def plot_insert_len(insert_len_filename, settings_filename, output_dir):
"""
Plot insert length distribution.
"""
if not os.path.isfile(settings_filename):
print "Error: settings filename %s not found." % (settings_filename)
#sys.exit(1)
plot_name = os.path.basename(insert_len_filename)
sashimi_obj = Sashimi(plot_name,
output_dir,
settings_filename=settings_filename)
settings = sashimi_obj.settings
num_bins = settings["insert_len_bins"]
output_filename = sashimi_obj.output_filename
sashimi_obj.setup_figure()
s = plt.subplot(1, 1, 1)
print "Plotting insert length distribution..."
print " - Distribution file: %s" % (insert_len_filename)
print " - Output plot: %s" % (output_filename)
insert_dist, params = pe_utils.load_insert_len(insert_len_filename)
mean, sdev, dispersion, num_pairs \
= pe_utils.compute_insert_len_stats(insert_dist)
print "min insert: %.1f" % (min(insert_dist))
print "max insert: %.1f" % (max(insert_dist))
plt.title("%s (%d read-pairs)" \
%(plot_name,
num_pairs),
fontsize=10)
plt.hist(insert_dist,
bins=num_bins,
color='k',
edgecolor="#ffffff",
align='mid')
axes_square(s)
ymin, ymax = s.get_ylim()
plt.text(0.05, 0.95, "$\mu$: %.1f\n$\sigma$: %.1f\n$d$: %.1f" \
%(round(mean, 2),
round(sdev, 2),
round(dispersion, 2)),
horizontalalignment='left',
verticalalignment='top',
bbox=dict(edgecolor='k', facecolor="#ffffff",
alpha=0.5),
fontsize=10,
transform=s.transAxes)
plt.xlabel("Insert length (nt)")
plt.ylabel("No. read pairs")
sashimi_obj.save_plot()
def plot_insert_len(insert_len_filename,
settings_filename,
output_dir):
"""
Plot insert length distribution.
"""
if not os.path.isfile(settings_filename):
print "Error: settings filename %s not found." %(settings_filename)
#sys.exit(1)
plot_name = os.path.basename(insert_len_filename)
sashimi_obj = Sashimi(plot_name, output_dir,
settings_filename=settings_filename)
settings = sashimi_obj.settings
num_bins = settings["insert_len_bins"]
output_filename = sashimi_obj.output_filename
sashimi_obj.setup_figure()
s = plt.subplot(1, 1, 1)
print "Plotting insert length distribution..."
print " - Distribution file: %s" %(insert_len_filename)
print " - Output plot: %s" %(output_filename)
insert_dist, params = pe_utils.load_insert_len(insert_len_filename)
mean, sdev, dispersion, num_pairs \
= pe_utils.compute_insert_len_stats(insert_dist)
print "min insert: %.1f" %(min(insert_dist))
print "max insert: %.1f" %(max(insert_dist))
plt.title("%s (%d read-pairs)" \
%(plot_name,
num_pairs),
fontsize=10)
plt.hist(insert_dist, bins=num_bins, color='k',
edgecolor="#ffffff", align='mid')
axes_square(s)
ymin, ymax = s.get_ylim()
plt.text(0.05, 0.95, "$\mu$: %.1f\n$\sigma$: %.1f\n$d$: %.1f" \
%(round(mean, 2),
round(sdev, 2),
round(dispersion, 2)),
horizontalalignment='left',
verticalalignment='top',
bbox=dict(edgecolor='k', facecolor="#ffffff",
alpha=0.5),
fontsize=10,
transform=s.transAxes)
plt.xlabel("Insert length (nt)")
plt.ylabel("No. read pairs")
sashimi_obj.save_plot()
def plot_density_from_file(settings_f,
pickle_filename,
event,
output_dir,
group_info=None,
no_posteriors=False,
plot_title=None,
plot_label=None):
"""
Read MISO estimates given an event name.
"""
##
## Read information about gene
##
tx_start, tx_end, exon_starts, exon_ends, gene_obj, mRNAs, strand, chrom = \
parseGene(pickle_filename, event)
# Override settings flag on whether to show posterior plots
# if --no-posteriors was given to plot.py
sashimi_obj = Sashimi(event,
output_dir,
event=event,
chrom=chrom,
settings_filename=settings_f,
no_posteriors=no_posteriors)
print "Plotting read densities and MISO estimates along event..."
print " - Event: %s" % (event)
settings = sashimi_obj.settings
if no_posteriors:
settings["show_posteriors"] = False
# bam_files = settings['bam_files']
# miso_files = settings['miso_files']
# Setup the figure
sashimi_obj.setup_figure()
plot_density(sashimi_obj,
pickle_filename,
event,
group_info=group_info,
plot_title=plot_title)
# Save figure
sashimi_obj.save_plot(plot_label=plot_label)
def plot_posterior(miso_filename, settings_filename, output_dir,
plot_mean=False, with_intervals=95):
"""
Plot posterior distribution.
"""
if not os.path.isfile(settings_filename):
print "Error: settings filename %s not found." %(settings_filename)
sys.exit(1)
# samples, log_scores, params, gene = load_samples(miso_filename)
samples, h, log_scores, sampled_map,\
sampled_map_log_score, counts_info = load_samples(miso_filename)
params = parse_sampler_params(miso_filename)
plot_name = os.path.basename(miso_filename).replace(".miso", "")
sashimi_obj = Sashimi(plot_name, output_dir,
settings_filename=settings_filename)
settings = sashimi_obj.settings
# Setup the figure
sashimi_obj.setup_figure()
dimensions = (settings["fig_width"],
settings["fig_height"])
sp = SamplesPlotter(samples, params)
if with_intervals != None:
with_intervals = float(with_intervals)/100.
print "Plotting with %d-percent confidence intervals" %(int(with_intervals * 100))
else:
with_intervals = False
if plot_mean:
print "Plotting mean of posterior."
print "Plotting posterior distribution..."
print " - MISO event file: %s" %(miso_filename)
print " - Output dir: %s" %(output_dir)
sashimi_obj.setup_figure()
sp.plot(plot_intervals=with_intervals, fig_dims=dimensions,
plot_mean=plot_mean)
sashimi_obj.save_plot()
def plot_density_from_file(settings_f, pickle_filename, event,
output_dir,
no_posteriors=False,
plot_title=None,
plot_label=None):
"""
Read MISO estimates given an event name.
"""
##
## Read information about gene
##
tx_start, tx_end, exon_starts, exon_ends, gene_obj, mRNAs, strand, chrom = \
parseGene(pickle_filename, event)
#chrom = str(chrom)[3:]
# Override settings flag on whether to show posterior plots
# if --no-posteriors was given to plot.py
sashimi_obj = Sashimi(event, output_dir,
event=event,
chrom=chrom,
settings_filename=settings_f,
no_posteriors=no_posteriors)
#print "Plotting read densities and MISO estimates along event..."
#print " - Event: %s" %(event)
settings = sashimi_obj.settings
if no_posteriors:
settings["show_posteriors"] = False
bam_files = settings['bam_files']
miso_files = settings['miso_files']
# Setup the figure
sashimi_obj.setup_figure()
plot_density(sashimi_obj, pickle_filename, event,
plot_title=plot_title)
# Save figure
sashimi_obj.save_plot(plot_label=plot_label)
def plot_bf_dist(bf_filename, settings_filename, output_dir, max_bf=1e12):
"""
Plot a Bayes factor distribution from a .miso_bf file.
"""
if not bf_filename.endswith(".miso_bf"):
print "WARNING: %s does not end in .miso_bf, are you sure it is the " \
"output of a MISO samples comparison?" %(bf_filename)
# Load BF data
data, h = csv2dictlist_raw(bf_filename)
plot_name = os.path.basename(bf_filename)
sashimi_obj = Sashimi(plot_name,
output_dir,
settings_filename=settings_filename)
settings = sashimi_obj.settings
# Setup the figure
sashimi_obj.setup_figure()
# Matrix of bayes factors and delta psi pairs
bfs_and_deltas = []
for event in data:
bf = event['bayes_factor']
delta_psi = event['diff']
if type(bf) == str and "," in bf:
print "WARNING: %s is a multi-isoform event, skipping..." \
%(event)
continue
else:
# Impose upper limit on Bayes factor
bf = min(1e12, float(bf))
delta_psi = float(delta_psi)
bfs_and_deltas.append([bf, delta_psi])
bfs_and_deltas = array(bfs_and_deltas)
num_events = len(bfs_and_deltas)
print "Loaded %d event comparisons." % (num_events)
output_filename = sashimi_obj.output_filename
print "Plotting Bayes factors distribution"
print " - Output filename: %s" % (output_filename)
bf_thresholds = settings["bf_thresholds"]
bar_color = settings["bar_color"]
min_bf_thresh = min(bf_thresholds)
num_events_used = sum(bfs_and_deltas[:, 0] >= min_bf_thresh)
for thresh in bf_thresholds:
if type(thresh) != int:
print "Error: BF thresholds must be integers."
#sys.exit(1)
print "Using BF thresholds: "
print bf_thresholds
print "Using bar color: %s" % (bar_color)
plot_cumulative_bars(bfs_and_deltas[:, 0],
bf_thresholds,
bar_color=bar_color,
logged=True)
plt.xticks(bf_thresholds)
c = 1
plt.xlim([bf_thresholds[0] - c, bf_thresholds[-1] + c])
plt.title("Bayes factor distributions\n(using %d/%d events)" \
%(num_events_used, num_events))
plt.xlabel("Bayes factor thresh.")
plt.ylabel("No. events")
sashimi_obj.save_plot()
def plot_bf_dist(bf_filename, settings_filename, output_dir,
max_bf=1e12):
"""
Plot a Bayes factor distribution from a .miso_bf file.
"""
if not bf_filename.endswith(".miso_bf"):
print "WARNING: %s does not end in .miso_bf, are you sure it is the "
"output of a MISO samples comparison?" %(bf_filename)
# Load BF data
data, h = csv2dictlist_raw(bf_filename)
plot_name = os.path.basename(bf_filename)
sashimi_obj = Sashimi(plot_name, output_dir,
settings_filename=settings_filename)
settings = sashimi_obj.settings
# Setup the figure
sashimi_obj.setup_figure()
# Matrix of bayes factors and delta psi pairs
bfs_and_deltas = []
for event in data:
bf = event['bayes_factor']
delta_psi = event['diff']
print bf
if type(bf) == str and "," in bf:
print "WARNING: %s is a multi-isoform event, skipping..."
continue
elif isinstance(bf,float) :
# Impose upper limit on Bayes factor
bf = min(1e12, float(bf))
delta_psi = float(delta_psi)
else:
continue
bfs_and_deltas.append([bf, delta_psi])
bfs_and_deltas = array(bfs_and_deltas)
num_events = len(bfs_and_deltas)
print "Loaded %d event comparisons." %(num_events)
output_filename = sashimi_obj.output_filename
print "Plotting Bayes factors distribution"
print " - Output filename: %s" %(output_filename)
bf_thresholds = settings["bf_thresholds"]
bar_color = settings["bar_color"]
min_bf_thresh = min(bf_thresholds)
num_events_used = sum(bfs_and_deltas[:, 0] >= min_bf_thresh)
for thresh in bf_thresholds:
if type(thresh) != int:
print "Error: BF thresholds must be integers."
sys.exit(1)
print "Using BF thresholds: "
print bf_thresholds
print "Using bar color: %s" %(bar_color)
plot_cumulative_bars(bfs_and_deltas[:, 0],
bf_thresholds,
bar_color=bar_color,
logged=True)
plt.xticks(bf_thresholds)
c = 1
plt.xlim([bf_thresholds[0] - c, bf_thresholds[-1] + c])
plt.title("Bayes factor distributions\n(using %d/%d events)" \
%(num_events_used, num_events))
plt.xlabel("Bayes factor thresh.")
plt.ylabel("No. events")
sashimi_obj.save_plot()