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)
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('Module %s%s' % (sampleStr, plotLabel),
xy=(0.5, 0.99),
xycoords='figure fraction',
va='top',
ha='center')
def plotInterModuleCorr(cyDf, labels, dropped=None, compCommVar=None):
"""Make a plot showing inter-module correlation"""
modDf = makeModuleVariables(cyDf[labels.index], labels, dropped=dropped)
modVars = modDf.columns.tolist()
if not compCommVar is None:
modDf = modDf.join(cyDf[compCommVar])
modVars += [compCommVar]
figh = plt.gcf()
figh.clf()
combocorrplot(modDf[modVars], method='pearson')
def plotInterModuleCorr(cyDf, labels, dropped = None, compCommVar = None):
"""Make a plot showing inter-module correlation"""
modDf = makeModuleVariables(cyDf[labels.index], labels, dropped = dropped)
modVars = modDf.columns.tolist()
if not compCommVar is None:
modDf = modDf.join(cyDf[compCommVar])
modVars += [compCommVar]
figh = plt.gcf()
figh.clf()
combocorrplot(modDf[modVars], method = 'pearson')
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)
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('Module %s%s' % (sampleStr, plotLabel), xy=(0.5,0.99), xycoords='figure fraction', va = 'top', ha='center')
