def _plot_gene_inner(g,
region_series_fits,
change_distribution_bin_centers=None):
fig = plt.figure()
nRows, nCols = rect_subplot(len(region_series_fits))
for iRegion, (r, series, fit) in enumerate(region_series_fits):
ax = fig.add_subplot(nRows, nCols, iRegion + 1)
if change_distribution_bin_centers is None or not hasattr(
fit, 'change_distribution_weights'):
change_distribution = None
else:
change_distribution = Bunch(
centers=change_distribution_bin_centers,
weights=fit.change_distribution_weights,
)
plot_one_series(series,
fit.fitter.shape,
fit.theta,
change_distribution=change_distribution,
minimal_annotations=True,
ax=ax)
ax.set_title('Region {}'.format(r))
if iRegion % nCols == 0:
ax.set_ylabel('expression level')
if cfg.exon_level:
plt.subplots_adjust(hspace=0.8)
gene, start, end = g.split(cfg.exon_separator)
ttl = '{}({}-{})'.format(gene, start, end)
else:
fig.tight_layout(h_pad=0, w_pad=0)
ttl = 'Gene {}'.format(g)
fig.suptitle(ttl, fontsize=14)
return fig
def _plot_exons_inner(gene,
region,
exon_series_fits,
plot_ind=1,
total_plots=1):
fig = plt.figure()
nRows, nCols = rect_subplot(len(exon_series_fits))
nRows, nCols = min(nRows, nCols), max(nRows, nCols)
if cfg.exons_same_scale:
expression_max = max(
[max(exon[1].single_expression) for exon in exon_series_fits])
expression_min = min(
[min(exon[1].single_expression) for exon in exon_series_fits])
expression_range = np.array([expression_min, expression_max + np.e])
else:
expression_range = None
for iExon, (exon, series, fit) in enumerate(exon_series_fits):
ax = fig.add_subplot(nRows, nCols, iExon + 1)
plot_one_exon(series,
fit.fitter.shape,
fit.theta,
LOO_predictions=fit.LOO_predictions,
ax=ax,
y_range=expression_range)
ax.set_title(exon.replace(cfg.exon_separator, '-'))
if iExon % nCols == 0:
ax.set_ylabel('log expression level' if cfg.plots_scaling in
['log+1', 'log'] else 'expression level')
plt.subplots_adjust(hspace=0.4)
plot_ind = '({}\\{})'.format(plot_ind, total_plots)
fig.suptitle('Gene: {}, Region: {} {}'.format(gene, region, plot_ind),
fontsize=20,
fontweight='bold')
return fig
def plot_series(series, shape=None, theta=None, LOO_predictions=None):
if series.num_genes == 1:
return plot_one_series(series, shape, theta, LOO_predictions)
fig = plt.figure()
nRows, nCols = rect_subplot(series.num_genes)
for iGene,g in enumerate(series.gene_names):
ax = fig.add_subplot(nRows,nCols,iGene+1)
theta_i = theta[iGene] if theta is not None else None
LOO_i = LOO_predictions[:,iGene] if LOO_predictions is not None else None
plot_one_series(series.get_single_gene_series(iGene), shape, theta_i, LOO_i, ax=ax)
ax.set_title('Gene {}'.format(g), fontsize=cfg.fontsize)
fig.tight_layout(h_pad=0,w_pad=0)
fig.suptitle('Region {}'.format(series.region_name), fontsize=cfg.fontsize)
return fig
def _plot_exons_from_series_inner(gene, region, img_files, plot_ind,
total_plots):
fig = plt.figure() #figsize = (20,9))
nRows, nCols = rect_subplot(len(img_files))
nRows, nCols = min(nRows, nCols), max(nRows, nCols)
for n, fname in enumerate(img_files):
image = Image.open(fname)
ax = fig.add_subplot(nRows, nCols, n + 1)
ax.axis('off')
ax.imshow(image)
plot_ind = '({}\\{})'.format(plot_ind, total_plots)
fig.suptitle('Gene: {}, Region: {} {}'.format(gene, region, plot_ind),
fontsize=20,
fontweight='bold')
return fig
def plot_series(series, shape=None, theta=None, LOO_predictions=None):
if series.num_genes == 1:
return plot_one_series(series, shape, theta, LOO_predictions)
fig = plt.figure()
nRows, nCols = rect_subplot(series.num_genes)
for iGene, g in enumerate(series.gene_names):
ax = fig.add_subplot(nRows, nCols, iGene + 1)
theta_i = theta[iGene] if theta is not None else None
LOO_i = LOO_predictions[:,
iGene] if LOO_predictions is not None else None
plot_one_series(series.get_single_gene_series(iGene),
shape,
theta_i,
LOO_i,
ax=ax)
ax.set_title('Gene {}'.format(g), fontsize=cfg.fontsize)
fig.tight_layout(h_pad=0, w_pad=0)
fig.suptitle('Region {}'.format(series.region_name), fontsize=cfg.fontsize)
return fig
def _plot_gene_inner(g, region_series_fits, change_distribution_bin_centers=None):
fig = plt.figure()
nRows, nCols = rect_subplot(len(region_series_fits))
for iRegion,(r,series,fit) in enumerate(region_series_fits):
ax = fig.add_subplot(nRows,nCols,iRegion+1)
if change_distribution_bin_centers is None or not hasattr(fit, 'change_distribution_weights'):
change_distribution = None
else:
change_distribution = Bunch(
centers = change_distribution_bin_centers,
weights = fit.change_distribution_weights,
)
plot_one_series(series, fit.fitter.shape, fit.theta, change_distribution=change_distribution, minimal_annotations=True, ax=ax)
ax.set_title('Region {}'.format(r))
if iRegion % nCols == 0:
ax.set_ylabel('expression level')
fig.tight_layout(h_pad=0,w_pad=0)
fig.suptitle('Gene {}'.format(g))
return fig
