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Plots.py
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from Measures import BuildMatrices, measures
from Utils import FancyApp, ColourClass
from collections import defaultdict
import seaborn as sns
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import argparse
import os
import matplotlib
matplotlib.use('Agg')
# from Utils.notification import load_configuration, get_api
def save_list_to_txt(l, file_name):
with open(file_name, "w") as out:
out.write("\n".join(map(str, l)))
def tell(*args, **kwargs):
FancyApp.FancyApp.yell(
ColourClass.bcolors.BOLD_GREEN, 'plotter', *args, **kwargs)
aparser = argparse.ArgumentParser()
required_arguments = aparser.add_argument_group('required arguments')
required_arguments.add_argument('--bacteria-selection',
help='pickle file that contains the informtion'
' on the selected bacteria',
required=True)
required_arguments.add_argument('--s2f-dir',
help='S2F directory', required=True)
required_arguments.add_argument('--fasta-directory',
help='directory where all the fasta files'
' can be found', required=True)
required_arguments.add_argument('--goa-directory',
help='directory where all the goa files'
' can be found, the names need to match '
'the ones defined by the '
'`--bacteria-seleciton` argument',
required=True)
required_arguments.add_argument('--plots-directory',
help='directory where the plots will be saved',
required=True)
required_arguments.add_argument('--matrices-directory',
help='directory where the scores matrices will'
' be stored', required=True)
required_arguments.add_argument('--obo',
help='go.obo file', required=True)
required_arguments.add_argument('--plot-individual', action='store_true')
required_arguments.add_argument('--compute-metrics', action='store_true')
args = aparser.parse_args()
obo = args.obo
S2F_DIR = args.s2f_dir
GOA_DIRECTORY = args.goa_directory
FASTA_DIRECTORY = args.fasta_directory
PLOTS_DIRECTORY = args.plots_directory
MATRICES_DIRECTORY = args.matrices_directory
# load the selected organisms pandas fil
final_selection = pd.read_pickle(args.bacteria_selection)
all_organisms = pd.DataFrame()
# for coloring the plots
flatui = ["#705319", "#e5aa34", "#911f76", "#e534bb", "#1c7c69", "#31d6b5", ]
flatui3 = ["#e5aa34", "#e534bb", "#31d6b5", ]
# for nice labelling
labels = [
('I+H+D', 'S2F'),
('I+H+CM', 'InterPro + HMMER + CM'),
('I+H', 'InterPro + HMMER'),
('I', 'InterPro'),
('I+D', 'InterPro w/ diffusion'),
('I+CM', 'InterPro + CM'),
('H', 'HMMER'),
('H+D', 'HMMER w/ diffusion'),
('H+CM', 'HMMER + CM'),
]
# we have these values per threshold, so they are unsafe for plotting
unsafe_metrics = ['roc', 'pr', 'tp', 'fp', 'tn', 'fn', 's', 'ru', 'mi',
'AUC per-gene raw', 'AUPR per-gene raw',
'Precision at 0.2 Recall per-gene raw', 'F_max per-gene raw',
'NDCG per-gene raw', 'Jaccard per-gene raw',
'smin per-gene raw', 'AUC per-term raw', 'AUPR per-term raw',
'Precision at 0.2 Recall per-term raw', 'F_max per-term raw',
'NDCG per-term raw', 'Jaccard per-term raw',
'smin per-term raw']
for i, organism in final_selection.sort_values(by='Tax ID').iterrows():
tell('processing organism: ', organism['Organism'],
'(', organism['Tax ID'], ')...')
interpro_file = os.path.join(
S2F_DIR, f'seeds/interpro/{organism["Tax ID"]}.seed.txt')
hmmer_file = os.path.join(
S2F_DIR, f'seeds/hmmer/{organism["Tax ID"]}.seed.txt')
interpro_diff_file = os.path.join(
S2F_DIR, f'output/{organism["Tax ID"]}/ip_seed.diffusion')
hmmer_diff_file = os.path.join(
S2F_DIR, f'output/{organism["Tax ID"]}/hmmer_seed.diffusion')
hmmer_cm_file = os.path.join(
S2F_DIR, f'output/{organism["Tax ID"]}/hmmer_seed.diffusion.cm')
interpro_cm_file = os.path.join(
S2F_DIR, f'output/{organism["Tax ID"]}/ip_seed.diffusion.cm')
s2f_file = os.path.join(
S2F_DIR, f'output/{organism["Tax ID"]}/prediction.df')
goa = os.path.join(GOA_DIRECTORY, organism['File'])
fasta = os.path.join(FASTA_DIRECTORY, organism['Tax ID'] + '.fasta')
org_dir = os.path.join(MATRICES_DIRECTORY, organism['Tax ID'] + '/')
if os.path.exists(org_dir + 'metrics_df.pkl'):
tell('Found saved metrics file, loading...')
metrics_df = pd.read_pickle(org_dir + 'metrics_df.pkl')
else:
if os.path.exists(org_dir):
tell('Found precomputed matrices, loading...')
goa_values = np.load(org_dir + 'goa_values.npy')
hmmer_diff_values = np.load(org_dir + 'hmmer_diff_values.npy')
hmmer_cm_values = np.load(org_dir + 'hmmer_cm_values.npy')
hmmer_values = np.load(org_dir + 'hmmer_values.npy')
interpro_diff_values = np.load(org_dir +
'interpro_diff_values.npy')
interpro_cm_values = np.load(org_dir + 'interpro_cm_values.npy')
interpro_values = np.load(org_dir + 'interpro_values.npy')
s2f_values = np.load(org_dir + 's2f_values.npy')
information_content = np.load(org_dir + 'information_content.npy')
else:
os.makedirs(org_dir)
matrix_builder = BuildMatrices.BuildMatrices(
interpro_file, interpro_diff_file, interpro_cm_file,
hmmer_file, hmmer_diff_file, hmmer_cm_file, s2f_file, goa,
fasta, obo)
np.save(org_dir + 'goa_values.npy', matrix_builder.goa_values)
np.save(org_dir + 'hmmer_diff_values.npy',
matrix_builder.hmmer_diff_values)
np.save(org_dir + 'hmmer_cm_values.npy',
matrix_builder.hmmer_cm_values)
np.save(org_dir + 'hmmer_values.npy', matrix_builder.hmmer_values)
np.save(org_dir + 'interpro_diff_values.npy',
matrix_builder.interpro_diff_values)
np.save(org_dir + 'interpro_cm_values.npy',
matrix_builder.interpro_cm_values)
np.save(org_dir + 'interpro_values.npy',
matrix_builder.interpro_values)
np.save(org_dir + 's2f_values.npy', matrix_builder.s2f_values)
np.save(org_dir + 'information_content.npy',
matrix_builder.information_content)
save_list_to_txt(org_dir + "genes.txt", matrix_builder.genes)
save_list_to_txt(org_dir + "goterms.txt", matrix_builder.go_terms)
goa_values = matrix_builder.goa_values
hmmer_diff_values = matrix_builder.hmmer_diff_values
hmmer_cm_values = matrix_builder.hmmer_cm_values
hmmer_values = matrix_builder.hmmer_values
interpro_diff_values = matrix_builder.interpro_diff_values
interpro_cm_values = matrix_builder.interpro_cm_values
interpro_values = matrix_builder.interpro_values
s2f_values = matrix_builder.s2f_values
information_content = matrix_builder.information_content
if args.compute_metrics:
# ranges as HX's method
group_name = ['3-10', '11-30', '31-100', '101-300', '1-300', 'all']
ranges = [(3, 10), (11, 30), (31, 100),
(101, 300), (1, 300), (1, 1000000)]
group_name = ['all']
ranges = [(1, 1000000)]
s2f_nodiff = 0.9 * interpro_values + 0.1 * hmmer_values
i_h_cm_nodiff = 0.9 * interpro_cm_values + 0.1 * hmmer_cm_values
sumcol = np.sum(goa_values, axis=0)
sumrow = np.sum(goa_values, axis=1)
predictions = []
gold_standards = []
information_contents = []
for low, high in ranges:
pred = {
'I': interpro_values[sumrow > 3, :][
:, (sumcol >= low) & (sumcol <= high)],
'I+D': interpro_diff_values[sumrow > 3, :][
:, (sumcol >= low) & (sumcol <= high)],
'I+CM': interpro_cm_values[sumrow > 3, :][
:, (sumcol >= low) & (sumcol <= high)],
'H': hmmer_values[sumrow > 3, :][
:, (sumcol >= low) & (sumcol <= high)],
'H+D': hmmer_diff_values[sumrow > 3, :][
:, (sumcol >= low) & (sumcol <= high)],
'H+CM': hmmer_cm_values[sumrow > 3, :][
:, (sumcol >= low) & (sumcol <= high)],
'I+H': s2f_nodiff[sumrow > 3, :][
:, (sumcol >= low) & (sumcol <= high)],
'I+H+CM': i_h_cm_nodiff[sumrow > 3, :][
:, (sumcol >= low) & (sumcol <= high)],
'I+H+D': s2f_values[sumrow > 3, :][
:, (sumcol >= low) & (sumcol <= high)]}
predictions.append(pred)
gold_standards.append(
goa_values[sumrow > 3, :][
:, (sumcol >= low) & (sumcol <= high)])
information_contents.append(
information_content[(sumcol >= low) & (sumcol <= high)])
data = defaultdict(list)
meas = {}
res = {}
for pred, gs, r, ic in zip(predictions, gold_standards, group_name,
information_contents):
tell('Processing range:', r)
# HX measure calculator
for k in pred.keys():
tell(k)
rounded_pred = pred[k]
if len(np.unique(pred[k])) > 10000:
rounded_pred = np.around(pred[k], decimals=4)
meas[k] = measures.HX_py(rounded_pred, ic,
organism['Tax ID'])
tell('computing OVERALL')
res[k] = meas[k].compute_overall(gs)
for m in res[k].keys():
data[m].append([k, r, res[k][m]])
tell('computing per-gene')
res[k] = meas[k].compute_per_gene(gs)
for m in res[k].keys():
data[m].append([k, r, res[k][m]])
tell('computing per-term')
res[k] = meas[k].compute_per_term(gs)
for m in res[k].keys():
data[m].append([k, r, res[k][m]])
tell('saving metrics')
# save all metrics
metrics = []
for k, l in data.items():
for v in l:
metrics.append((k, v[0], v[1], v[2]))
metrics_df = pd.DataFrame(
metrics, columns=['metric', 'method', 'range', 'value'])
metrics_df.to_pickle(org_dir + 'metrics_df.pkl')
if args.compute_metrics:
sns.palplot(sns.color_palette(flatui))
metrics_df = metrics_df.sort_values(by=['metric', 'range', 'method'])
metrics_df['organism'] = organism['Organism']
for source, dest in labels:
metrics_df['method'].replace(source, dest, inplace=True)
all_organisms = all_organisms.append(metrics_df, ignore_index=True)
if args.plot_individual:
tell('Generating figures...')
for metric in metrics_df['metric'].unique():
if metric in unsafe_metrics:
continue
tell(f'metric: {metric}')
plot_selection = metrics_df[metrics_df['metric'] == metric]
if 'per-' in metric:
metric += '(mean)'
# PLOT WITH RANGES
a4_dims = (11.7, 8.27)
fig, ax = plt.subplots(figsize=a4_dims, dpi=100)
g = sns.barplot(ax=ax,
data=plot_selection,
x='range',
y='value',
hue='method',
palette=flatui)
for p in g.patches:
ax.annotate(
"%.3f" % p.get_height(),
(p.get_x() + p.get_width() / 2., p.get_height()),
ha='center',
va='center',
fontsize=9,
xytext=(1, -20),
rotation=90,
textcoords='offset points',
color='white',
weight='black')
ax.set(ylabel=metric)
ax.set(title=organism['Organism'] + ' ' + organism['Tax ID'])
ax.legend(bbox_to_anchor=(1.0, 0.5))
fig.savefig(
PLOTS_DIRECTORY +
organism['Tax ID'] + ' ' + metric + '-ranges.png')
plt.close(fig)
# PLOT WITHOUT RANGES
fig, ax = plt.subplots(figsize=a4_dims, dpi=100)
g = sns.barplot(
ax=ax,
data=plot_selection[plot_selection['range'] == 'all'],
x='method',
y='value',
palette=flatui)
for p in g.patches:
ax.annotate(
"%.3f" % p.get_height(),
(p.get_x() + p.get_width() / 2., p.get_height()),
ha='center',
va='center',
fontsize=11,
xytext=(0, 10),
textcoords='offset points')
ax.set(ylabel=metric)
ax.set(title=organism['Organism'] + ' ' + organism['Tax ID'])
fig.savefig(
PLOTS_DIRECTORY+organism['Tax ID']+' '+metric+'.png')
plt.close(fig)
if args.compute_metrics:
tell('Generating figures for all organisms...')
# plot all organisms
all_organisms.organism = all_organisms.organism.str.split(
'(', n=0, expand=True)[0]
all_organisms.organism = all_organisms.organism.str.split(
'ATCC', n=0, expand=True)[0]
all_organisms.organism = all_organisms.organism.str.split(
'MG', n=0, expand=True)[0]
all_organisms.to_pickle(
os.path.join(MATRICES_DIRECTORY, 'all_organisms.pkl'))
for metric in all_organisms['metric'].unique():
if metric in unsafe_metrics:
continue
methods = ['InterPro', 'HMMER', 'S2F']
fig, ax = plt.subplots(figsize=(11.7, 6.27), dpi=100)
condition = (all_organisms['range'] == 'all') & \
(all_organisms['metric'] == metric) & \
(all_organisms['method'].isin(methods))
g = sns.barplot(ax=ax, data=all_organisms[condition],
x='organism', y='value', hue='method', palette=flatui3)
for p in g.patches:
ax.annotate(
"%.3f" % p.get_height(),
(p.get_x() + p.get_width() / 2., p.get_height()),
ha='center',
va='center',
fontsize=9,
xytext=(1, -20),
rotation=90,
textcoords='offset points',
color='white',
weight='black')
ax.set_xticklabels(ax.get_xticklabels(), rotation=-25, ha='left')
ax.set(ylabel=metric)
ax.set(title='')
ax.legend(bbox_to_anchor=(1.0, 0.5))
plt.savefig(
os.path.join(PLOTS_DIRECTORY, 'ALL '+metric+'.png'),
bbox_inches='tight')
plt.close(fig)