def test_paralinear_variance(self):
"""calculate paralinear variance consistent with hand calculation"""
data = [
(
"seq1",
"GGGGGGGGGGGCCCCCCCCCCCCCCCCCGGGGGGGGGGGGGGGCGGTTTTTTTTTTTTTTTTTT",
),
(
"seq2",
"TAAAAAAAAAAGGGGGGGGGGGGGGGGGGTTTTTTTTTTTTTTTTTTCCCCCCCCCCCCCCCCC",
),
]
aln = make_aligned_seqs(data=data, moltype=DNA)
paralinear_calc = ParalinearPair(moltype=DNA, alignment=aln)
paralinear_calc.run(show_progress=False)
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)
f = J.sum(1), J.sum(0)
var = 0.0
for i in range(4):
for j in range(4):
var += M[j, i]**2 * J[i, j]
var -= 1 / numpy.sqrt(f[0][i] * f[1][i])
var /= 16 * len(data[0][1])
assert_allclose(paralinear_calc.variances[1, 1], var, atol=1e-3)
def test_paralinear_distance(self):
"""calculate paralinear variance consistent with hand calculation"""
data = [
(
"seq1",
"GGGGGGGGGGGCCCCCCCCCCCCCCCCCGGGGGGGGGGGGGGGCGGTTTTTTTTTTTTTTTTTT",
),
(
"seq2",
"TAAAAAAAAAAGGGGGGGGGGGGGGGGGGTTTTTTTTTTTTTTTTTTCCCCCCCCCCCCCCCCC",
),
]
aln = make_aligned_seqs(data=data, moltype=DNA)
paralinear_calc = ParalinearPair(moltype=DNA, alignment=aln)
paralinear_calc.run(show_progress=False)
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)
f = J.sum(1), J.sum(0)
dist = -0.25 * numpy.log(
numpy.linalg.det(J) / numpy.sqrt(f[0].prod() * f[1].prod()))
assert_allclose(paralinear_calc.dists["seq1", "seq2"], dist)
def test_paralinear_pair_aa(self):
"""paralinear shouldn't fail to produce distances for aa seqs"""
aln = load_aligned_seqs("data/brca1_5.paml", moltype=DNA)
aln = aln.get_translation()
paralinear_calc = ParalinearPair(moltype=PROTEIN, alignment=aln)
paralinear_calc.run(show_progress=False)
dists = paralinear_calc.get_pairwise_distances()
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_paralinear_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)
paralinear_calc = ParalinearPair(moltype=DNA, alignment=aln)
paralinear_calc.run(show_progress=False)
dists = paralinear_calc.get_pairwise_distances().to_dict()
self.assertTrue(numpy.isnan(list(dists.values())[0]))
paralinear_calc.run(show_progress=False)
dists = paralinear_calc.get_pairwise_distances().to_dict()
self.assertTrue(numpy.isnan(list(dists.values())[0]))
def get_calc(data):
aln = make_aligned_seqs(data=data, moltype=DNA)
calc = ParalinearPair(moltype=DNA, alignment=aln)
calc(show_progress=False)
return calc
