def test_probabilisticQuotientNormaliser(self):
"""
Check that the ProbabilisticQuotientNormaliser
"""
X = numpy.copy(self.X)
reference = numpy.nanmedian(X, axis=0)
pqn_norm = ProbabilisticQuotientNormaliser()
fold_change_matrix = X / reference
pqn_norm_coefs = numpy.absolute(
numpy.median(fold_change_matrix, axis=1))
pqn_normed = X / pqn_norm_coefs[:, None]
numpy.testing.assert_array_almost_equal(
pqn_normed,
pqn_norm.normalise(self.X),
err_msg=
"PQN normaliser not working correctly - mismatching normalised data"
)
numpy.testing.assert_array_almost_equal(
pqn_norm_coefs,
pqn_norm.normalisation_coefficients,
err_msg=
"PQN normaliser not working correctly - non-matching PQN coefficients"
)
numpy.testing.assert_array_equal(reference, pqn_norm.reference)
def test_delete_reference(self):
pqn_norm = ProbabilisticQuotientNormaliser()
pqn_norm.normalise(self.X)
del pqn_norm.reference
self.assertIsNone(pqn_norm.normalisation_coefficients)
def test_probabilisticQuotientNormaliser_nans(self):
##
# Check we dont crash with NaNs
##
self.X[0, 0] = numpy.nan
pqn_norm = ProbabilisticQuotientNormaliser()
pqn_norm.normalise(self.X)
def test_raises(self):
pqn_norm = ProbabilisticQuotientNormaliser()
with self.subTest(msg='1D X matrix'):
X = numpy.array([2])
self.assertRaises(ValueError, pqn_norm.normalise, X)
with self.subTest(msg='3D X matrix'):
X = numpy.array([2, 2, 2])
self.assertRaises(ValueError, pqn_norm.normalise, X)
with self.subTest(msg='Reference wrong size'):
X = numpy.array([5, 5])
pqn_norm.reference = numpy.array([4])
self.assertRaises(ValueError, pqn_norm.normalise, X)
def test_repr(self):
with self.subTest(msg='Default reference profile'):
pqn_norm = ProbabilisticQuotientNormaliser()
strform = str(pqn_norm)
self.assertEqual(
strform,
'Normalised to median fold-change, reference profile was the median profile.'
)
def test_pass_reference(self):
X = numpy.copy(self.X)
reference = numpy.abs(numpy.random.randn(self.noFeat))
pqn_norm = ProbabilisticQuotientNormaliser(reference=reference)
pqn_norm.normalise(X)
featureMask = numpy.logical_and(numpy.isfinite(reference),
reference != 0)
fold_change_matrix = X[:, featureMask] / reference[featureMask]
fold_change_matrix[fold_change_matrix == 0] = numpy.nan
pqn_norm_coefs = numpy.absolute(
numpy.median(fold_change_matrix, axis=1))
# Set 0 cofficients to 1
pqn_norm_coefs[pqn_norm_coefs == 0] = 1
numpy.testing.assert_array_almost_equal(
pqn_norm_coefs,
pqn_norm.normalisation_coefficients,
err_msg="Change of reference does not work")
def test_nullNormaliser_eq_(self):
"""
Check that the NullNormaliser equality testing works
"""
with self.subTest():
norm = NullNormaliser()
norm2 = NullNormaliser()
self.assertEqual(norm, norm2)
pqn = ProbabilisticQuotientNormaliser()
tanorm = TotalAreaNormaliser(keepMagnitude=False)
tanorm2 = TotalAreaNormaliser(keepMagnitude=True)
notEqualList = [
1, True, 'str', 1.1,
list(), dict(), tanorm, tanorm2, pqn
]
norm = NullNormaliser()
for comparison in notEqualList:
with self.subTest(msg=comparison):
self.assertNotEqual(norm, comparison)
def test_totalAreaNormaliser_eq_(self):
"""
Check that the TotalAreaNormaliser equality testing works
"""
with self.subTest(msg='Test keepMagnitude is respected'):
tanorm = TotalAreaNormaliser(keepMagnitude=False)
tanorm2 = TotalAreaNormaliser(keepMagnitude=False)
tanorm3 = TotalAreaNormaliser(keepMagnitude=True)
tanorm4 = TotalAreaNormaliser(keepMagnitude=True)
self.assertEqual(tanorm, tanorm2)
self.assertEqual(tanorm3, tanorm3)
self.assertNotEqual(tanorm, tanorm3)
pqn = ProbabilisticQuotientNormaliser()
notEqualList = [
1, True, 'str', 1.1,
list(), dict(),
NullNormaliser(), pqn
]
tanorm = TotalAreaNormaliser()
for comparison in notEqualList:
with self.subTest(msg=comparison):
self.assertNotEqual(tanorm, comparison)
def test_nans(self):
self.X[0, 0] = numpy.nan
pqn_norm = ProbabilisticQuotientNormaliser()
pqn_norm.normalise(self.X)