarBase(ax1, cmap=jet,
# norm=colors.Normalize(vmin=0., vmax=1.))
# cb1.set_label('Border prediction score')
# cb1.ax.set_yticklabels([str(i)for i in range(1, 11)])
fig.set_facecolor('white')
plots = []
for scr in xrange(1, 11):
plots += plt.plot((100,),(100,), marker=6, ms=9,
color=jet(float(scr) / 10), mec='none')
try:
axes[-1].legend(plots,
[str(scr) for scr in xrange(1, 11)],
numpoints=1, title='Border scores',
fontsize='small', loc='lower left',
bbox_to_anchor=(1, 0.5))
except TypeError:
axes[-1].legend(plots,
[str(scr) for scr in xrange(1, 11)],
numpoints=1, title='Border scores',
loc='lower left',
bbox_to_anchor=(1, 0.5))
if savefig:
tadbit_savefig(savefig)
else:
plt.show()
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