def test_logdet_pair_aa(self):
"""logdet shouldn't fail to produce distances for aa seqs"""
aln = load_aligned_seqs("data/brca1_5.paml", moltype=DNA)
aln = aln.get_translation()
logdet_calc = LogDetPair(moltype=PROTEIN, alignment=aln)
logdet_calc.run(use_tk_adjustment=True, show_progress=False)
dists = logdet_calc.get_pairwise_distances()
def test_logdet_variance(self):
"""calculate logdet variance consistent with hand calculation"""
data = [
(
"seq1",
"GGGGGGGGGGGCCCCCCCCCCCCCCCCCGGGGGGGGGGGGGGGCGGTTTTTTTTTTTTTTTTTT",
),
(
"seq2",
"TAAAAAAAAAAGGGGGGGGGGGGGGGGGGTTTTTTTTTTTTTTTTTTCCCCCCCCCCCCCCCCC",
),
]
aln = make_aligned_seqs(data=data, moltype=DNA)
logdet_calc = LogDetPair(moltype=DNA, alignment=aln)
logdet_calc.run(use_tk_adjustment=True, show_progress=False)
self.assertEqual(logdet_calc.variances[1, 1], None)
index = dict(list(zip("ACGT", list(range(4)))))
J = numpy.zeros((4, 4))
for p in zip(data[0][1], data[1][1]):
J[index[p[0]], index[p[1]]] += 1
for i in range(4):
if J[i, i] == 0:
J[i, i] += 0.5
J /= J.sum()
M = numpy.linalg.inv(J)
var = 0.0
for i in range(4):
for j in range(4):
var += M[j, i]**2 * J[i, j] - 1
var /= 16 * len(data[0][1])
logdet_calc.run(use_tk_adjustment=False, show_progress=False)
dists = logdet_calc.get_pairwise_distances()
assert_allclose(logdet_calc.variances[1, 1], var, atol=1e-3)
def test_logdet_pair_dna(self):
"""logdet should produce distances that match MEGA"""
aln = load_aligned_seqs("data/brca1_5.paml", moltype=DNA)
logdet_calc = LogDetPair(moltype=DNA, alignment=aln)
logdet_calc.run(use_tk_adjustment=True, show_progress=False)
dists = logdet_calc.get_pairwise_distances().to_dict()
all_expected = {
("Human", "NineBande"): 0.075336929999999996,
("NineBande", "DogFaced"): 0.0898575452,
("DogFaced", "Human"): 0.1061747919,
("HowlerMon", "DogFaced"): 0.0934480008,
("Mouse", "HowlerMon"): 0.26422862920000001,
("NineBande", "Human"): 0.075336929999999996,
("HowlerMon", "NineBande"): 0.062202897899999998,
("DogFaced", "NineBande"): 0.0898575452,
("DogFaced", "HowlerMon"): 0.0934480008,
("Human", "DogFaced"): 0.1061747919,
("Mouse", "Human"): 0.26539976700000001,
("NineBande", "HowlerMon"): 0.062202897899999998,
("HowlerMon", "Human"): 0.036571181899999999,
("DogFaced", "Mouse"): 0.2652555144,
("HowlerMon", "Mouse"): 0.26422862920000001,
("Mouse", "DogFaced"): 0.2652555144,
("NineBande", "Mouse"): 0.22754789210000001,
("Mouse", "NineBande"): 0.22754789210000001,
("Human", "Mouse"): 0.26539976700000001,
("Human", "HowlerMon"): 0.036571181899999999,
}
for pair in dists:
got = dists[pair]
expected = all_expected[pair]
assert_allclose(got, expected)
def test_logdet_tk_adjustment(self):
"""logdet using tamura kumar differs from classic"""
aln = load_aligned_seqs("data/brca1_5.paml", moltype=DNA)
logdet_calc = LogDetPair(moltype=DNA, alignment=aln)
logdet_calc.run(use_tk_adjustment=True, show_progress=False)
tk = logdet_calc.get_pairwise_distances()
logdet_calc.run(use_tk_adjustment=False, show_progress=False)
not_tk = logdet_calc.get_pairwise_distances()
self.assertNotEqual(tk, not_tk)
def test_paralinear_pair_dna(self):
"""calculate paralinear distance consistent with logdet distance"""
data = [
(
"seq1",
"TAATTCATTGGGACGTCGAATCCGGCAGTCCTGCCGCAAAAGCTTCCGGAATCGAATTTTGGCA",
),
(
"seq2",
"AAAAAAAAAAAAAAAACCCCCCCCCCCCCCCCTTTTTTTTTTTTTTTTGGGGGGGGGGGGGGGG",
),
]
aln = make_aligned_seqs(data=data, moltype=DNA)
paralinear_calc = ParalinearPair(moltype=DNA, alignment=aln)
paralinear_calc.run(show_progress=False)
logdet_calc = LogDetPair(moltype=DNA, alignment=aln)
logdet_calc.run(show_progress=False)
self.assertEqual(logdet_calc.dists[1, 1], paralinear_calc.dists[1, 1])
self.assertEqual(paralinear_calc.variances[1, 1], logdet_calc.variances[1, 1])
def test_logdet_missing_states(self):
"""should calculate logdet measurement with missing states"""
data = [
(
"seq1",
"GGGGGGGGGGGCCCCCCCCCCCCCCCCCGGGGGGGGGGGGGGGCGGTTTTTTTTTTTTTTTTTT",
),
(
"seq2",
"TAAAAAAAAAAGGGGGGGGGGGGGGGGGGTTTTTNTTTTTTTTTTTTCCCCCCCCCCCCCCCCC",
),
]
aln = make_aligned_seqs(data=data, moltype=DNA)
logdet_calc = LogDetPair(moltype=DNA, alignment=aln)
logdet_calc.run(use_tk_adjustment=True, show_progress=False)
dists = logdet_calc.get_pairwise_distances().to_dict()
self.assertTrue(list(dists.values())[0] is not None)
logdet_calc.run(use_tk_adjustment=False, show_progress=False)
dists = logdet_calc.get_pairwise_distances().to_dict()
self.assertTrue(list(dists.values())[0] is not None)
def test_logdet_for_determinant_lte_zero(self):
"""returns distance of None if the determinant is <= 0"""
data = dict(
seq1="AGGGGGGGGGGCCCCCCCCCCCCCCCCCGGGGGGGGGGGGGGGCGGTTTTTTTTTTTTTTTTTT",
seq2="TAAAAAAAAAAGGGGGGGGGGGGGGGGGGTTTTTTTTTTTTTTTTTTCCCCCCCCCCCCCCCCC",
)
aln = make_aligned_seqs(data=data, moltype=DNA)
logdet_calc = LogDetPair(moltype=DNA, alignment=aln)
logdet_calc.run(use_tk_adjustment=True, show_progress=False)
dists = logdet_calc.get_pairwise_distances().to_dict()
self.assertTrue(numpy.isnan(list(dists.values())[0]))
logdet_calc.run(use_tk_adjustment=False, show_progress=False)
dists = logdet_calc.get_pairwise_distances().to_dict()
self.assertTrue(numpy.isnan(list(dists.values())[0]))
# but raises ArithmeticError if told to
logdet_calc = LogDetPair(moltype=DNA, alignment=aln, invalid_raises=True)
with self.assertRaises(ArithmeticError):
logdet_calc.run(use_tk_adjustment=True, show_progress=False)