def generate_figures():
header_data = np.loadtxt(dev_const.PUB_CONDEL_PREDICTION_RESULT,
dtype='S20'
)[:, :5]
scores_data = np.loadtxt(dev_const.PUB_CONDEL_PREDICTION_RESULT,
dtype='S20'
)[:, 5:13].astype(np.float)
min_value = np.amin(scores_data)
max_value = np.amax(scores_data)
predictor_names = ('CombiVEP',
'Phylop',
'SIFT',
'PP2',
'LRT',
'MT',
'GERP',
'Condel',
)
predictor_colors = ('k',
'm',
'c',
'g',
'b',
'coral',
'darkred',
'r',
)
#produce roc data from CombiVEP, Phylop, SIFT, PP2, LRT, MT, GERP, Condel
fp_rates, tp_rates = calculate_roc(scores_data[header_data[:, 4] == '1'],
scores_data[header_data[:, 4] == '0'],
np.linspace(min_value, max_value, 5001))
fig = plt.figure()
ax = fig.add_subplot(111)
for i in xrange(len(predictor_names)):
ax.plot(fp_rates[:, i],
tp_rates[:, i],
predictor_colors[i],
label=predictor_names[i])
ax.set_ylabel('true positive rate')
ax.set_xlabel('false positive rate')
ax.legend(bbox_to_anchor=(0.9999, 0.0001), loc=4)
fig.savefig(dev_const.PUB_ROC_FIG, bbox_inches='tight', pad_inches=0.05)
#produce auc data from roc data
fig = plt.figure()
aucs = []
ind = []
ax = fig.add_subplot(111)
for i in xrange(len(predictor_names)):
aucs.append(auc(fp_rates[:, i], tp_rates[:, i]))
ind.append(0.5*(i+1)-0.4)
ax.bar(ind, aucs, 0.3, color=predictor_colors)
for i in xrange(len(aucs)):
ax.text(ind[i], aucs[i] + 0.01, "%0.3f" % aucs[i])
ax.set_ylim([0.7, 0.9])
ax.set_xticks(np.array(ind) + 0.15)
ax.set_xticklabels(predictor_names, rotation=30)
fig.savefig(dev_const.PUB_AUC_FIG, bbox_inches='tight', pad_inches=0.05)
#plot scores distribution
fig = plt.figure()
ax = fig.add_subplot(211)
hist_range = (-0.005, 1.005)
patho_hist, bins = hist(scores_data[header_data[:, 4] == '1'][:, 0],
bins=100,
range=hist_range)
neutr_hist, bins = hist(scores_data[header_data[:, 4] == '0'][:, 0],
bins=100,
range=hist_range)
center = (bins[:-1]+bins[1:]) / 2
ax.plot(center, patho_hist, 'r--', label='pathogenic variants')
ax.plot(center, neutr_hist, 'b--', label='neutral variants')
ax.set_title('CombiVEP score distributuion')
ax.set_ylabel('samples')
ax.set_xlabel('score')
ax.legend(bbox_to_anchor=(0.999, 0.999), loc=1)
ax = fig.add_subplot(212)
patho_hist, bins = hist(scores_data[header_data[:, 4] == '1'][:, 7],
bins=100,
range=hist_range)
neutr_hist, bins = hist(scores_data[header_data[:, 4] == '0'][:, 7],
bins=100,
range=hist_range)
center = (bins[:-1]+bins[1:])/2
ax.plot(center, patho_hist, 'r--', label='pathogenic variants')
ax.plot(center, neutr_hist, 'b--', label='neutral variants')
ax.set_title('Condel score distributuion')
ax.set_ylabel('samples')
ax.set_xlabel('score')
ax.legend(bbox_to_anchor=(0.999, 0.999), loc=1)
fig.tight_layout()
fig.savefig(dev_const.PUB_SCORES_DISTR_FIG,
bbox_inches='tight',
pad_inches=0.05)
return (dev_const.PUB_ROC_FIG,
dev_const.PUB_AUC_FIG,
dev_const.PUB_SCORES_DISTR_FIG,
)
