from pydiffexp import DEPlot
from pydiffexp.utils.io import read_dea_pickle
pd.set_option('display.width', 1000)
# Load the DEAnalysis object with fits and data
dea = read_dea_pickle("./sprouty_pickle.pkl")
# Initialize a plotting object
dep = DEPlot(dea)
#
# dep.tsplot(dea.data.loc['SPRY2'])
# plt.tight_layout()
# plt.show()
dep.heatmap()
plt.savefig(
'/Users/jfinkle/Dropbox/Justin Finkle, shared/Media/Sprouty/images/row_max_normalized_heatmap.png',
fmt='png',
dpi=600)
#
#
sys.exit()
# Volcano Plot
x = dea.results['KO-WT'].top_table(coef=1, use_fstat=False)
# dep.volcano_plot(x, top_n=5, show_labels=True)
# Time Series Plot
x = dea.data.loc['IVL']
def plot_collections(hm_data, hash_data, term_data, output='show'):
# Organize plot
# Overall gridspec with 1 row, two columns
f = plt.figure(figsize=(10, 10))
gs = gridspec.GridSpec(1, 2)
# Create a gridspec within the gridspec. 1 row and 2 columns, specifying width ratio
gs_left = gridspec.GridSpecFromSubplotSpec(
2,
2,
subplot_spec=gs[0],
width_ratios=[hm_data.shape[1], 2],
height_ratios=[1, 50],
wspace=0.05,
hspace=0.05)
gs_right = gridspec.GridSpecFromSubplotSpec(2,
1,
subplot_spec=gs[1],
height_ratios=[1, 1.5],
hspace=0.25)
cbar_ax = plt.subplot(gs_left[0, 0])
hidden_ax = plt.subplot(gs_left[0, 1])
hm_ax = plt.subplot(gs_left[1, 0])
hash_ax = plt.subplot(gs_left[1, 1])
gene_ax = plt.subplot(gs_right[0])
go_ax = plt.subplot(gs_right[1])
# Hide the top right axes where the venn diagram goes
gene_ax.axis('off')
# Initialize plotter
dep = DEPlot()
hm_ax, hash_ax = dep.heatmap(hm_data,
hash_data,
hm_ax=hm_ax,
hash_ax=hash_ax,
cbar_ax=cbar_ax,
yticklabels=False,
cbar_kws=dict(orientation='horizontal',
ticks=[-1, 0, 1]))
cbar_ax.xaxis.tick_top()
cbar_ax.invert_xaxis()
hidden_ax.set_xlabel('')
hidden_ax.set_ylabel('')
hidden_ax.axis('off')
index_order = [
'DEG', 'DDE', 'DRG', 'DEG∩DDE', 'DEG∩DRG', 'DDE∩DRG', 'DEG∩DDE∩DRG',
'All'
]
c_index = [1, 7, 5, 9, 3, 6]
colors = [Prism_10.mpl_colors[idx] for idx in c_index] + ['k', '0.5']
cmap = {gc: colors[ii] for ii, gc in enumerate(index_order)}
x = term_data.reset_index(level=0)
x.columns = ['gene_class'] + x.columns[1:].values.tolist()
full_dist = x.copy()
full_dist['gene_class'] = "All"
full_dist.reset_index(drop=True, inplace=True)
all_x = pd.concat([x, full_dist])
g = all_x.groupby('gene_class')
go_ax = sns.boxplot(data=g.filter(lambda xx: True),
x='depth',
y='gene_class',
order=index_order,
ax=go_ax,
showfliers=False,
boxprops=dict(linewidth=0),
medianprops=dict(solid_capstyle='butt', color='w'),
palette=cmap)
small_groups = g.filter(lambda x: len(x) < 50)
go_ax = sns.swarmplot(data=small_groups,
x='depth',
y='gene_class',
order=index_order,
ax=go_ax,
color='k')
go_ax.plot([x['depth'].median(), x['depth'].median()],
go_ax.get_ylim(),
'k-',
lw=2,
zorder=0,
c='0.25')
term_sizes = g.apply(len).reindex(index_order).fillna(0).astype(int)
y_ticks = ["n={}".format(term_sizes.loc[idx]) for idx in index_order]
go_ax.set_yticklabels(y_ticks)
go_ax.set_ylabel('')
plt.tight_layout()
if output.lower() == 'show':
plt.show()
else:
plt.savefig(output, fmt='pdf')