def test_average(self):
"""summarize phenotypes using an average (consistent values)."""
# first add several pieces of evidence into an entity object
m = Entity("abc", "genes")
d1 = PhenotypeDatum("MP:002", Experiment(1, 0.8, 0.05))
d2 = PhenotypeDatum("MP:007", Experiment(0, 0.4, 0.05))
d3 = PhenotypeDatum("MP:002", Experiment(1, 0.6, 0.05))
d4 = PhenotypeDatum("MP:007", Experiment(0, 0.4, 0.15))
d5 = PhenotypeDatum("MP:009", Experiment(1, 0.6, 0.05))
m.add(d1).add(d2).add(d3)
m.add(d4).add(d5)
self.assertEqual(len(m.data), 5)
# check that the average contains all phenotypes
m.average()
self.assertEqual(len(m.data), 3)
expected_tpr = {"MP:002": 0.7, "MP:007": 0.4, "MP:009": 0.6}
expected_fpr = {"MP:002": 0.05, "MP:007": 0.1, "MP:009": 0.05}
expected_val = {"MP:002": 1, "MP:007": 0, "MP:009": 1}
for i in range(3):
iphen = m.data[i].phenotype
iexp = m.data[i].experiment
self.assertEqual(iexp.value, expected_val[iphen])
self.assertEqual(iexp.tpr, expected_tpr[iphen])
self.assertEqual(iexp.fpr, expected_fpr[iphen])
def test_consensus_imputed(self):
"""summarize multiple rows of phenotypes using a consensus, with imputed values"""
# first add several pieces of evidence into an entity object
m = Entity("abc", "genes")
d1 = PhenotypeDatum("MP:002", Experiment(1, 0.8, 0.05))
# here add with value between 0 and 1
d2 = PhenotypeDatum("MP:007", Experiment(0.6, 0.4, 0.05))
d3 = PhenotypeDatum("MP:007", Experiment(0.4, 0.6, 0.05))
m.add(d1).add(d2).add(d3)
self.assertEqual(len(m.data), 3)
# check that the consensus matches the inputs
m.consensus()
self.assertEqual(len(m.data), 2)
c1 = m.data[0]
c2 = m.data[1]
expected_tpr = {"MP:002": 0.8, "MP:007": 0.5}
expected_fpr = {"MP:002": 0.05, "MP:007": 0.05}
expected_val = {"MP:002": 1, "MP:007": 0.5}
for i in range(2):
iphen = m.data[i].phenotype
iexp = m.data[i].experiment
self.assertEqual(iexp.value, expected_val[iphen])
self.assertEqual(iexp.tpr, expected_tpr[iphen])
self.assertEqual(iexp.fpr, expected_fpr[iphen])
def test_compare_by_phenotype(self):
"""comparison should compare phenotypes, experiments, timestamps"""
d1 = PhenotypeDatum("MP:1", e1, "2018")
d2 = PhenotypeDatum("MP:1", e1, "2018")
d3 = PhenotypeDatum("MP:2", e1, "2018")
self.assertTrue(d1 == d2)
self.assertTrue(d1 != d3)
def test_equivalent_same_phenotypes(self):
"""entities with the same phenotypes are equivalent"""
m1, m2 = Entity("A", "X"), Entity("A", "X")
m1.add(PhenotypeDatum("MP:002", Experiment(1, 0.8, 0.05)))
m1.add(PhenotypeDatum("MP:005", Experiment(1, 0.8, 0.05)))
m2.add(PhenotypeDatum("MP:005", Experiment(1, 0.8, 0.05)))
m2.add(PhenotypeDatum("MP:002", Experiment(1, 0.8, 0.05)))
self.assertTrue(m1.equivalent(m2))
self.assertTrue(m2.equivalent(m1))
def test_trim_easy_keep(self):
"""trimming does not eliminate node if ask to keep."""
m = Entity("abc", "genes")
d1 = PhenotypeDatum("DOID:4", Experiment(1, 0.8, 0.05))
d2 = PhenotypeDatum("DOID:11044", Experiment(1, 0.8, 0.05))
m.add(d1).add(d2)
self.assertEqual(len(m.data), 2)
m.trim_ancestors(self.obo, set(["DOID:4"]))
self.assertEqual(len(m.data), 2)
def test_trim_nothing(self):
"""trimming does nothing if there is nothing to do."""
m = Entity("abc", "genes")
d1 = PhenotypeDatum("DOID:3650", Experiment(1, 0.8, 0.05))
d2 = PhenotypeDatum("DOID:11044", Experiment(1, 0.8, 0.05))
m.add(d1).add(d2)
self.assertEqual(len(m.data), 2)
m.trim_ancestors(self.obo)
self.assertEqual(len(m.data), 2)
def test_equivalent_different_phenotypes(self):
"""entities with the same phenotypes are equivalent"""
m1, m2 = Entity("A", "X"), Entity("A", "X")
# add phenotypes, but two
m1.add(PhenotypeDatum("MP:001", Experiment(1, 0.8, 0.05)))
m1.add(PhenotypeDatum("MP:005", Experiment(1, 0.8, 0.05)))
m2.add(PhenotypeDatum("MP:005", Experiment(1, 0.8, 0.05)))
m2.add(PhenotypeDatum("MP:002", Experiment(1, 0.8, 0.05)))
self.assertFalse(m1.equivalent(m2))
self.assertFalse(m2.equivalent(m1))
def test_trim_easy(self):
"""trimming eliminates root node."""
m = Entity("abc", "genes")
d1 = PhenotypeDatum("DOID:4", Experiment(1, 0.8, 0.05))
d2 = PhenotypeDatum("DOID:11044", Experiment(1, 0.8, 0.05))
m.add(d1).add(d2)
self.assertEqual(len(m.data), 2)
m.trim_ancestors(self.obo)
self.assertEqual(len(m.data), 1)
self.assertEqual(m.data[0].phenotype, "DOID:11044")
def test_trim_medium(self):
"""trimming eliminates when when there are several leafs."""
m = Entity("abc", "genes")
d1 = PhenotypeDatum("DOID:4", Experiment(1, 0.8, 0.05))
d2 = PhenotypeDatum("DOID:11044", Experiment(1, 0.8, 0.05))
d3 = PhenotypeDatum("DOID:0080015", Experiment(1, 0.8, 0.05))
d4 = PhenotypeDatum("DOID:655", Experiment(1, 0.8, 0.05))
m.add(d1).add(d2).add(d3).add(d4)
self.assertEqual(len(m.data), 4)
m.trim_ancestors(self.obo)
self.assertEqual(len(m.data), 2)
result = set([_.phenotype for _ in m.data])
self.assertEqual(result, set(["DOID:11044", "DOID:655"]))
def test_add_phenotype_data(self):
"""cannot add corrupt data"""
m = Entity("abc", "genes", marker_id="X:001", marker_symbol="x001")
self.assertEqual(len(m.data), 0, "initial model has no pheontypes")
d1 = PhenotypeDatum("MP:002", Experiment(1, 0.8, 0.0555))
m.add(d1)
d2 = PhenotypeDatum("MP:007", Experiment(1, 0.456, 0.0234))
m.add(d2)
self.assertEqual(len(m.data), 2, "just added two phenotypes")
# check content of each datum
pheno_str_0 = str(m.data[0])
pheno_str_1 = str(m.data[1])
self.assertTrue("002" in pheno_str_0)
self.assertTrue("555" in pheno_str_0)
self.assertTrue("234" in pheno_str_1)
def test_equivalent_phenotypes(self):
"""entities with different phenotypes cannot be the same."""
m1, m2 = Entity("A", "X"), Entity("A", "X")
m1.add(PhenotypeDatum("MP:002", Experiment(1, 0.8, 0.05)))
self.assertFalse(m1.equivalent(m2))
self.assertFalse(m2.equivalent(m1))
def test_str(self):
"""object can be summarized."""
datum = PhenotypeDatum("MP:001", e1)
datum_str = repr(datum)
self.assertTrue("MP:001" in datum_str)
self.assertTrue("Experiment" in datum_str)
self.assertTrue("0.8" in datum_str)
def test_default_timestamp(self):
"""default should set a timestamp"""
now = datetime.now()
datum = PhenotypeDatum("MP:1", Experiment(1, 0.7, 0.1))
stamp = datetime.strptime(datum.timestamp, timestamp_format)
diff = (now - stamp).total_seconds()
self.assertLess(diff, 60, "stamps should be within a few seconds")
def test_init(self):
"""init of basic object"""
datum = PhenotypeDatum("MP:1", Experiment(1, 0.7, 0.1))
self.assertEqual(datum.phenotype, "MP:1")
self.assertEqual(datum.value, 1)
self.assertEqual(datum.tpr, 0.7)
self.assertEqual(datum.fpr, 0.1)
self.assertFalse(datum.timestamp is None)
def test_average_2(self):
"""summarize phenotypes using an average (discordant values)."""
# first add several pieces of evidence into an entity object
m = Entity("abc", "genes")
d1 = PhenotypeDatum("MP:002", Experiment(1, 0.8, 0.05))
d2 = PhenotypeDatum("MP:002", Experiment(0, 0.4, 0.05))
d3 = PhenotypeDatum("MP:002", Experiment(1, 0.6, 0.05))
m.add(d1).add(d2).add(d3)
self.assertEqual(len(m.data), 3)
# check that the consensus matches the inputs
m.average()
self.assertEqual(len(m.data), 1)
self.assertEqual(m.data[0].phenotype, "MP:002")
iexp = m.data[0].experiment
self.assertGreater(iexp.value, 0)
self.assertAlmostEqual(iexp.tpr, (0.8 + 0.0 + 0.6) / 3)
self.assertAlmostEqual(iexp.fpr, 0.05)
def control1(self, refname, entity):
"""create a model with sibling phenotypes to a given model"""
model = tech_model(refname + "_siblings", "siblings")
model.set_description("control_for", refname)
timestamp = self.timestamp
for datum in entity.data:
phen = datum.phenotype
newphen = self._pick_sibling(phen)
model.add(PhenotypeDatum(newphen, datum.experiment, timestamp))
return model.trim_ancestors(self.obo)
def control0(self, refname, refrep):
"""create a model with the same phenotypes as a representation. """
model = tech_model(refname + "_match", "match")
model.set_description("control_for", refname)
timestamp = self.timestamp
for phen in refrep.keys():
phen_value = refrep.get(phen)
phen_exp = Experiment(1, self.tpr, self.fpr)
model.add(PhenotypeDatum(phen, phen_exp, timestamp))
return model.trim_ancestors(self.obo)
def test_consensus_2(self):
"""summarize multiple rows of phenotypes using a consensus with some discordance."""
# first add several pieces of evidence into an entity object
m = Entity("abc", "genes")
d1 = PhenotypeDatum("MP:002", Experiment(1, 0.8, 0.05))
d2 = PhenotypeDatum("MP:002", Experiment(0, 0.4, 0.05))
d3 = PhenotypeDatum("MP:002", Experiment(1, 0.6, 0.05))
m.add(d1).add(d2).add(d3)
self.assertEqual(len(m.data), 3)
# check that the consensus matches the inputs
m.consensus()
self.assertEqual(len(m.data), 1)
c1 = m.data[0]
iphen = m.data[0].phenotype
iexp = m.data[0].experiment
self.assertEqual(iexp.value, 1)
# the tpr will be lower than (0.6+0.8)/2
# it should be (0.7*2/3)
self.assertEqual(iexp.tpr, 0.7 * (2 / 3))
self.assertEqual(iexp.fpr, 0.05)
def get_gxd(gxd_path, emp_map, tprfpr):
"""read a file with marker-emapa associationss
Arguments:
gxd_path file with columns ....
emp_map dict mapping EMAPA ids to other ids
tprfpr 2-tuple with (tpr, fpr)
Returns:
dict mapping markers to phenotypes terms
"""
tpr = tprfpr[0]
fpr = tprfpr[1]
# get all the mapping from the raw file
result = dict()
with open_file(gxd_path, "rt") as f:
reader = csv.DictReader(f, delimiter="\t", quotechar="'")
for row in reader:
feature = row["feature.primaryIdentifier"]
emapa = row["structure.identifier"]
strength = row["strength"]
if feature not in result:
modelid = "GXD_" + feature
result[feature] = Entity(modelid,
"expression",
marker_id=feature)
result[feature].set_description("expression", 1)
result[feature].set_description("source", "GXD")
if emapa not in emp_map:
continue
if strength not in gxd_strength:
continue
# determine whether to add a positive or negative phenotype
strength_factor = gxd_strength[strength]
row_exp = Experiment(1, fpr + (tpr - fpr) * strength_factor, fpr)
if strength == "Absent":
row_exp.value = 0
for mp in emp_map[emapa]:
result[feature].add(PhenotypeDatum(mp, row_exp))
# get a concensus value
for id in result:
result[id].consensus()
return result
def prep_IMPC(datapath, tprfpr, pthreshold, simplify="average", obo=None):
"""parse IMPC statistical results and assemble a set of models.
Args:
datapath: path to MGI raw file
tprfpr: list with two elements (tpr, fpr)
pthreshold: float, minimum threshold for significance
simplify: string, method for simplifying multiple data type
(use 'none', 'average', or 'consensus')
obo: object of class MinimalObo
"""
models = dict()
if datapath is None:
return models
now = now_timestamp()
base_tpr, base_fpr = tprfpr[0], tprfpr[1]
male = set(["M", "B", "U"])
female = set(["F", "B", "U"])
def create_models(id, category, zygosity, row):
"""Create a family of model definitions, for sex=FMU, neg_phen=01"""
prefix = "IMPC_" + id + "_" + zygosity + "_"
for suffix in ["F", "FA", "M", "MA", "U", "UA"]:
id = prefix + suffix
if id not in models:
models[id] = impc_model(id, category, row, zygosity)
models[id].set_description("sex", sex_code(suffix))
with_negative = negative_code(suffix)
models[id].set_description("neg_phenotypes", with_negative)
def add_to_model(datum, id, zygosity, suffix):
"""add a datum into an existing model definition.
Arguments:
datum phenotype and experiment result
id, zygosity, suffix
characterization of model
"""
id = "IMPC_" + id + "_" + zygosity + "_" + suffix
models[id].add(datum)
def add_set_to_models(datum, row, val, sex):
"""helper to add a set of models, for alleles, markers
Arguments:
datum phenotype and experiment result
row dict
val value of phenotype (0/1)
sex one-letter code
"""
zygosity = (row["zygosity"])[:3]
zygosity = "hom" if zygosity == "hem" else zygosity
marker = row["marker_accession_id"]
allele = row["allele_accession_id"]
# perhaps create model definitions
create_models(marker, "marker", zygosity, row)
create_models(allele, "allele", zygosity, row)
# record phenotypes into the models
if val == 1:
add_to_model(datum, marker, zygosity, sex)
add_to_model(datum, allele, zygosity, sex)
add_to_model(datum, marker, zygosity, sex + "A")
add_to_model(datum, allele, zygosity, sex + "A")
# get a map from parameter to mp_terms - used for negative phenotypes
parameter_phenotype_map = get_parameter_phenotype_map(datapath, obo)
with open_file(datapath, "rt") as f:
reader = csv.DictReader(f, delimiter=",", quotechar="\"")
for row in reader:
# skip over bad data rows
if row["status"] not in ("Success", "Successful"):
continue
if row["allele_symbol"] == "":
continue
# get a phenotype MP id
phenotype = row["mp_term_id"].strip()
# redefine some phenotypes
# (this handles morphology MP:0002169 annotations)
parameter = row["parameter_name"].strip()
if phenotype + " " + parameter in redef:
phenotype = redef[phenotype + " " + parameter]
if phenotype == "" and parameter in parameter_phenotype_map:
phenotype = parameter_phenotype_map[parameter]
if phenotype == "" or phenotype == "MP:0002169":
continue
sex = sex_code(row["phenotype_sex"])
# identify whether this is a positive or a negative phenotype
value = get_value(row, pthreshold)
# add data at marker level, allele level, by gender
hit = Experiment(value, base_tpr, base_fpr)
datum = PhenotypeDatum(phenotype, hit, now)
add_set_to_models(datum, row, value, "U")
if sex in male:
add_set_to_models(datum, row, value, "M")
if sex in female:
add_set_to_models(datum, row, value, "F")
# some models may have redundant rows (e.g. a phenotype recorded twice)
# so collapse into a consensus here
if simplify == "consensus":
for id in models:
models[id].consensus()
elif simplify == "average":
for id in models:
models[id].average()
return models
def test_order_by_timestamp(self):
d1 = PhenotypeDatum("MP:1", e1, "2016")
d2 = PhenotypeDatum("MP:1", e1, "2018")
self.assertTrue(d1 < d2)
self.assertTrue(d2 > d1)
def test_order_by_value(self):
d1 = PhenotypeDatum("MP:1", e2, "2018")
d2 = PhenotypeDatum("MP:1", e1, "2018")
self.assertTrue(d1 < d2)
self.assertTrue(d2 > d1)
def test_compare_by_timestamp(self):
"""comparison should compare phenotypes, experiments, timestamps"""
d1 = PhenotypeDatum("MP:1", e1, "2018")
d2 = PhenotypeDatum("MP:1", e1, "2017")
self.assertFalse(d1 == d2)
