def plotModuleCorr(cyDf,
labels,
plotLabel,
sampleStr='M',
dropped=None,
compCommVar=None):
"""Make a corr plot for a module."""
modDf = makeModuleVariables(cyDf[labels.index],
labels,
dropped=dropped,
sampleStr=sampleStr)
modVar = '%s%s' % (sampleStr, plotLabel)
cyVars = labels2modules(labels, dropped=None)[plotLabel]
if not compCommVar is None:
cyVars.append(compCommVar)
tmpDf = cyDf[cyVars].join(modDf[modVar]).copy()
"""Rename dropped columns with an asterisk but leave them on the plot"""
if not dropped is None:
tmpDf.columns = np.array([
c + '*' if c in dropped and dropped[c] else c
for c in tmpDf.columns
])
figh = plt.gcf()
figh.clf()
combocorrplot(tmpDf, method='pearson')
axh = plt.gca()
axh.annotate('%s%s' % (sampleStr, plotLabel),
xy=(0.5, 0.99),
xycoords='figure fraction',
va='top',
ha='center')
def plot_module_correl(clust_object, folder):
"""Plot intra-module correlation"""
i = 0
for lab in list(
cy.labels2modules(clust_object.labels,
clust_object.dropped).keys()):
plt.figure(50 + i, figsize=(15, 9))
cyplot.plotModuleCorr(clust_object.cyDf,
clust_object.labels,
lab,
dropped=clust_object.dropped)
plt.figure(50 + i).savefig(os.path.join(
folder,
'%s_modules_correlations_%s.png' % (clust_object.name, lab)),
dpi=300)
i += 1
def plot_modules(clust_object, folder):
'''Plot cytomod object modules information'''
"""Hierarchical clustering heatmap"""
plt.figure(41, figsize=(15.5, 9.5))
# colInds = plotHColCluster(ds[s].cyDf, method='complete', metric='pearson-signed', col_labels=ds[s].labels, col_dmat=ds[s].dmatDf)
colInds = plotHColCluster(clust_object.cyDf,
method='complete',
metric='pearson-signed',
col_labels=clust_object.labels,
save_path=os.path.join(
folder, '%s_hierchical_clust_heatmap.png' %
clust_object.name))
# plt.figure(41).savefig(os.path.join(folder, '%s_hierchical_clust_heatmap.png' % clust_object.name))
"""Heatmaps of pairwise reliability"""
plt.figure(43, figsize=(15.5, 9.5))
colInds = cyplot.plotHierClust(1 - clust_object.pwrel,
clust_object.Z,
labels=clust_object.labels,
titleStr='Pairwise reliability (%s)' %
clust_object.name.replace('_', ' '),
vRange=(0, 1))
plt.figure(43).savefig(os.path.join(folder,
'%s_pwrel.png' % clust_object.name),
dpi=300)
"""color_label_legend"""
plt.figure(48, figsize=(3, 3))
plt.clf()
axh = plt.subplot(1, 1, 1)
colorLegend(palettable.colorbrewer.qualitative.Set3_6.mpl_colors,
['%s' % s for s in clust_object.modDf.columns],
loc=10)
axh.spines['right'].set_color('none')
axh.spines['left'].set_color('none')
axh.spines['top'].set_color('none')
axh.spines['bottom'].set_color('none')
axh.set_xticks([])
axh.set_yticks([])
axh.set_facecolor('white')
plt.figure(48).savefig(os.path.join(folder, 'color_label_legend.png'),
dpi=300)
"""Plot intra-module correlation"""
plt.figure(50, figsize=(15, 9))
for lab in list(
cy.labels2modules(clust_object.labels,
clust_object.dropped).keys()):
cyplot.plotModuleCorr(clust_object.cyDf,
clust_object.labels,
lab,
dropped=clust_object.dropped)
plt.figure(50).savefig(os.path.join(
folder, '%s_mod_corr_%s.png' % (clust_object.name, lab)),
dpi=300)
"""Cytokine embedding"""
plt.figure(901, figsize=(13, 9.7))
cyplot.plotModuleEmbedding(clust_object.dmatDf,
clust_object.labels,
method='kpca')
colors = palettable.colorbrewer.get_map(
'Set1', 'qualitative', len(np.unique(clust_object.labels))).mpl_colors
colorLegend(colors, [
'%s%1.0f' % (clust_object.sampleStr, i)
for i in np.unique(clust_object.labels)
],
loc='lower left')
plt.figure(901).savefig(os.path.join(folder,
'%sembed.png' % clust_object.name),
dpi=300)