def scatter_plot(config_file,
plot_file,
burnin=0,
max_clusters=None,
mesh_size=101,
min_cluster_size=0,
samples=None,
thin=1):
utils.setup_plot()
df = post_process.clusters.load_summary_table(
config_file,
burnin=burnin,
max_clusters=max_clusters,
mesh_size=mesh_size,
min_size=min_cluster_size,
thin=thin,
)
mean_df = df.pivot(index='cluster_id', columns='sample_id', values='mean')
error_df = df.pivot(index='cluster_id', columns='sample_id', values='std')
if samples is None:
samples = sorted(df['sample_id'].unique())
color_map = utils.get_clusters_color_map(pd.Series(df['cluster_id']))
_scatter.plot_all_pairs(color_map,
mean_df,
plot_file,
samples,
error_df=error_df)
def scatter_plot(config_file,
plot_file,
burnin=0,
max_clusters=None,
min_cluster_size=0,
samples=None,
thin=1,
value='cellular_prevalence'):
utils.setup_plot()
df = post_process.loci.load_table(config_file,
burnin,
thin,
max_clusters=max_clusters,
min_cluster_size=min_cluster_size)
if samples is None:
samples = sorted(df['sample_id'].unique())
color_map = utils.get_clusters_color_map(df['cluster_id'])
cluster_df = df[['mutation_id',
'cluster_id']].drop_duplicates().set_index('mutation_id')
loci_color_map = cluster_df['cluster_id'].map(color_map).to_dict()
mean_df = df.pivot(index='mutation_id', columns='sample_id', values=value)
_scatter.plot_all_pairs(loci_color_map,
mean_df,
plot_file,
samples,
legend_color_map=color_map)
def density_plot(config_file,
plot_file,
burnin=0,
max_clusters=None,
mesh_size=101,
min_cluster_size=0,
samples=None,
thin=1):
df = post_process.clusters.load_table(config_file,
burnin=burnin,
thin=thin,
max_clusters=max_clusters,
mesh_size=mesh_size,
min_size=min_cluster_size)
sizes = df[['cluster_id', 'size'
]].drop_duplicates().set_index('cluster_id').to_dict()['size']
if samples is None:
samples = sorted(df['sample_id'].unique())
else:
df = df[df['sample_id'].isin(samples)]
num_samples = len(samples)
clusters = df['cluster_id'].unique()
postions = range(1, len(clusters) + 1)
utils.setup_plot()
width = 8
height = 2 * num_samples + 1
fig = pp.figure(figsize=(width, height))
grid = gs.GridSpec(nrows=num_samples, ncols=1)
colors = utils.get_clusters_color_map(pd.Series(clusters))
for ax_index, sample_id in enumerate(samples):
plot_df = df[df['sample_id'] == sample_id]
plot_df = plot_df.drop(['sample_id', 'size'],
axis=1).set_index('cluster_id')
ax = fig.add_subplot(grid[ax_index])
utils.setup_axes(ax)
ax.annotate(sample_id,
xy=(1.01, 0.5),
xycoords='axes fraction',
fontsize=defaults.axis_label_font_size)
for i, (cluster_id, log_pdf) in enumerate(plot_df.iterrows()):
pos = postions[i]
y = log_pdf.index.astype(float)
x = np.exp(log_pdf)
x = (x / x.max()) * 0.3
ax.fill_betweenx(y,
pos - x,
pos + x,
color=colors[cluster_id],
where=(x > 1e-6))
ax.set_xticks(postions)
if ax_index == (num_samples - 1):
x_tick_labels = [
'{0} (n={1})'.format(x, sizes[x]) for x in clusters
]
ax.set_xticklabels(x_tick_labels, rotation=90)
ax.set_xlabel(defaults.cluster_label,
fontsize=defaults.axis_label_font_size)
else:
ax.set_xticklabels([])
utils.set_tick_label_font_sizes(ax, defaults.tick_label_font_size)
ax.set_ylim(defaults.cellular_prevalence_limits)
if num_samples == 1:
ax.set_ylabel(defaults.cellular_prevalence_label,
fontsize=defaults.axis_label_font_size)
else:
fig.text(-0.01,
0.5,
defaults.cellular_prevalence_label,
fontsize=defaults.axis_label_font_size,
ha='center',
rotation=90,
va='center')
grid.tight_layout(fig)
utils.save_figure(fig, plot_file)
def parallel_coordinates_plot(config_file,
plot_file,
burnin=0,
max_clusters=None,
mesh_size=101,
min_cluster_size=0,
samples=None,
thin=1):
utils.setup_plot()
plot_df = post_process.clusters.load_summary_table(
config_file,
burnin=burnin,
max_clusters=max_clusters,
mesh_size=mesh_size,
min_size=min_cluster_size,
thin=thin,
)
if samples is None:
samples = sorted(plot_df['sample_id'].unique())
else:
plot_df = plot_df[plot_df['sample_id'].isin(samples)]
clusters = sorted(plot_df['cluster_id'].unique())
plot_df['sample_index'] = plot_df['sample_id'].apply(
lambda x: samples.index(x))
plot_df = plot_df.sort_values(by='sample_index')
grid = sb.FacetGrid(plot_df,
hue='cluster_id',
hue_order=clusters,
palette='husl')
grid.map(pp.errorbar,
'sample_index',
'mean',
'std',
marker=defaults.line_plot_marker,
markersize=defaults.line_plot_marker_size)
ax = grid.ax
utils.setup_axes(ax)
fig = grid.fig
# Legend
box = ax.get_position()
ax.set_position([box.x0, box.y0, box.width * 0.8, box.height])
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5), title='Cluster')
# Axis formatting
ax.set_xticks(sorted(plot_df['sample_index'].unique()))
ax.set_xticklabels(samples)
ax.set_xlabel(defaults.sample_label,
fontsize=defaults.axis_label_font_size)
ax.set_ylabel(defaults.cellular_prevalence_label,
fontsize=defaults.axis_label_font_size)
utils.set_tick_label_font_sizes(ax, defaults.tick_label_font_size)
# Plot limits
ax.set_xlim(plot_df['sample_index'].min() - 0.1,
plot_df['sample_index'].max() + 0.1)
ax.set_ylim(*defaults.cellular_prevalence_limits)
# Resize and save figure
fig.set_size_inches(*utils.get_parallel_coordinates_figure_size(samples))
utils.save_figure(fig, plot_file)
def density_plot(config_file, plot_file, burnin=0, samples=None, thin=1):
utils.setup_plot()
df = _load_density_df(config_file, burnin, thin)
if samples is None:
samples = sorted(df['sample_id'].unique())
else:
df = df[df['sample_id'].isin(samples)]
loci = df['mutation_id'].unique()
num_loci = len(loci)
width = 8
height = 2 * num_loci + 2
fig = pp.figure(figsize=(width, height))
grid = gs.GridSpec(nrows=num_loci, ncols=1)
for ax_index, locus in enumerate(loci):
ax = fig.add_subplot(grid[ax_index])
utils.setup_axes(ax)
plot_df = df[df['mutation_id'] == locus]
sb.violinplot(ax=ax,
data=plot_df,
x='sample_id',
y='cellular_prevalence',
inner=None,
order=samples,
scale='width')
ax.set_ylabel('')
if ax_index != (num_loci - 1):
ax.set_xticklabels([])
ax.set_xlabel('')
else:
ax.set_xlabel(defaults.sample_label)
ax.set_ylim(*defaults.cellular_prevalence_limits)
ax.annotate(locus,
xy=(1.01, 0.5),
xycoords='axes fraction',
fontsize=defaults.axis_label_font_size)
utils.set_tick_label_font_sizes(ax, defaults.tick_label_font_size)
fig.text(-0.01,
0.5,
defaults.cellular_prevalence_label,
fontsize=defaults.axis_label_font_size,
ha='center',
rotation=90,
va='center')
grid.tight_layout(fig, h_pad=3)
utils.save_figure(fig, plot_file)
def parallel_coordinates_plot(config_file,
plot_file,
burnin=0,
max_clusters=None,
min_cluster_size=0,
samples=None,
thin=1,
value='cellular_prevalence'):
utils.setup_plot()
df = post_process.loci.load_table(config_file,
burnin,
thin,
max_clusters=max_clusters,
min_cluster_size=min_cluster_size)
color_map = utils.get_clusters_color_map(df['cluster_id'])
if samples is None:
samples = sorted(df['sample_id'].unique())
else:
df = df[df['sample_id'].isin(samples)]
df['sample_index'] = df['sample_id'].apply(lambda x: samples.index(x))
df = df.sort_values(by='sample_index')
fig = pp.figure()
ax = fig.add_subplot(1, 1, 1)
utils.setup_axes(ax)
for cluster_id, cluster_df in df.groupby('cluster_id'):
for _, locus_df in cluster_df.groupby('mutation_id'):
x = locus_df['sample_index']
y = locus_df[value]
ax.plot(x,
y,
alpha=0.75,
c=color_map[cluster_id],
marker=defaults.line_plot_marker,
markersize=defaults.line_plot_marker_size)
ax.set_xlabel(defaults.sample_label,
fontsize=defaults.axis_label_font_size)
if value == 'cellular_prevalence':
ax.set_ylabel(defaults.cellular_prevalence_label,
fontsize=defaults.axis_label_font_size)
elif value == 'variant_allele_frequency':
ax.set_ylabel(defaults.variant_allele_frequency_label)
ax.set_xticks(sorted(df['sample_index'].unique()))
ax.set_xticklabels(samples)
utils.set_tick_label_font_sizes(ax, defaults.tick_label_font_size)
ax.set_ylim(*defaults.cellular_prevalence_limits)
box = ax.get_position()
ax.set_position([box.x0, box.y0, box.width * 0.8, box.height])
legend_handles = utils.get_legend_handles(color_map)
legend = ax.legend(legend_handles.values(),
legend_handles.keys(),
bbox_to_anchor=(1, 0.5),
fontsize=defaults.legend_font_size,
loc='center left',
title=defaults.cluster_label)
legend.get_title().set_fontsize(defaults.legend_title_font_size)
utils.save_figure(fig, plot_file)