def tad_density_plot(self, name, axe=None, focus=None, extras=None,
normalized=True, savefig=None, shape='ellipse'):
"""
Draw an summary of the TAD found in a given experiment and their density
in terms of relative Hi-C interaction count.
:param name: name of the experiment to visualize
: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).
"""
if not self.experiments[name].tads:
raise Exception("TAD borders not found\n")
_tad_density_plot(self.experiments[name], axe=axe, focus=focus,
extras=extras, normalized=normalized,
savefig=savefig, shape=shape)
def tad_density_plot(self, name, axe=None, focus=None, extras=None,
normalized=True, savefig=None, shape='ellipse'):
"""
Draw an summary of the TAD found in a given experiment and their density
in terms of relative Hi-C interaction count.
:param name: name of the experiment to visualize
: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).
"""
if not self.experiments[name].tads:
raise Exception("TAD borders not found\n")
_tad_density_plot(self.experiments[name], axe=axe, focus=focus,
extras=extras, normalized=normalized,
savefig=savefig, shape=shape)
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(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()
