def plotClusterOffrates(self, cluster=None, variant=None, idx=None):
"""Plot an off rate curve of a particular cluster."""
times = self.x
if cluster is not None:
fluorescence = self.binding_series.loc[cluster]
else:
if variant is None:
print 'Error: need to define either variant or cluster!'
return
subSeries = self.getVariantBindingSeries(variant)
if idx is not None:
fluorescence = subSeries.iloc[idx]
else:
fluorescence = subSeries.iloc[0]
cluster = fluorescence.name
cluster_table = self.cluster_table
if cluster in cluster_table.index:
results = cluster_table.loc[cluster]
else:
results = pd.Series(index=cluster_table.columns)
fig = plt.figure(figsize=(4,3))
ax = fig.add_subplot(111)
fitParameters = pd.DataFrame(columns=['fmax', 'koff', 'fmin'])
plotting.plotFitCurve(times, fluorescence, results, fitParameters, ax=ax,
log_axis=False, func=fitting.objectiveFunctionOffRates, fittype='off')
def plotBindingCurve(self, variant, annotate=True):
"""Plot a binding curve of a particular variant."""
subSeries = self.getVariantBindingSeries(variant)
if len(subSeries)==0:
print 'No fluorescence data associated with variant %s'%str(variant)
return
concentrations = self.x
variant_table = self.variant_table
# plot
fig = plt.figure(figsize=(3,3))
ax = fig.add_subplot(111)
plotting.plotFitCurve(concentrations,
subSeries,
variant_table.loc[variant],
ax=ax)
if annotate:
names = ['dG', 'dG_lb', 'dG_ub', 'fmax', 'fmax_lb', 'fmax_ub', 'numTests', 'pvalue', 'rsq', 'flag']
vec = pd.Series([getValueInTable(variant_table.loc[variant], name) for name in names], index=names)
#vec.fillna('', inplace=True)
annotationText = ['dG= %4.2f (%4.2f, %4.2f)'%(vec.dG,
vec.dG_lb,
vec.dG_ub),
'fmax= %4.2f (%4.2f, %4.2f)'%(vec.fmax,
vec.fmax_lb,
vec.fmax_ub),
'Nclusters= %4.0f'%vec.numTests,
'pvalue= %.1e'%vec.pvalue,
'average Rsq= %4.2f'%vec.rsq,
'fmax enforced=%s'%str(vec.flag)
]
ax.annotate('\n'.join(annotationText), xy=(0.05, .95), xycoords='axes fraction',
horizontalalignment='left', verticalalignment='top', fontsize=9)
def plotBindingCurvesAcrossTargets(affinityDataList,
variant,
colors=None,
names=None,
norm=False,
ax=None):
'''Plot a binding curve of a particular variant'''
if colors is None:
colors = sns.color_palette("Paired", 12)
if ax is None:
fig = plt.figure(figsize=(4, 4))
ax = fig.add_subplot(111)
for i, affinityData in enumerate(affinityDataList):
# Load data
subSeries = affinityData.getVariantBindingSeries(variant)
if len(subSeries) == 0:
print 'No fluorescence data associated with variant %s' % str(
variant)
return
concentrations = affinityData.x
variant_table = affinityData.variant_table
variant_data = variant_table.loc[variant]
if norm:
fmax = variant_data['fmax']
subSeries = subSeries.copy() / fmax
variant_data = variant_data.copy()
for field in [
'fmax_init', 'fmin_init', 'fmax_lb', 'fmax', 'fmax_ub',
'fmin_lb', 'fmin', 'fmin_ub'
]:
variant_data[field] = variant_data[field] / fmax
# Plot
plotting.plotFitCurve(concentrations, subSeries, variant_data, ax=ax)
ax.lines[i * 4 + 3].set_color(colors[i])
ax.lines[i * 4 + 3].set_linewidth(2)
if names is not None:
ax.lines[i * 4 + 3].set_label(names[i])
if norm:
ylabel = 'Normalized fluorescence'
else:
ylabel = 'Fluorescence'
plotlib.setproperties(xlabel='Concentration (nM)',
ylabel=ylabel,
labelfontsize=16,
tickfontsize=16,
legend=names,
legendloc=2,
legendfontsize=12)
return ax
def plotOffrateCurve(self, variant, annotate=False, numtiles=None, tiles=None):
"""Plot an off rate curve of a particular variant."""
subSeries = self.getVariantBindingSeries(variant).dropna(how='all')
timeDict = self.x
variant_table = self.variant_table
fitParameters = pd.DataFrame(columns=['fmax', 'koff', 'fmin'])
if len(subSeries)==0:
print 'No fluorescence data associated with variant %s'%str(variant)
return
# find tiles with variant cluster
clusterTiles = self.getVariantTiles(variant)
if tiles is None:
tileNum = clusterTiles.value_counts()
tileInit = tileNum.idxmax() # start by plotting the tile with the most clusters
if numtiles is None: # plot as many tiles as there are clusters if num tiles is not specified
numtiles = len(tileNum)
otherTiles = tileNum.iloc[1:numtiles].index.tolist()
else:
# if tile is specified, just plot that tile.
tileInit, otherTiles = tiles[0], tiles[1:]
if np.in1d(clusterTiles, np.hstack([tileInit, otherTiles])).sum()==0:
print 'No fluorescence data associated with variant %s'%str(variant)
return
# initiae plot
fig = plt.figure(figsize=(3.5,3))
ax = fig.add_subplot(111)
# plot fit and one tile's data
plotting.plotFitCurve(timeDict[tileInit], subSeries.loc[clusterTiles==tileInit], variant_table.loc[variant], fitParameters, ax=ax, capsize=0,
log_axis=False, func=fitting.objectiveFunctionOffRates, fittype='off')
# plot the other tile's data
for tile in otherTiles:
plotting.plotDataErrorbars(timeDict[tile], subSeries.loc[clusterTiles==tile], ax, capsize=0)
# annotate
if annotate:
names = ['koff', 'koff_lb', 'koff_ub', 'fmax', 'fmax_lb', 'fmax_ub', 'numTests', 'pvalue', 'rsq']
vec = pd.Series([getValueInTable(variant_table.loc[variant], name) for name in names], index=names)
#vec.fillna('', inplace=True)
annotationText = ['koff= %4.2e (%4.2e, %4.2e)'%(vec.koff,
vec.koff_lb,
vec.koff_ub),
'fmax= %4.2f (%4.2f, %4.2f)'%(vec.fmax,
vec.fmax_lb,
vec.fmax_ub),
'Nclusters= %4.0f'%vec.numTests,
'pvalue= %.1e'%vec.pvalue,
'average Rsq= %4.2f'%vec.rsq,
]
ax.annotate('\n'.join(annotationText), xy=(.05, .95), xycoords='axes fraction',
horizontalalignment='left', verticalalignment='top', fontsize=9)
return ax
def plotBindingCurve(affinityData, variant, annotate=True, ax=None):
'''Plot a binding curve of a particular variant'''
# Load data
subSeries = affinityData.getVariantBindingSeries(variant)
if len(subSeries) == 0:
print 'No fluorescence data associated with variant %s' % str(variant)
return
concentrations = affinityData.x
variant_table = affinityData.variant_table
# Plot
if ax is None:
fig = plt.figure(figsize=(4, 4))
ax = fig.add_subplot(111)
plotting.plotFitCurve(concentrations,
subSeries,
variant_table.loc[variant],
ax=ax)
plotlib.setproperties(xlabel='Concentration (nM)',
ylabel='Fluorescence',
labelfontsize=16,
tickfontsize=16)
# Annonate
if annotate:
names = [
'dG', 'dG_lb', 'dG_ub', 'fmax', 'fmax_lb', 'fmax_ub', 'numTests',
'pvalue', 'rsq', 'flag'
]
vec = pd.Series([
processresults.getValueInTable(variant_table.loc[variant], name)
for name in names
],
index=names)
#vec.fillna('', inplace=True)
annotationText = [
'Kd = {:4.2f} uM ({:4.2f}, {:4.2f})'.format(
liblib.dGtoKd(vec.dG, 'uM'), liblib.dGtoKd(vec.dG_lb, 'uM'),
liblib.dGtoKd(vec.dG_ub, 'uM')),
'fmax = {:4.2f} ({:4.2f}, {:4.2f})'.format(vec.fmax, vec.fmax_lb,
vec.fmax_ub),
'Nclusters = {:4.0f}'.format(vec.numTests),
'pvalue = {:.1e}'.format(vec.pvalue),
'average Rsq = {:4.2f}'.format(vec.rsq),
'fmax enforced = {}'.format(vec.flag)
]
ax.annotate('\n'.join(annotationText),
xy=(0.05, .95),
xycoords='axes fraction',
horizontalalignment='left',
verticalalignment='top',
fontsize=11)
return ax
def plotBindingCurvesAcrossTargets(affinityDataList, variant,
colors=None, names=None, norm=False, ax=None):
'''Plot a binding curve of a particular variant'''
if colors is None:
colors = sns.color_palette("Paired", 12)
if ax is None:
fig = plt.figure(figsize=(4, 4))
ax = fig.add_subplot(111)
for i, affinityData in enumerate(affinityDataList):
# Load data
subSeries = affinityData.getVariantBindingSeries(variant)
if len(subSeries)==0:
print 'No fluorescence data associated with variant %s'%str(variant)
return
concentrations = affinityData.x
variant_table = affinityData.variant_table
variant_data = variant_table.loc[variant]
if norm:
fmax = variant_data['fmax']
subSeries = subSeries.copy() / fmax
variant_data = variant_data.copy()
for field in ['fmax_init', 'fmin_init',
'fmax_lb', 'fmax', 'fmax_ub',
'fmin_lb', 'fmin', 'fmin_ub']:
variant_data[field] = variant_data[field] / fmax
# Plot
plotting.plotFitCurve(concentrations, subSeries, variant_data, ax=ax)
ax.lines[i*4+3].set_color(colors[i])
ax.lines[i*4+3].set_linewidth(2)
if names is not None:
ax.lines[i*4+3].set_label(names[i])
if norm:
ylabel = 'Normalized fluorescence'
else:
ylabel = 'Fluorescence'
plotlib.setproperties(xlabel='Concentration (nM)', ylabel=ylabel,
labelfontsize=16, tickfontsize=16,
legend=names, legendloc=2, legendfontsize=12)
return ax
def plotBindingCurve(affinityData, variant, annotate=True, ax=None):
'''Plot a binding curve of a particular variant'''
# Load data
subSeries = affinityData.getVariantBindingSeries(variant)
if len(subSeries)==0:
print 'No fluorescence data associated with variant %s'%str(variant)
return
concentrations = affinityData.x
variant_table = affinityData.variant_table
# Plot
if ax is None:
fig = plt.figure(figsize=(4, 4))
ax = fig.add_subplot(111)
plotting.plotFitCurve(concentrations, subSeries, variant_table.loc[variant], ax=ax)
plotlib.setproperties(xlabel='Concentration (nM)', ylabel='Fluorescence',
labelfontsize=16, tickfontsize=16)
# Annonate
if annotate:
names = ['dG', 'dG_lb', 'dG_ub', 'fmax', 'fmax_lb', 'fmax_ub', 'numTests', 'pvalue', 'rsq', 'flag']
vec = pd.Series([processresults.getValueInTable(variant_table.loc[variant], name) for name in names], index=names)
#vec.fillna('', inplace=True)
annotationText = ['Kd = {:4.2f} uM ({:4.2f}, {:4.2f})'.format(liblib.dGtoKd(vec.dG, 'uM'),
liblib.dGtoKd(vec.dG_lb, 'uM'),
liblib.dGtoKd(vec.dG_ub, 'uM')),
'fmax = {:4.2f} ({:4.2f}, {:4.2f})'.format(vec.fmax,
vec.fmax_lb,
vec.fmax_ub),
'Nclusters = {:4.0f}'.format(vec.numTests),
'pvalue = {:.1e}'.format(vec.pvalue),
'average Rsq = {:4.2f}'.format(vec.rsq),
'fmax enforced = {}'.format(vec.flag)
]
ax.annotate('\n'.join(annotationText), xy=(0.05, .95), xycoords='axes fraction',
horizontalalignment='left', verticalalignment='top', fontsize=11)
return ax
def plotClusterBinding(self, variant, cluster=None, idx=None):
"""Plot a binding curve of a particular cluster."""
concentrations = self.x
if cluster is not None:
fluorescence = subSeries.loc[cluster]
else:
subSeries = self.getVariantBindingSeries(variant)
if idx is not None:
fluorescence = subSeries.iloc[idx]
else:
fluorescence = subSeries.iloc[0]
cluster = fluorescence.name
cluster_table = self.cluster_table
# plot
fig = plt.figure(figsize=(4,3))
ax = fig.add_subplot(111)
plotting.plotFitCurve(concentrations,
fluorescence,
cluster_table.loc[cluster],
ax=ax)