def test_dfe_errors(self):
m1 = stdpopsim.MutationType()
m2 = stdpopsim.MutationType()
for bad_props in [["abc"], 1.0, [1.0], [0.2, 0.4, 0.4], [-0.1, -0.1]]:
with pytest.raises(ValueError):
_ = stdpopsim.DFE(
id="abc",
description="test test",
long_description="test test test test",
proportions=bad_props,
mutation_types=[m1, m2],
)
for bad_mut_types in ["abc", {}, [1.0, 2.0], [m1], m1, ["a", "b"]]:
with pytest.raises(ValueError):
_ = stdpopsim.DFE(
id="abc",
description="test test",
long_description="test test test test",
proportions=[0.6, 0.4],
mutation_types=bad_mut_types,
)
for bad_sums in [[-0.4, 0.5], [0.6, 0.8], [139487135987, 0.0],
[0.2, 0.3]]:
print(bad_sums)
with pytest.raises(ValueError):
_ = stdpopsim.DFE(
id="abc",
description="test test",
long_description="test test test test",
proportions=bad_sums,
mutation_types=[m1, m2],
)
def test_is_neutral(self):
for neutral in (True, False):
for dist in ("f", "e"):
contig = stdpopsim.Contig.basic_contig(length=100)
contig.clear_dfes()
props = [0.3, 0.7]
if neutral:
s = 0
else:
s = 0.1
mt = [
stdpopsim.MutationType(distribution_type=dist,
distribution_args=[s])
for _ in props
]
dfes = [
stdpopsim.DFE(
id=str(j),
description="test",
long_description="test test",
proportions=props,
mutation_types=mt,
) for j in range(2)
]
contig.add_dfe(
intervals=np.array([[10, 30], [50, 100]]),
DFE=dfes[0],
)
contig.add_dfe(intervals=np.array([[30, 40]]), DFE=dfes[1])
# exponential with mean zero doesn't count as neutral!
assert contig.is_neutral is (neutral and dist == "f")
def test_no_msprime_DFE(self):
# test we cannot simulate a non-neutral DFE with msprime
m1 = stdpopsim.ext.MutationType(
dominance_coeff=0.2,
distribution_type="e",
distribution_args=[0.1],
)
desc = "test test"
long_desc = "test test 🐢"
dfe = stdpopsim.DFE(
id="abc",
description=desc,
long_description=long_desc,
mutation_types=[m1],
)
contig = stdpopsim.Contig.basic_contig(
length=10000,
mutation_rate=1e-6,
)
contig.clear_genomic_mutation_types()
contig.add_DFE(
intervals=np.array([[0, contig.length / 2]], dtype="int"),
DFE=dfe,
)
model = stdpopsim.PiecewiseConstantSize(1000)
samples = model.get_samples(2)
engine = stdpopsim.get_engine("msprime")
with pytest.raises(ValueError):
_ = engine.simulate(
model,
contig,
samples,
)
def _KimDFE():
id = "Gamma_K17"
description = "Deleterious Gamma DFE"
long_description = """
Return neutral and negative MutationType()s representing a human DFE.
Kim et al. (2017), https://doi.org/10.1534/genetics.116.197145
"""
citations = [
stdpopsim.Citation(
author="Kim et al.",
year=2017,
doi="https://doi.org/10.1534/genetics.116.197145",
reasons="to be defined", # include the dfe_model reason
)
]
neutral = stdpopsim.ext.MutationType()
gamma_shape = 0.186 # shape
gamma_mean = -0.01314833 # expected value
h = 0.5 # dominance coefficient
negative = stdpopsim.ext.MutationType(
dominance_coeff=h,
distribution_type="g", # gamma distribution
distribution_args=[gamma_mean, gamma_shape],
)
return stdpopsim.DFE(
id=id,
description=description,
long_description=long_description,
mutation_types=[neutral, negative],
proportions=[0.3, 0.7],
citations=citations,
)
def test_too_many_dfes(self):
contig = stdpopsim.Contig.basic_contig(length=100)
contig.clear_DFEs()
props = [0.3, 0.7]
mt = [
stdpopsim.ext.MutationType(distribution_type="e",
distribution_args=[1]) for _ in props
]
dfes = [
stdpopsim.DFE(
id=str(j),
description="test",
long_description="test test",
proportions=props,
mutation_types=mt,
) for j in range(3)
]
contig.add_DFE(intervals=np.array([[10, 30], [50, 100]]),
DFE=dfes[0],
fill_neutral=False)
contig.add_DFE(intervals=np.array([[30, 40]]),
DFE=dfes[1],
fill_neutral=False)
with pytest.raises(ValueError):
contig.add_DFE(intervals=np.array([[30, 40]]),
DFE=dfes[2],
fill_neutral=False)
def test_DFE_defaults(self):
m1 = stdpopsim.ext.MutationType()
desc = "test test"
long_desc = "test test 🐢"
dfe = stdpopsim.DFE(
id="abc",
description=desc,
long_description=long_desc,
mutation_types=[m1],
proportions=[],
)
assert isinstance(dfe.proportions, list)
assert len(dfe.proportions) == 1
assert dfe.proportions[0] == 1
def test_add_DFE_errors(self):
contig = stdpopsim.Contig.basic_contig(length=100)
props = [0.3, 0.7]
mt = [stdpopsim.ext.MutationType() for _ in props]
# bad intervals
dfe = stdpopsim.DFE(
id="abc",
description="test",
long_description="test test",
proportions=props,
mutation_types=mt,
)
with pytest.raises(ValueError):
contig.add_DFE(np.array([10, 20]), dfe)
def test_non_neutral_contig(self):
species = stdpopsim.get_species("HomSap")
model = species.get_demographic_model("AshkSub_7G19")
samples = model.get_samples(10)
contig = stdpopsim.Contig.basic_contig(length=100)
contig.clear_DFEs()
props = [1]
mt = [stdpopsim.ext.MutationType(distribution_type="f", distribution_args=[1])]
dfes = [
stdpopsim.DFE(
id=str(0),
description="test",
long_description="test test",
proportions=props,
mutation_types=mt,
)
]
contig.add_DFE(intervals=np.array([[0, 50]]), DFE=dfes[0])
engine = stdpopsim.get_engine("msprime")
with pytest.raises(ValueError):
engine.simulate(model, contig, samples, seed=1)
# okay now change selection coefficient to neutral
contig.clear_DFEs()
props = [1]
mt = [stdpopsim.ext.MutationType(distribution_type="f", distribution_args=[0])]
dfes = [
stdpopsim.DFE(
id=str(0),
description="test",
long_description="test test",
proportions=props,
mutation_types=mt,
)
]
contig.add_DFE(intervals=np.array([[0, 50]]), DFE=dfes[0])
engine = stdpopsim.get_engine("msprime")
engine.simulate(model, contig, samples, seed=1)
def test_printing_DFE(self, capsys):
m1 = stdpopsim.ext.MutationType()
desc = "test test"
long_desc = "test test 🐢"
dfe = stdpopsim.DFE(
id="abc",
description=desc,
long_description=long_desc,
mutation_types=[m1],
)
print(dfe)
captured = capsys.readouterr()
assert "DFE:\n" in captured.out
assert "║" in captured.out
assert "║ id = abc\n" in captured.out
def test_basic_DFE(self):
desc = "test test"
long_desc = "test test 🐢"
for props in ([0.4, 0.6], [1.0, 0.0], [1.0], [1 / 3, 1 / 3, 1 / 3]):
mt = [stdpopsim.ext.MutationType() for _ in props]
dfe = stdpopsim.DFE(
id="abc",
description=desc,
long_description=long_desc,
proportions=props,
mutation_types=mt,
)
assert dfe.id == "abc"
assert dfe.description == desc
assert dfe.long_description == long_desc
for a, b in zip(props, dfe.proportions):
assert a == b
for a, b in zip(mt, dfe.mutation_types):
assert a == b
def test_add_dfe(self):
for clear in (True, False):
contig = stdpopsim.Contig.basic_contig(length=100)
if clear:
contig.clear_dfes()
props = [0.3, 0.7]
mt = [stdpopsim.MutationType() for _ in props]
dfes = [
stdpopsim.DFE(
id=str(j),
description="test",
long_description="test test",
proportions=props,
mutation_types=mt,
) for j in range(3)
]
contig.add_dfe(
intervals=np.array([[10, 30], [50, 100]]),
DFE=dfes[0],
)
contig.add_dfe(intervals=np.array([[30, 40]]), DFE=dfes[1])
contig.add_dfe(intervals=np.array([[20, 60]]), DFE=dfes[2])
assert len(contig.dfe_list) == 4 - clear
assert len(contig.mutation_types()) == 7 - clear
if clear:
dfe_ids = []
true_ints = []
else:
true_ints = [np.array([[0, 10]])]
dfe_ids = ["neutral"]
dfe_ids += [dfe.id for dfe in dfes]
true_ints += [
np.array([[10, 20], [60, 100]]),
np.empty((0, 2)),
np.array([[20, 60]]),
]
for d, i in zip(contig.dfe_list, dfe_ids):
assert d.id == i
for a1, a2 in zip(contig.interval_list, true_ints):
assert np.all(a1.shape == a2.shape)
assert np.all(a1 == a2)
self.verify_dfe_breakpoints(contig)
def test_dfe_breakpoints(self):
contig = stdpopsim.Contig.basic_contig(length=100)
contig.clear_dfes()
mt = stdpopsim.MutationType()
for j in range(3):
dfe = stdpopsim.DFE(
id=str(j),
description="test",
long_description="test test",
mutation_types=[mt],
)
contig.add_dfe(
np.array(
[[(j + 1) * 5,
(j + 1) * 10], [(j + 1) * 20, (j + 1) * 20 + 1]],
dtype="int",
),
dfe,
)
self.verify_dfe_breakpoints(contig)
def test_is_neutral2(self):
contig = stdpopsim.Contig.basic_contig(length=100)
contig.clear_DFEs()
props = [0.3, 0.7]
mt = [stdpopsim.ext.MutationType() for _ in props]
dfes = [
stdpopsim.DFE(
id=str(j),
description="test",
long_description="test test",
proportions=props,
mutation_types=mt,
) for j in range(2)
]
contig.add_DFE(intervals=np.array([[10, 30], [50, 100]]),
DFE=dfes[0],
fill_neutral=False)
contig.add_DFE(intervals=np.array([[30, 40]]),
DFE=dfes[1],
fill_neutral=False)
assert contig.is_neutral
def test_dfe_is_neutral(self):
for neutral in (True, False):
for dist in ("f", "e"):
props = [0.3, 0.7]
if neutral:
svals = [0.0, 0.0]
else:
svals = [0.0, 0.1]
mt = [
stdpopsim.MutationType(distribution_type=dist,
distribution_args=[s])
for s in svals
]
dfe = stdpopsim.DFE(
id=0,
description="test",
long_description="test test",
proportions=props,
mutation_types=mt,
)
assert dfe.is_neutral is (neutral and dist == "f")
def _HuberDFE():
id = "Gamma_H17"
description = "Deleterious Gamma DFE"
long_description = """
Return neutral and negative MutationType()s representing a H**o sapiens DFE.
Huber et al. (2017), https://doi.org/10.1073/pnas.1619508114.
DFE parameters are based on the Full Model described in Table S2, in which
All Data (none of the listed filters) were used.
""" # [this was a different filtering scheme than the Huber et al DroMel DFE ]
citations = [
stdpopsim.Citation(
author="Huber et al.",
year=2017,
doi="https://doi.org/10.1073/pnas.1619508114",
reasons="to be defined", # include the dfe_model reason
)
]
neutral = stdpopsim.MutationType()
gamma_shape = 0.19 # shape
gamma_mean = -0.014 # expected value
h = 0.5 # dominance coefficient
negative = stdpopsim.MutationType(
dominance_coeff=h,
distribution_type="g", # gamma distribution
distribution_args=[gamma_mean, gamma_shape],
)
return stdpopsim.DFE(
id=id,
description=description,
long_description=long_description,
mutation_types=[neutral, negative],
proportions=[
0.3,
0.7,
], # [0.3 and 0.7 were used in Xin's analysis,
# but I couldn't find these values in the Huber paper]
citations=citations,
)
def _HuberDFE():
id = "Gamma_H17"
description = "Deleterious Gamma DFE"
long_description = """
Return neutral and negative MutationType()s representing a drosophila DFE.
Huber et al. (2017), https://doi.org/10.1073/pnas.1619508114.
DFE parameters are based on the Full model described in Table S2, in which
singletons are excluded and a recent mutation rate estimate is used
(mu=3x10e-9, Keightley 2014).
"""
citations = [
stdpopsim.Citation(
author="Huber et al.",
year=2017,
doi="https://doi.org/10.1073/pnas.1619508114",
reasons="to be defined", # include the dfe_model reason
)
]
neutral = stdpopsim.ext.MutationType()
gamma_shape = 0.33 # shape
gamma_mean = -3.96e-04 # expected value
h = 0.5 # dominance coefficient
negative = stdpopsim.ext.MutationType(
dominance_coeff=h,
distribution_type="g", # gamma distribution
distribution_args=[gamma_mean, gamma_shape],
)
return stdpopsim.DFE(
id=id,
description=description,
long_description=long_description,
mutation_types=[neutral, negative],
proportions=[0.26, 0.74], # LNS = 2.85 x LS
citations=citations,
)
class TestContig(object):
example_dfe = stdpopsim.DFE(
id="abc",
description="example DFE",
long_description="test test beep boop beep",
proportions=[0.3, 0.7],
mutation_types=[stdpopsim.MutationType() for _ in range(2)],
)
def verify_dfe_breakpoints(self, contig):
breaks, dfe_labels = contig.dfe_breakpoints()
for j, intervals in enumerate(contig.interval_list):
for left, right in intervals:
assert left in breaks
assert right in breaks
k = np.searchsorted(breaks, left)
assert dfe_labels[k] == j
def test_default_dfe(self):
contig = stdpopsim.Contig.basic_contig(length=100)
assert len(contig.dfe_list) == 1
assert len(contig.interval_list) == 1
assert contig.dfe_list[0].id == "neutral"
assert np.all(
contig.interval_list[0] == np.array([[0, contig.length]]))
def test_add_dfe_errors(self):
contig = stdpopsim.Contig.basic_contig(length=100)
# bad intervals
with pytest.raises(ValueError):
contig.add_dfe(np.array([10, 20]), self.example_dfe)
with pytest.raises(ValueError):
contig.add_dfe("abc", self.example_dfe)
def test_dfe_breakpoints(self):
contig = stdpopsim.Contig.basic_contig(length=100)
contig.clear_dfes()
mt = stdpopsim.MutationType()
for j in range(3):
dfe = stdpopsim.DFE(
id=str(j),
description="test",
long_description="test test",
mutation_types=[mt],
)
contig.add_dfe(
np.array(
[[(j + 1) * 5,
(j + 1) * 10], [(j + 1) * 20, (j + 1) * 20 + 1]],
dtype="int",
),
dfe,
)
self.verify_dfe_breakpoints(contig)
def test_add_dfe(self):
for clear in (True, False):
contig = stdpopsim.Contig.basic_contig(length=100)
if clear:
contig.clear_dfes()
props = [0.3, 0.7]
mt = [stdpopsim.MutationType() for _ in props]
dfes = [
stdpopsim.DFE(
id=str(j),
description="test",
long_description="test test",
proportions=props,
mutation_types=mt,
) for j in range(3)
]
contig.add_dfe(
intervals=np.array([[10, 30], [50, 100]]),
DFE=dfes[0],
)
contig.add_dfe(intervals=np.array([[30, 40]]), DFE=dfes[1])
contig.add_dfe(intervals=np.array([[20, 60]]), DFE=dfes[2])
assert len(contig.dfe_list) == 4 - clear
assert len(contig.mutation_types()) == 7 - clear
if clear:
dfe_ids = []
true_ints = []
else:
true_ints = [np.array([[0, 10]])]
dfe_ids = ["neutral"]
dfe_ids += [dfe.id for dfe in dfes]
true_ints += [
np.array([[10, 20], [60, 100]]),
np.empty((0, 2)),
np.array([[20, 60]]),
]
for d, i in zip(contig.dfe_list, dfe_ids):
assert d.id == i
for a1, a2 in zip(contig.interval_list, true_ints):
assert np.all(a1.shape == a2.shape)
assert np.all(a1 == a2)
self.verify_dfe_breakpoints(contig)
@pytest.mark.skip(reason="TODO allow more flexible inputs")
def test_add_dfe_interval_formats(self):
L = 50818
for intervals in (
[[0, L]],
[[0, int(0.2 * L)]],
([0, int(0.2 * L)], [int(0.6 * L), L]),
):
contig = stdpopsim.Contig.basic_contig(length=L)
contig.add_dfe(intervals=intervals, DFE=self.example_dfe)
np.testing.assert_array_equal(intervals, contig.interval_list[1])
def test_is_neutral(self):
for neutral in (True, False):
for dist in ("f", "e"):
contig = stdpopsim.Contig.basic_contig(length=100)
contig.clear_dfes()
props = [0.3, 0.7]
if neutral:
s = 0
else:
s = 0.1
mt = [
stdpopsim.MutationType(distribution_type=dist,
distribution_args=[s])
for _ in props
]
dfes = [
stdpopsim.DFE(
id=str(j),
description="test",
long_description="test test",
proportions=props,
mutation_types=mt,
) for j in range(2)
]
contig.add_dfe(
intervals=np.array([[10, 30], [50, 100]]),
DFE=dfes[0],
)
contig.add_dfe(intervals=np.array([[30, 40]]), DFE=dfes[1])
# exponential with mean zero doesn't count as neutral!
assert contig.is_neutral is (neutral and dist == "f")
