def test_true_positive(self):
m = VariantCalls('Q54H 444H')
rule = HCVR("SCORE FROM ( 54H => 0, 444H => 8 )")
dtree = rule.dtree(m)
expected_repr = "[Mutation('Q54H'), Mutation('444H')]"
self.assertEqual(expected_repr, repr(sorted(dtree.residues)))
def test_score_residues(self):
rule = HCVR("SCORE FROM ( 100G => 10, 101D => 20 )")
expected_residue = repr({Mutation('S100G')})
result = rule.dtree(VariantCalls("S100G R101d"))
self.assertEqual(expected_residue, repr(result.residues))
def test_score_comment(self):
rule = HCVR(
'SCORE FROM (100G => 10, 200T => 3, 100S => "flag1 with_space")')
self.assertEqual(rule(VariantCalls("100G 200d")), 10)
result = rule.dtree(VariantCalls("100S 200T"))
self.assertEqual(result.score, 3)
self.assertIn("flag1 with_space", result.flags)
def interpret(self, aaseq, region):
result = AsiResult()
raw_mutations = defaultdict(set)
result.alg_name = self.alg_name
result.alg_version = self.alg_version
drug_classes = self.gene_def.get(region, {})
default_level = HcvResistanceLevels.FAIL.level
default_level_name = self.level_def[str(default_level)]
mutations = VariantCalls(reference=self.stds[region], sample=aaseq)
for drug_class in drug_classes:
for drug_code in self.drug_class[drug_class]:
drug_name, drug_rules = self.drugs[drug_code]
drug_result = AsiDrugResult()
drug_result.code = drug_code
drug_result.name = drug_name
drug_result.level = default_level
drug_result.level_name = default_level_name
drug_result.drug_class = drug_class
for condition, actions in drug_rules:
rule = HCVR(condition)
try:
rule_result = rule.dtree(mutations)
score = float(rule_result.score)
flags = rule_result.flags
# rule_result.residues doesn't always have wild types.
m = {mutation
for mutation_set in mutations
for mutation in mutation_set
if mutation in rule_result.residues}
raw_mutations[drug_class] |= m
for action, comment in actions:
if action == 'level':
if int(comment) > drug_result.level:
drug_result.level = int(comment)
drug_result.level_name = self.level_def[comment]
elif action == 'comment':
comment, _ = self.comment_def[comment]
while (re.search(r'\$numberOfMutsIn{', comment) or
re.search(r'\$listMutsIn{', comment)):
comment = self.comment_filter(comment, aaseq, region)
drug_result.comments.append(comment)
elif action == 'scorerange':
drug_result.score = score
scorerange = comment
if scorerange == 'useglobalrange':
scorerange = self.global_range
if score == 0 and flags:
if 'Not available' in flags:
drug_result.level = HcvResistanceLevels.NA.level
elif 'Not indicated' in flags:
drug_result.level = HcvResistanceLevels.NOT_INDICATED.level
elif 'Effect unknown' in flags:
drug_result.level = HcvResistanceLevels.UNKNOWN_MUTATIONS.level
else:
# use score range to determine level
for low_score, high_score, level in scorerange:
if low_score == '-INF':
low_score = -99999 # that is close enough to negative infinity.
else:
low_score = float(low_score)
if high_score == 'INF':
high_score = 99999 # that is close enough to infinity.
else:
high_score = float(high_score)
if low_score <= drug_result.score <= high_score:
if int(level) > drug_result.level:
drug_result.level = int(level)
break
except MissingPositionError:
drug_result.level = HcvResistanceLevels.FAIL.level
drug_result.level_name = self.level_def[
str(drug_result.level)]
result.drugs.append(drug_result)
for cls, cls_mutations in raw_mutations.items():
result.mutations[cls] = [str(m) for m in cls_mutations]
result.drugs.sort(key=lambda e: e.code)
result.drugs.sort(key=lambda e: e.drug_class, reverse=True)
# comments
for target_region, results in self.mutation_comments:
if target_region != region:
continue
for cond, actions in results:
# This evaluates comment rules.
# Previous evaluation was scoring rules.
rule = HCVR(cond)
try:
scoring = rule(mutations)
if scoring:
for _, act in actions:
comment_template, _ = self.comment_def[act]
comment = self.comment_filter(comment_template, aaseq, region)
result.mutation_comments.append(comment)
except MissingPositionError:
pass
return result
def interpret(self, aaseq, region):
result = AsiResult()
raw_mutations = defaultdict(set)
result.alg_name = self.alg_name
result.alg_version = self.alg_version
drug_classes = self.gene_def.get(region, {})
default_level = ResistanceLevels.FAIL.level
default_level_name = self.level_def[str(default_level)]
mutations = VariantCalls(reference=self.stds[region], sample=aaseq)
for drug_class in drug_classes:
for drug_code in self.drug_class[drug_class]:
drug_name, drug_rules = self.drugs[drug_code]
drug_result = AsiDrugResult()
drug_result.code = drug_code
drug_result.name = drug_name
drug_result.level = default_level
drug_result.level_name = default_level_name
drug_result.drug_class = drug_class
for condition, actions in drug_rules:
rule = HCVR(condition)
try:
rule_result = rule.dtree(mutations)
score = float(rule_result.score)
flags = rule_result.flags
# rule_result.residues doesn't always have wild types.
m = {
mutation
for mutation_set in mutations
for mutation in mutation_set
if mutation in rule_result.residues
}
raw_mutations[drug_class] |= m
for action, comment in actions:
if action == 'level':
if int(comment) > drug_result.level:
drug_result.level = int(comment)
drug_result.level_name = self.level_def[
comment]
elif action == 'comment':
comment, _ = self.comment_def[comment]
while (re.search('\$numberOfMutsIn{', comment)
or re.search('\$listMutsIn{', comment)):
comment = self.comment_filter(
comment, aaseq, region)
drug_result.comments.append(comment)
elif action == 'scorerange':
drug_result.score = score
scorerange = comment
if scorerange == 'useglobalrange':
scorerange = self.global_range
if score == 0 and flags:
if 'Not available' in flags:
drug_result.level = ResistanceLevels.NA.level
elif 'Not indicated' in flags:
drug_result.level = ResistanceLevels.NOT_INDICATED.level
elif 'Effect unknown' in flags:
drug_result.level = ResistanceLevels.UNKNOWN_MUTATIONS.level
else:
# use score range to determine level
for low_score, high_score, level in scorerange:
if low_score == '-INF':
low_score = -99999 # that is close enough to negative infinity.
else:
low_score = float(low_score)
if high_score == 'INF':
high_score = 99999 # that is close enough to infinity.
else:
high_score = float(high_score)
if low_score <= drug_result.score <= high_score:
if int(level) > drug_result.level:
drug_result.level = int(level)
break
except MissingPositionError:
drug_result.level = ResistanceLevels.FAIL.level
drug_result.level_name = self.level_def[str(
drug_result.level)]
result.drugs.append(drug_result)
for cls, cls_mutations in raw_mutations.items():
result.mutations[cls] = [str(m) for m in cls_mutations]
result.drugs.sort(key=lambda e: e.code)
result.drugs.sort(key=lambda e: e.drug_class, reverse=True)
# comments
for target_region, results in self.mutation_comments:
if target_region != region:
continue
for cond, actions in results:
# This evaluates comment rules.
# Previous evaluation was scoring rules.
rule = HCVR(cond)
try:
scoring = rule(mutations)
if scoring:
for _, act in actions:
comment_template, _ = self.comment_def[act]
comment = self.comment_filter(
comment_template, aaseq, region)
result.mutation_comments.append(comment)
except MissingPositionError:
pass
return result