def get_priors_from_models(models, categories, obo, dark=1):
"""Compute cohort-wide phenotype frequencies
Arguments:
models dictionary of Entity objects
categories set, determines what models to use in the calculation
obo object of class Obo
dark integer, dark count for phenotype normalization
Returns:
dict mapping phenotypes (from obo) to values [0,1]
integer, number of models used to inform the prior
"""
# get a subset of the models that satisfy the criteria
all = [obj for _, obj in models.items()]
hits = filter_entities_cat(all, categories)
# transfer phenotypes into representations
obodefaults = dict.fromkeys(obo.ids(), 0)
freqcounts = dict.fromkeys(list(obo.ids()), dark)
for entity in hits:
# prepare concise representations
rep = Representation(name=entity.id)
for datum in entity.data:
rep.set(datum.phenotype, datum.value)
# convert to complete representation
rep.impute(obo, obodefaults)
# count phenotypes
for phenotype in obo.ids():
freqcounts[phenotype] += rep.data[phenotype]
# convert counts into frequencies
result = counts_p(freqcounts, len(hits), dark)
return result, len(hits)
def test_impute_fromseeds_highfirst(self):
"""imputing values from manually-specified seeds."""
rr = Representation(dict())
# specify data for two children, DOID:4 is higher in tree, so should gain
rr.set("DOID:0014667", 0.4)
rr.set("DOID:0080015", 0.3)
rr.impute(self.obo,
self.obodef,
seeds=["DOID:0014667", "DOID:0080015"])
self.assertAlmostEqual(rr.get("DOID:0014667"),
0.4,
msg="should remain")
self.assertAlmostEqual(rr.get("DOID:0080015"),
0.3,
msg="should remain")
self.assertAlmostEqual(
rr.get("DOID:4"),
1 - ((1 - 0.4) * (1 - 0.3) * (1 - 0.2)),
msg="ancestor gains from two children (and prior)")
self.assertAlmostEqual(rr.get("DOID:655"),
0.2,
msg="remain; new DOID:4")
def test_impute_fromseeds_lowfirst(self):
"""imputing values from manually-specified seeds."""
rr = Representation(dict())
## specify an inconsistent set of values, DOID:4 is higher in tree, so cannot
## have a lower value than DOID:0014667
rr.set("DOID:0014667", 0.4).set("DOID:4", 0.1)
rr.impute(self.obo, self.obodef, seeds=["DOID:4", "DOID:0014667"])
self.assertEqual(rr.get("DOID:0080015"), 0.1, "child of DOID:4")
self.assertEqual(rr.get("DOID:655"), 0.1, "child of DOID:4")
def test_impute_down_ordering(self):
"""updating values in representation via negative evidence."""
r1 = Representation(dict())
r1.set("DOID:3650", 0.01).set("DOID:0014667", 0.05)
r2 = Representation(dict())
r2.set("DOID:3650", 0.01).set("DOID:0014667", 0.05)
# imputation down should not depend on order of the seeds
r1.impute(self.obo, self.obodef, seeds=["DOID:3650", "DOID:0014667"])
r2.impute(self.obo, self.obodef, seeds=["DOID:0014667", "DOID:3650"])
self.assertEqual(r1.data, r2.data, "all values the same")
def test_get_reference(self):
"""extract one reference from a representation set."""
r1 = Representation(name="refA").set("DOID:0014667", 0.4)
r1.impute(obo, obodefaults)
r2 = Representation(name="refB").set("DOID:0080015", 0.6)
r2.impute(obo, obodefaults)
rs = ReferenceSet(dict(refA=0.5, refB=0.5), ids=obo.ids())
rs.add(r1).add(r2)
r3 = rs.get_representation("refA")
self.assertTrue(r3.equal(r1))
def test_sum_with_impute(self):
"""sum of values associated with the representation."""
rr = Representation(dict())
rr.set("DOID:0014667", 1)
sum1 = rr.sum()
rr.impute(self.obo, self.obodef)
sum2 = rr.sum()
self.assertEqual(sum1, 1, "value of one phenotype")
self.assertGreater(sum2,
2,
msg="value for one phenotype+ancestors+defaults")
def test_impute_up_avoid_doubles(self):
"""updating values in representation via positive evidence in DAG"""
rr = Representation(dict())
# DOID:11044 in test ontology has two paths to root (DOID:4)
# one is direct (a shortcut)
# another path is through 0080015
rr.set("DOID:11044", 0.4)
rr.impute(self.obo, self.obodef)
self.assertGreater(rr.get("DOID:0080015"), 0.2,
"ancestor should increase")
self.assertAlmostEqual(
rr.get("DOID:0080015"),
rr.get("DOID:4"),
msg="4 should get bumped once, despite two paths from 11044")
def test_add_incrementally(self):
"""transferring values into a representation set."""
r1 = Representation(name="refA").set("DOID:0014667", 0.4)
r1.impute(obo, obodefaults)
r2 = Representation(name="refB").set("DOID:0080015", 0.6)
r2.impute(obo, obodefaults)
rs = ReferenceSet(dict(refA=0.5, refB=0.5), ids=obo.ids())
rs.add(r1).add(r2)
self.assertEqual(rs.get("DOID:0014667", "refA"), 0.4,
"refset should contain inserted data")
self.assertEqual(rs.get("DOID:0080015", "refB"), 0.6,
"refset should contain inserted data")
self.assertEqual(rs.get("DOID:4", "refB"), 0.6,
"refset should contain imputed data")
def test_impute_down(self):
"""updating values in representation via negative evidence."""
rr = Representation(dict())
rr.set("unrelated", 0.8)
rr.set("DOID:0014667", 0.05)
rr.impute(self.obo, self.obodef)
self.assertAlmostEqual(rr.get("unrelated"), 0.8,
"out-of-ontology terms remain")
self.assertAlmostEqual(rr.get("DOID:0014667"), 0.05,
"set value should remain")
self.assertAlmostEqual(rr.get("DOID:4"), 0.2,
"ancestors should remain")
self.assertAlmostEqual(rr.get("DOID:0080015"), 0.2,
"unrelated terms get default")
self.assertAlmostEqual(rr.get("DOID:655"), 0.05,
"children are unaffected")
def test_impute_up_always_increases(self):
"""updating values in representation via positive evidence."""
rr = Representation(dict())
rr.set("DOID:3650", 0.25)
defaults = self.obodef.copy()
defaults["DOID:0014667"] = 0.5
defaults["DOID:4"] = 1
rr.impute(self.obo, defaults)
self.assertEqual(rr.get("DOID:3650"), 0.25, "set value should remain")
self.assertGreater(rr.get("DOID:0060158"), 0.25,
"ancestors should receive greater score")
self.assertEqual(rr.get("DOID:655"), 0.2,
"unrelated should stay at default")
# ancestor that has already a higher score than what is propagated
self.assertGreater(
rr.get("DOID:0014667"), 0.5,
"ancestor should receive score greater than its prior")
def test_impute_fromseeds_auto(self):
"""imputing values from automatically-ordered seeds."""
# specify an inconsistent set of values, DOID:4 is higher in tree, so cannot
# have a lower value than DOID:0014667
# However, low DOID:4 can impact on other branches
rr1 = Representation(dict())
rr1.set("DOID:0014667", 0.4).set("DOID:4", 0.1)
rr1.impute(self.obo, self.obodef)
# auto seeds
rr2 = Representation(dict())
rr2.set("DOID:0014667", 0.4).set("DOID:4", 0.1)
rr2.impute(self.obo, self.obodef)
# auto seeds, different initial ordering
rr3 = Representation(dict())
rr3.set("DOID:4", 0.1).set("DOID:0014667", 0.4)
rr3.impute(self.obo, self.obodef)
self.assertTrue(rr1.data == rr2.data,
"auto and manual should have same data")
self.assertTrue(rr2.data == rr3.data,
"should be = regardless of input order")
self.assertGreater(rr1.data["DOID:0014667"], 0.2,
"DOID:0014667 increase by direct evidence")
self.assertGreater(rr1.data["DOID:4"], 0.2,
"DOID:4 increases driven by 0014667")
self.assertEqual(rr1.data["DOID:11044"], 0.1,
"low raw DOID:4 propagates down")
def test_impute_up(self):
"""updating values in representation via positive evidence."""
rr = Representation(dict())
rr.set("unrelated", 0.8)
rr.set("DOID:0014667", 0.4)
rr.impute(self.obo, self.obodef)
self.assertEqual(rr.get("unrelated"),
0.8,
msg="out-of-ontology terms remain")
self.assertEqual(rr.get("DOID:0014667"),
0.4,
msg="set value should remain")
self.assertGreater(rr.get("DOID:4"),
0.4,
msg="ancestors should receive greater score")
self.assertEqual(rr.get("DOID:0080015"),
0.2,
msg="unrelated terms get default")
self.assertEqual(rr.get("DOID:655"),
0.2,
msg="children are unaffected")