def _boxplot(self, data_list, names, colors, id_, neg_ctrl):
fig = pyplot.figure(figsize=(len(data_list)/3+5, 20))
ax1 = fig.add_subplot(111)
ax1.set_title('IBB Analysis %s' % self.PLOT_LABELS[id_])
bp = pyplot.boxplot(data_list, patch_artist=True)
pyplot.setp(bp['boxes'], color='black')
pyplot.setp(bp['whiskers'], color='black')
pyplot.setp(bp['fliers'], markerfacecolor='w', marker='o', markeredgecolor='k')
i = 0
for c, d in zip(colors, data_list):
if len(d) > 0:
b = bp['boxes'][i]
pyplot.setp(b, facecolor=rgb2hex(c, mpl=False))
i += 1
#ax1.set_xticks([x+0.8 for x in range(len(data_list))])
if len(neg_ctrl) > 0:
neg_line = numpy.concatenate(neg_ctrl).mean()
ax1.plot([0, len(data_list)],[neg_line, neg_line], 'k--', label="Neg. Ctrl.")
xtickNames = pyplot.setp(ax1, xticklabels=names)
pyplot.setp(xtickNames, rotation=45, horizontalalignment='right', fontsize=10)
data_max = numpy.nanmax(numpy.array(map(numpy.mean, numpy.array(data_list))))
if data_max < 0.5 * self.timeing_ylim_range[1]:
pyplot.ylim(self.timeing_ylim_range[0], self.timeing_ylim_range[1]/2.0)
if data_max < 0.25 * self.timeing_ylim_range[1]:
pyplot.ylim(self.timeing_ylim_range[0], self.timeing_ylim_range[1]/4.0)
if data_max < 0.125 * self.timeing_ylim_range[1]:
pyplot.ylim(self.timeing_ylim_range[0], self.timeing_ylim_range[1]/8.0)
fig.savefig(os.path.join(self.path_out, '_timing_ibb_events_%s_box.pdf' % id_), format='pdf')
def _plot_valid_bars(self, result):
data = []
names = []
bar_labels = ('valid', 'signal', 'split', 'ibb_onset', 'nebd_onset',
'prophase_onset')
bar_colors = map(lambda x: rgb2hex(x, mpl=False), [
[int(x * 255) for x in pyplot.cm.Greens(2)[0:3]],
] + [
tuple(x) for x in (pyplot.cm.RdBu(range(0, 256, 62)) *
255)[:, :3].astype('uint8')[:, :3]
])
def mycmp(x, y):
return cmp(
result[x]['positions'][0].__getattribute__(
self.color_sort_by).lower(),
result[y]['positions'][0].__getattribute__(
self.color_sort_by).lower())
for group_name in sorted(result, cmp=mycmp):
data.append(result[group_name]['valid'])
names.append(group_name)
self._plot_multi_bars(data,
bar_labels,
bar_colors,
names,
show_number=False)
def _barplot(self, data_list, names, colors, id_, neg_ctrl):
fig = pyplot.figure(figsize=(len(data_list)/3+5, 20))
ax1 = fig.add_subplot(111)
ax1.set_title('IBB Analysis %s' % self.PLOT_LABELS[id_])
ax1.bar(range(len(data_list)), map(numpy.mean, numpy.array(data_list)),
yerr=map(numpy.std, numpy.array(data_list)),
color=map(lambda x:rgb2hex(x, mpl=False), colors),
ecolor='k',
align='center',
)
#ax1.set_xticks([x+0.8 for x in range(len(data_list))])
if len(neg_ctrl) > 0:
neg_line = numpy.concatenate(neg_ctrl).mean()
ax1.plot([0, len(data_list)],[neg_line, neg_line], 'k--', label="Neg. Ctrl.")
#xtickNames = pyplot.setp(ax1, xticklabels=names)
#pyplot.setp(xtickNames, rotation=45, horizontalalignment='right', fontsize=10)
ax1.set_xticks(range(len(data_list)))
xticks = ax1.set_xticklabels(names)
pyplot.setp(xticks, rotation=45, horizontalalignment='right')
pyplot.ylim(*self.timeing_ylim_range)
data_max = numpy.nanmax(numpy.array(map(numpy.mean, numpy.array(data_list))))
if data_max < 0.5 * self.timeing_ylim_range[1]:
pyplot.ylim(self.timeing_ylim_range[0], self.timeing_ylim_range[1]/2.0)
if data_max < 0.25 * self.timeing_ylim_range[1]:
pyplot.ylim(self.timeing_ylim_range[0], self.timeing_ylim_range[1]/4.0)
if data_max < 0.125 * self.timeing_ylim_range[1]:
pyplot.ylim(self.timeing_ylim_range[0], self.timeing_ylim_range[1]/8.0)
fig.savefig(os.path.join(self.path_out, '_timing_ibb_events_%s_bar.pdf' % id_), format='pdf')
def binary_classification(self, data):
"""Perform an initial binary classifcation to distinguish between
mitotic and non-mitotic objects/cells.
1) Perform kmeans clustering (at least 5 initialisation,
emperically determined)
2) and assing mitotic labels (0 means "non-mitotic", 1 "mitotic")
assuming is that non-mitotic cell are the larger population.
"""
km = KMeans(n_clusters=2, n_init=5)
labels = km.fit_predict(data)
# assign labels, 0 for non-mitotic, 1 for mitotic
# from now on labels a biologial meaning
binary_class_names = {0: "non-mitotic", 1: "mitotic"}
if labels[labels==1].size > labels[labels==0].size:
labels = np.where(labels, 0, 1)
for i, node in enumerate(self.graph.node_list()):
obj = self.graph.node_data(node)
obj.iLabel = labels[i]
obj.strClassName = binary_class_names[obj.iLabel]
obj.strHexColor = rgb2hex(BINARY_CMAP(obj.iLabel), mpl=True)
obj.dctProb.update({0:np.nan, 1:np.nan})
def binary_classification(self, data):
"""Perform an initial binary classifcation to distinguish between
mitotic and non-mitotic objects/cells.
1) Perform kmeans clustering (at least 5 initialisation,
emperically determined)
2) and assing mitotic labels (0 means "non-mitotic", 1 "mitotic")
assuming is that non-mitotic cell are the larger population.
"""
km = KMeans(n_clusters=2, n_init=5)
labels = km.fit_predict(data)
# assign labels, 0 for non-mitotic, 1 for mitotic
# from now on labels a biologial meaning
binary_class_names = {0: "non-mitotic", 1: "mitotic"}
if labels[labels == 1].size > labels[labels == 0].size:
labels = np.where(labels, 0, 1)
for i, node in enumerate(self.graph.node_list()):
obj = self.graph.node_data(node)
obj.iLabel = labels[i]
obj.strClassName = binary_class_names[obj.iLabel]
obj.strHexColor = rgb2hex(BINARY_CMAP(obj.iLabel), mpl=True)
obj.dctProb.update({0: np.nan, 1: np.nan})
def _save_class_labels(self, labels, nodes, probabilities, prefix="unsupervised"):
# clear labels from binary classification
for node in self.graph.node_list():
obj = self.graph.node_data(node)
obj.iLabel = None
obj.strClassName = None
obj.strHexColor = None
obj.dctProb.clear()
for node, label, probs in zip(nodes, labels.flatten(), probabilities):
obj = self.graph.node_data(node)
obj.iLabel = label
obj.strClassName = "%s-%d" % (prefix, label)
obj.dctProb = dict((i, v) for i, v in enumerate(probs))
rgb = self.classdef.colormap(label)
obj.strHexColor = rgb2hex(rgb)
def _boxplot(self, data_list, names, colors, id_, neg_ctrl):
fig = pyplot.figure(figsize=(len(data_list) / 3 + 5, 20))
ax1 = fig.add_subplot(111)
ax1.set_title('IBB Analysis %s' % self.PLOT_LABELS[id_])
bp = pyplot.boxplot(data_list, patch_artist=True)
pyplot.setp(bp['boxes'], color='black')
pyplot.setp(bp['whiskers'], color='black')
pyplot.setp(bp['fliers'],
markerfacecolor='w',
marker='o',
markeredgecolor='k')
i = 0
for c, d in zip(colors, data_list):
if len(d) > 0:
b = bp['boxes'][i]
pyplot.setp(b, facecolor=rgb2hex(c, mpl=False))
i += 1
#ax1.set_xticks([x+0.8 for x in range(len(data_list))])
if len(neg_ctrl) > 0:
neg_line = numpy.concatenate(neg_ctrl).mean()
ax1.plot([0, len(data_list)], [neg_line, neg_line],
'k--',
label="Neg. Ctrl.")
xtickNames = pyplot.setp(ax1, xticklabels=names)
pyplot.setp(xtickNames,
rotation=45,
horizontalalignment='right',
fontsize=10)
data_max = numpy.nanmax(
numpy.array(map(numpy.mean, numpy.array(data_list))))
if data_max < 0.5 * self.timeing_ylim_range[1]:
pyplot.ylim(self.timeing_ylim_range[0],
self.timeing_ylim_range[1] / 2.0)
if data_max < 0.25 * self.timeing_ylim_range[1]:
pyplot.ylim(self.timeing_ylim_range[0],
self.timeing_ylim_range[1] / 4.0)
if data_max < 0.125 * self.timeing_ylim_range[1]:
pyplot.ylim(self.timeing_ylim_range[0],
self.timeing_ylim_range[1] / 8.0)
fig.savefig(os.path.join(self.path_out,
'_timing_ibb_events_%s_box.pdf' % id_),
format='pdf')
def _plot_valid_bars(self, result):
data = []
names = []
bar_labels = ('valid', 'signal', 'split', 'ibb_onset', 'nebd_onset', 'prophase_onset')
bar_colors = map(lambda x:rgb2hex(x, mpl=False), [[int(x*255) for x in pyplot.cm.Greens(2)[0:3]],] +
[tuple(x) for x in (pyplot.cm.RdBu(range(0,256,62))*255)[:,:3].astype('uint8')[:,:3]]
)
def mycmp(x, y):
return cmp(result[x]['positions'][0].__getattribute__(self.color_sort_by).lower(),
result[y]['positions'][0].__getattribute__(self.color_sort_by).lower())
for group_name in sorted(result, cmp=mycmp):
data.append(result[group_name]['valid'])
names.append(group_name)
self._plot_multi_bars(data, bar_labels, bar_colors, names, show_number=False)
def _barplot(self, data_list, names, colors, id_, neg_ctrl):
fig = pyplot.figure(figsize=(len(data_list) / 3 + 5, 20))
ax1 = fig.add_subplot(111)
ax1.set_title('IBB Analysis %s' % self.PLOT_LABELS[id_])
ax1.bar(
range(len(data_list)),
map(numpy.mean, numpy.array(data_list)),
yerr=map(numpy.std, numpy.array(data_list)),
color=map(lambda x: rgb2hex(x, mpl=False), colors),
ecolor='k',
align='center',
)
#ax1.set_xticks([x+0.8 for x in range(len(data_list))])
if len(neg_ctrl) > 0:
neg_line = numpy.concatenate(neg_ctrl).mean()
ax1.plot([0, len(data_list)], [neg_line, neg_line],
'k--',
label="Neg. Ctrl.")
#xtickNames = pyplot.setp(ax1, xticklabels=names)
#pyplot.setp(xtickNames, rotation=45, horizontalalignment='right', fontsize=10)
ax1.set_xticks(range(len(data_list)))
xticks = ax1.set_xticklabels(names)
pyplot.setp(xticks, rotation=45, horizontalalignment='right')
pyplot.ylim(*self.timeing_ylim_range)
data_max = numpy.nanmax(
numpy.array(map(numpy.mean, numpy.array(data_list))))
if data_max < 0.5 * self.timeing_ylim_range[1]:
pyplot.ylim(self.timeing_ylim_range[0],
self.timeing_ylim_range[1] / 2.0)
if data_max < 0.25 * self.timeing_ylim_range[1]:
pyplot.ylim(self.timeing_ylim_range[0],
self.timeing_ylim_range[1] / 4.0)
if data_max < 0.125 * self.timeing_ylim_range[1]:
pyplot.ylim(self.timeing_ylim_range[0],
self.timeing_ylim_range[1] / 8.0)
fig.savefig(os.path.join(self.path_out,
'_timing_ibb_events_%s_bar.pdf' % id_),
format='pdf')
def _save_class_labels(self,
labels,
nodes,
probabilities,
prefix='unsupervised'):
# clear labels from binary classification
for node in self.graph.node_list():
obj = self.graph.node_data(node)
obj.iLabel = None
obj.strClassName = None
obj.strHexColor = None
obj.dctProb.clear()
for node, label, probs in zip(nodes, labels.flatten(), probabilities):
obj = self.graph.node_data(node)
obj.iLabel = label
obj.strClassName = "%s-%d" % (prefix, label)
obj.dctProb = dict((i, v) for i, v in enumerate(probs))
rgb = self.classdef.colormap(label)
obj.strHexColor = rgb2hex(rgb)
def qcolor_to_hex(qcolor):
return rgb2hex((qcolor.red(), qcolor.green(), qcolor.blue()), mpl=False)
def qcolor_to_hex(qcolor):
return rgb2hex((qcolor.red(), qcolor.green(), qcolor.blue()), mpl=False)
