def test_auto_dom_extended():
fam = make_fam2()
efam = EvalFamily(fam)
assert [f.affected for f in efam.subjects] == [False, True, True, False, True, False]
efam.gt_types = [Family.HOM_REF, Family.HET, Family.HET, Family.HOM_REF, Family.HET, Family.HOM_REF]
assert efam.auto_dom()
# unaffected is het:
efam.gt_types[0] = Family.HET
assert not efam.auto_dom()
assert efam.auto_dom(only_affected=False)
efam.gt_types[0] = Family.HOM_REF
assert efam.auto_dom()
efam.gt_depths = [9] * 6
assert not efam.auto_dom(min_depth=10)
# check with no unaffecteds
for f in efam.subjects:
f.affected = True
assert not efam.auto_dom()
# check with all unaffecteds
for f in efam.subjects:
f.affected = False
assert not efam.auto_dom()
def test_auto_dom_extended():
fam = make_fam2()
efam = EvalFamily(fam)
assert [f.affected
for f in efam.subjects] == [False, True, True, False, True, False]
efam.gt_types = [
Family.HOM_REF, Family.HET, Family.HET, Family.HOM_REF, Family.HET,
Family.HOM_REF
]
assert efam.auto_dom()
# unaffected is het:
efam.gt_types[0] = Family.HET
assert not efam.auto_dom()
assert efam.auto_dom(only_affected=False)
efam.gt_types[0] = Family.HOM_REF
assert efam.auto_dom()
efam.gt_depths = [9] * 6
assert not efam.auto_dom(min_depth=10)
# check with no unaffecteds
for f in efam.subjects:
f.affected = True
assert not efam.auto_dom()
# check with all unaffecteds
for f in efam.subjects:
f.affected = False
assert not efam.auto_dom()
def make_fam(n_affecteds, n_unaffecteds, n_unknowns, id="xxx"):
samples = []
for i in range(n_affecteds):
samples.append(
Sample('affected_%d' % i,
affected=True,
sex=random.randint(1, 2),
name='affected_%d' % i))
for i in range(n_unaffecteds):
samples.append(
Sample('unaffected_%d' % i,
affected=False,
sex=random.randint(1, 2),
name='affected_%d' % i))
for i in range(n_unknowns):
samples.append(
Sample('unknown_%d' % i,
affected=None,
sex=random.randint(1, 2),
name='affected_%d' % i))
for i in range(int((n_affecteds + n_affecteds + n_unknowns) / 2)):
sample = random.choice(samples)
if random.random() < 0.9:
try:
sample.dad = random.choice([
s for s in samples if not s == sample and s.sex == 'male'
])
except IndexError:
pass
if random.random() < 0.9:
try:
sample.mom = random.choice([
s for s in samples if not s == sample and s.sex == 'female'
])
except IndexError:
pass
fam = EvalFamily(Family(samples, 'fam_%s' % id))
fam.gt_types = [random.randrange(0, 4) for _ in range(len(samples))]
fam.gt_depths = [random.randrange(0, 100) for _ in range(len(samples))]
fam.gt_phred_ll_homref = [
random.randrange(0, 100) for _ in range(len(samples))
]
fam.gt_phred_ll_het = [
random.randrange(0, 100) for _ in range(len(samples))
]
fam.gt_phred_ll_homalt = [
random.randrange(0, 100) for _ in range(len(samples))
]
fam.gt_quals = [random.randrange(5, 100) for _ in range(len(samples))]
return fam
def test_auto_rec_extended():
fam = make_fam1()
efam = EvalFamily(fam)
efam.gt_types = [
Family.HET, Family.HET, Family.HOM_ALT, Family.HET, Family.HET,
Family.HET
]
assert efam.auto_rec()
#if grandpa is affected it is no longer autosomal recessive
efam.subjects[5].affected = True
assert not efam.auto_rec()
efam.subjects[5].affected = False
# set both kids to hom_alt:
efam.gt_types[3] = Family.HOM_ALT
assert not efam.auto_rec()
assert efam.auto_rec(only_affected=False)
# set unaffected kid back:
efam.gt_types[3] = Family.HET
# expected that is is auto_rec
assert efam.auto_rec()
# but it's not if we set all depths to 9 and have min_depth of 10
efam.gt_depths = [9] * 6
assert not efam.auto_rec(min_depth=10)
efam.gt_depths[2] = 1000
# even if affected has high depth
assert not efam.auto_rec(min_depth=10)
# passes if we meet the minimum depth
efam.gt_depths = [100] * 6
assert efam.auto_rec(min_depth=10)
# check if we have no affecteds:
efam.subjects[2].affected = False
assert not efam.auto_rec(min_depth=10)
def test_auto_rec_extended():
fam = make_fam1()
efam = EvalFamily(fam)
efam.gt_types = [Family.HET, Family.HET, Family.HOM_ALT, Family.HET, Family.HET, Family.HET]
assert efam.auto_rec()
#if grandpa is affected it is no longer autosomal recessive
efam.subjects[5].affected = True
assert not efam.auto_rec()
efam.subjects[5].affected = False
# set both kids to hom_alt:
efam.gt_types[3] = Family.HOM_ALT
assert not efam.auto_rec()
assert efam.auto_rec(only_affected=False)
# set unaffected kid back:
efam.gt_types[3] = Family.HET
# expected that is is auto_rec
assert efam.auto_rec()
# but it's not if we set all depths to 9 and have min_depth of 10
efam.gt_depths = [9] * 6
assert not efam.auto_rec(min_depth=10)
efam.gt_depths[2] = 1000
# even if affected has high depth
assert not efam.auto_rec(min_depth=10)
# passes if we meet the minimum depth
efam.gt_depths = [100] * 6
assert efam.auto_rec(min_depth=10)
# check if we have no affecteds:
efam.subjects[2].affected = False
assert not efam.auto_rec(min_depth=10)
def make_fam(n_affecteds, n_unaffecteds, n_unknowns, id="xxx"):
samples = []
for i in range(n_affecteds):
samples.append(Sample('affected_%d' % i, affected=True,
sex=random.randint(1, 2), name='affected_%d' % i))
for i in range(n_unaffecteds):
samples.append(Sample('unaffected_%d' % i, affected=False,
sex=random.randint(1, 2), name='affected_%d' % i))
for i in range(n_unknowns):
samples.append(Sample('unknown_%d' % i, affected=None,
sex=random.randint(1, 2), name='affected_%d' % i))
for i in range(int((n_affecteds + n_affecteds + n_unknowns) / 2)):
sample = random.choice(samples)
if random.random() < 0.9:
try:
sample.dad = random.choice([s for s in samples if not s == sample and s.sex == 'male'])
except IndexError:
pass
if random.random() < 0.9:
try:
sample.mom = random.choice([s for s in samples if not s == sample and s.sex == 'female'])
except IndexError:
pass
fam = EvalFamily(Family(samples, 'fam_%s' % id))
fam.gt_types = [random.randrange(0, 4) for _ in range(len(samples))]
fam.gt_depths = [random.randrange(0, 100) for _ in range(len(samples))]
fam.gt_phred_ll_homref = [random.randrange(0, 100) for _ in range(len(samples))]
fam.gt_phred_ll_het = [random.randrange(0, 100) for _ in range(len(samples))]
fam.gt_phred_ll_homalt = [random.randrange(0, 100) for _ in range(len(samples))]
fam.gt_quals = [random.randrange(5, 100) for _ in range(len(samples))]
return fam
