def test_five_signatures_perturbed(self):
weights = np.zeros((30, ))
weights[3] = .4
weights[7] = .2
weights[13] = .2
weights[17] = .1
weights[23] = .1
tumor_profile, context_counts = generate_tumor_profile(weights)
# Perturb the counts by up to 6% more or less of value
for k, v in context_counts.items():
context_counts[k] = v * (1 + (np.random.rand() * 0.12) - 0.06)
ds = DeconstructSigs(context_counts=context_counts)
# Limit to two deconstructed signatures
reconstructed_weights = ds.which_signatures(5)
self.assertAlmostEqual(reconstructed_weights[3], .4, places=1)
self.assertAlmostEqual(reconstructed_weights[7], .2, places=1)
self.assertAlmostEqual(reconstructed_weights[13], .2, places=1)
self.assertAlmostEqual(reconstructed_weights[17], .1, places=1)
self.assertAlmostEqual(reconstructed_weights[23], .1, places=1)
# Don't impose a signature limit to test that algorithm correctly deduces only five are necessary
reconstructed_weights = ds.which_signatures()
self.assertAlmostEqual(reconstructed_weights[3], .4, places=1)
self.assertAlmostEqual(reconstructed_weights[7], .2, places=1)
self.assertAlmostEqual(reconstructed_weights[13], .2, places=1)
self.assertAlmostEqual(reconstructed_weights[17], .1, places=1)
self.assertAlmostEqual(reconstructed_weights[23], .1, places=1)
def test_five_signatures_discard_insignificant(self):
weights = np.zeros((30, ))
weights[3] = .19
weights[7] = .41
weights[13] = .24
weights[17] = .14
weights[23] = .02
tumor_profile, context_counts = generate_tumor_profile(weights)
ds = DeconstructSigs(context_counts=context_counts)
# Limit to five deconstructed signatures
reconstructed_weights = ds.which_signatures(5)
self.assertAlmostEqual(reconstructed_weights[3], .19, places=2)
self.assertAlmostEqual(reconstructed_weights[7], .41, places=2)
self.assertAlmostEqual(reconstructed_weights[13], .24, places=2)
self.assertAlmostEqual(reconstructed_weights[17], .14, places=2)
self.assertAlmostEqual(reconstructed_weights[23], 0, places=2)
# Don't impose a signature limit to test that algorithm correctly deduces only five necessary and significant
reconstructed_weights = ds.which_signatures()
self.assertAlmostEqual(reconstructed_weights[3], .19, places=2)
self.assertAlmostEqual(reconstructed_weights[7], .41, places=2)
self.assertAlmostEqual(reconstructed_weights[13], .24, places=2)
self.assertAlmostEqual(reconstructed_weights[17], .14, places=2)
self.assertAlmostEqual(reconstructed_weights[23], 0, places=2)
def test_one_signature(self):
weights = np.zeros((30, ))
weights[0] = 1
tumor_profile, context_counts = generate_tumor_profile(weights)
ds = DeconstructSigs(context_counts=context_counts)
# Limit to one deconstructed signatures
reconstructed_weights = ds.which_signatures(1)
self.assertAlmostEqual(reconstructed_weights[0], weights[0], places=2)
# Don't impose a signature limit to test that algorithm correctly deduces only one are necessary
reconstructed_weights = ds.which_signatures()
self.assertAlmostEqual(reconstructed_weights[0], weights[0], places=2)
def test_three_signatures_with_associated(self):
weights = np.zeros((30, ))
weights[3] = .6
weights[11] = .25
weights[27] = .15
tumor_profile, context_counts = generate_tumor_profile(weights)
ds = DeconstructSigs(context_counts=context_counts)
# Limit to two deconstructed signatures
reconstructed_weights = ds.which_signatures(3, associated=[11, 27])
self.assertAlmostEqual(reconstructed_weights[3], 0, places=2)
# Don't impose a signature limit to test that algorithm correctly deduces only three are necessary
reconstructed_weights = ds.which_signatures(associated=[11, 27])
self.assertAlmostEqual(reconstructed_weights[3], 0, places=2)
def test_four_signatures(self):
weights = np.zeros((30, ))
weights[6] = .43
weights[10] = .27
weights[25] = .20
weights[29] = .1
tumor_profile, context_counts = generate_tumor_profile(weights)
ds = DeconstructSigs(context_counts=context_counts)
# Limit to four deconstructed signatures
reconstructed_weights = ds.which_signatures(4)
self.assertAlmostEqual(reconstructed_weights[6], .43, places=2)
self.assertAlmostEqual(reconstructed_weights[10], .27, places=2)
self.assertAlmostEqual(reconstructed_weights[25], .20, places=2)
self.assertAlmostEqual(reconstructed_weights[29], .1, places=2)
# Don't impose a signature limit to test that algorithm correctly deduces only fiour are necessary
reconstructed_weights = ds.which_signatures()
self.assertAlmostEqual(reconstructed_weights[6], .43, places=2)
self.assertAlmostEqual(reconstructed_weights[10], .27, places=2)
self.assertAlmostEqual(reconstructed_weights[25], .20, places=2)
self.assertAlmostEqual(reconstructed_weights[29], .1, places=2)