def test_filter_aa(self):
D = GlobalDescriptor(
['GLFDIVKKVVGALG', 'LLLLLL', 'KKKKKKKKKK', 'DDDDDDDDDDDD'])
D.calculate_charge()
D.filter_aa(['D'])
self.assertEqual(D.sequences, ['LLLLLL', 'KKKKKKKKKK'])
self.assertEqual(len(D.descriptor), 2)
def analyze_generated(self, num, fname='analysis.txt', plot=False):
""" Method to analyze the generated sequences located in `self.generated`.
:param num: {int} wanted number of sequences to sample
:param fname: {str} filename to save analysis info to
:param plot: {bool} whether to plot an overview of descriptors
:return: file with analysis info (distances)
"""
with open(fname, 'w') as f:
print("Analyzing...")
f.write("ANALYSIS OF SAMPLED SEQUENCES\n==============================\n\n")
f.write("Nr. of duplicates in generated sequences: %i\n" % (len(self.generated) - len(set(self.generated))))
count = len(set(self.generated) & set(self.sequences)) # get shared entries in both lists
f.write("%.1f percent of generated sequences are present in the training data.\n" %
((count / len(self.generated)) * 100))
d = GlobalDescriptor(self.generated)
len1 = len(d.sequences)
d.filter_aa('B')
len2 = len(d.sequences)
d.length()
f.write("\n\nLENGTH DISTRIBUTION OF GENERATED DATA:\n\n")
f.write("Number of sequences too short:\t%i\n" % (num - len1))
f.write("Number of invalid (with 'B'):\t%i\n" % (len1 - len2))
f.write("Number of valid unique seqs:\t%i\n" % len2)
f.write("Mean sequence length: \t\t%.1f ± %.1f\n" % (np.mean(d.descriptor), np.std(d.descriptor)))
f.write("Median sequence length: \t\t%i\n" % np.median(d.descriptor))
f.write("Minimal sequence length: \t\t%i\n" % np.min(d.descriptor))
f.write("Maximal sequence length: \t\t%i\n" % np.max(d.descriptor))
descriptor = 'pepcats'
seq_desc = PeptideDescriptor([s[1:].rstrip() for s in self.sequences], descriptor)
seq_desc.calculate_autocorr(7)
gen_desc = PeptideDescriptor(d.sequences, descriptor)
gen_desc.calculate_autocorr(7)
# random comparison set
self.ran = Random(len(self.generated), np.min(d.descriptor), np.max(d.descriptor)) # generate rand seqs
probas = count_aas(''.join(seq_desc.sequences)).values() # get the aa distribution of training seqs
self.ran.generate_sequences(proba=probas)
ran_desc = PeptideDescriptor(self.ran.sequences, descriptor)
ran_desc.calculate_autocorr(7)
# amphipathic helices comparison set
self.hel = Helices(len(self.generated), np.min(d.descriptor), np.max(d.descriptor))
self.hel.generate_sequences()
hel_desc = PeptideDescriptor(self.hel.sequences, descriptor)
hel_desc.calculate_autocorr(7)
# distance calculation
f.write("\n\nDISTANCE CALCULATION IN '%s' DESCRIPTOR SPACE\n\n" % descriptor.upper())
desc_dist = distance.cdist(gen_desc.descriptor, seq_desc.descriptor, metric='euclidean')
f.write("Average euclidean distance of sampled to training data:\t%.3f +/- %.3f\n" %
(np.mean(desc_dist), np.std(desc_dist)))
ran_dist = distance.cdist(ran_desc.descriptor, seq_desc.descriptor, metric='euclidean')
f.write("Average euclidean distance if randomly sampled seqs:\t%.3f +/- %.3f\n" %
(np.mean(ran_dist), np.std(ran_dist)))
hel_dist = distance.cdist(hel_desc.descriptor, seq_desc.descriptor, metric='euclidean')
f.write("Average euclidean distance if amphipathic helical seqs:\t%.3f +/- %.3f\n" %
(np.mean(hel_dist), np.std(hel_dist)))
# more simple descriptors
g_seq = GlobalDescriptor(seq_desc.sequences)
g_gen = GlobalDescriptor(gen_desc.sequences)
g_ran = GlobalDescriptor(ran_desc.sequences)
g_hel = GlobalDescriptor(hel_desc.sequences)
g_seq.calculate_all()
g_gen.calculate_all()
g_ran.calculate_all()
g_hel.calculate_all()
sclr = StandardScaler()
sclr.fit(g_seq.descriptor)
f.write("\n\nDISTANCE CALCULATION FOR SCALED GLOBAL DESCRIPTORS\n\n")
desc_dist = distance.cdist(sclr.transform(g_gen.descriptor), sclr.transform(g_seq.descriptor),
metric='euclidean')
f.write("Average euclidean distance of sampled to training data:\t%.2f +/- %.2f\n" %
(np.mean(desc_dist), np.std(desc_dist)))
ran_dist = distance.cdist(sclr.transform(g_ran.descriptor), sclr.transform(g_seq.descriptor),
metric='euclidean')
f.write("Average euclidean distance if randomly sampled seqs:\t%.2f +/- %.2f\n" %
(np.mean(ran_dist), np.std(ran_dist)))
hel_dist = distance.cdist(sclr.transform(g_hel.descriptor), sclr.transform(g_seq.descriptor),
metric='euclidean')
f.write("Average euclidean distance if amphipathic helical seqs:\t%.2f +/- %.2f\n" %
(np.mean(hel_dist), np.std(hel_dist)))
# hydrophobic moments
uh_seq = PeptideDescriptor(seq_desc.sequences, 'eisenberg')
uh_seq.calculate_moment()
uh_gen = PeptideDescriptor(gen_desc.sequences, 'eisenberg')
uh_gen.calculate_moment()
uh_ran = PeptideDescriptor(ran_desc.sequences, 'eisenberg')
uh_ran.calculate_moment()
uh_hel = PeptideDescriptor(hel_desc.sequences, 'eisenberg')
uh_hel.calculate_moment()
f.write("\n\nHYDROPHOBIC MOMENTS\n\n")
f.write("Hydrophobic moment of training seqs:\t%.3f +/- %.3f\n" %
(np.mean(uh_seq.descriptor), np.std(uh_seq.descriptor)))
f.write("Hydrophobic moment of sampled seqs:\t\t%.3f +/- %.3f\n" %
(np.mean(uh_gen.descriptor), np.std(uh_gen.descriptor)))
f.write("Hydrophobic moment of random seqs:\t\t%.3f +/- %.3f\n" %
(np.mean(uh_ran.descriptor), np.std(uh_ran.descriptor)))
f.write("Hydrophobic moment of amphipathic seqs:\t%.3f +/- %.3f\n" %
(np.mean(uh_hel.descriptor), np.std(uh_hel.descriptor)))
if plot:
if self.refs:
a = GlobalAnalysis([uh_seq.sequences, uh_gen.sequences, uh_hel.sequences, uh_ran.sequences],
['training', 'sampled', 'hel', 'ran'])
else:
a = GlobalAnalysis([uh_seq.sequences, uh_gen.sequences], ['training', 'sampled'])
a.plot_summary(filename=fname[:-4] + '.png')