def _type_variants(self):
self.out_json[self.sample]["variant_calls"] = {}
gt = VariantTyper(
expected_depths=self.expected_depths,
error_rate=self.expected_error_rate,
contamination_depths=self.contamination_depths,
ignore_filtered=self.ignore_filtered,
minor_freq=self.minor_freq,
confidence_threshold=self.variant_confidence_threshold,
filters=self.filters,
model=self.model,
kmer_size=self.kmer_size,
min_proportion_expected_depth=self.min_proportion_expected_depth,
ploidy=self.ploidy,
)
genotypes = []
filters = []
for probe_id, probe_coverages in self.variant_covgs.items():
probe_name = self._name_to_id(probe_coverages.var_name)
variant = None
call = gt.type(probe_coverages, variant=probe_name)
genotypes.append(sum(call["genotype"]))
filters.append(int(call["info"]["filter"] == []))
if (sum(call["genotype"]) > 0 or not call["genotype"]
or self.report_all_calls):
self.variant_calls[probe_name] = call
self.variant_calls_dict[probe_id] = call
self.out_json[self.sample]["genotypes"] = genotypes
self.out_json[self.sample]["filtered"] = filters
self.out_json[self.sample]["variant_calls"] = self.variant_calls_dict
def _simulate_snps(self):
correct_covg = np.random.poisson(lam=self.mean_depth,
size=self.iterations)
incorrect_covg = np.random.binomial(self.mean_depth,
self.error_rate,
size=self.iterations)
# f = open('test.simulate_snps_data.tsv', 'w')
# print('Correct_cov', 'Incorrect_cov', 'correct_k_count', 'incorrect_k_count', 'correct_percent_coverage', 'incorrect_percent_coverage', 'Cov', 'Conf', sep='\t', file=f)
probe_coverage_list = []
vtyper = VariantTyper([self.mean_depth],
error_rate=self.error_rate,
kmer_size=self.kmer_length)
for i in range(self.iterations):
if correct_covg[i] + incorrect_covg[i] == 0:
continue
min_depth = 1 # not used?
# Check what allele_length means in depth_to_expected_kmer_coun()! Probably need to change next two lines...
correct_k_count = (
self.kmer_length * correct_covg[i]
) + 0.01 # see KmerCountGenotypeModel.depth_to_expected_kmer_count()
incorrect_k_count = (
self.kmer_length * incorrect_covg[i]
) + 0.01 # see KmerCountGenotypeModel.depth_to_expected_kmer_count()
# correct_percent_coverage = ConfThresholder._simulate_percent_coverage(int(correct_k_count), 2 + self.kmer_length)
# incorrect_percent_coverage = ConfThresholder._simulate_percent_coverage(int(incorrect_k_count), 2 + self.kmer_length)
correct_percent_coverage = 100
incorrect_percent_coverage = ConfThresholder._get_incorrect_kmer_percent_cov(
int(incorrect_k_count), self.incorrect_kmer_to_pc_cov)
correct_probe_coverage = ProbeCoverage(
correct_percent_coverage,
self.mean_depth,
min_depth,
correct_k_count,
self.kmer_length,
)
incorrect_probe_coverage = ProbeCoverage(
incorrect_percent_coverage,
self.mean_depth,
min_depth,
incorrect_k_count,
self.kmer_length,
)
vpc = VariantProbeCoverage([correct_probe_coverage],
[incorrect_probe_coverage])
call = vtyper.type(vpc)
cov = np.log10(correct_covg[i] + incorrect_covg[i])
conf = call["info"]["conf"]
self.log_conf_and_covg.append((conf, cov))
# print(correct_covg[i], incorrect_covg[i], correct_k_count, incorrect_k_count, correct_percent_coverage, incorrect_percent_coverage, cov, conf, sep='\t', file=f)
# f.close()
self.log_conf_and_covg.sort(reverse=True)
def _type_variants(self):
self.out_json[self.sample]["variant_calls"] = {}
gt = VariantTyper(
expected_depths=self.expected_depths,
error_rate=self.expected_error_rate,
contamination_depths=self.contamination_depths,
ignore_filtered=self.ignore_filtered,
minor_freq=self.minor_freq,
confidence_threshold=self.variant_confidence_threshold,
filters=self.filters,
model=self.model,
kmer_size=self.kmer_size,
min_proportion_expected_depth=self.min_proportion_expected_depth,
ploidy=self.ploidy,
)
genotypes = []
filters = []
for probe_id, probe_coverages in self.variant_covgs.items():
probe_name = self._name_to_id(probe_coverages.var_name)
call = gt.type(probe_coverages, variant=probe_name)
genotypes.append(sum(call["genotype"]))
filters.append(int(call["info"]["filter"] == []))
if (sum(call["genotype"]) > 0 or not call["genotype"]
or self.report_all_calls):
self.variant_calls[probe_name] = call
self.variant_calls_dict[probe_id] = call
# note: here's an example probe_coverages.var_name:
# ref-K43R?var_name=AAG781686AGA&num_alts=1&ref=NC_000962.3&enum=0&gene=rpsL&mut=K43R
self._update_lineage_calls_dict(
call, probe_name=probe_coverages.var_name)
self.out_json[self.sample]["genotypes"] = genotypes
self.out_json[self.sample]["filtered"] = filters
self.out_json[self.sample]["variant_calls"] = self.variant_calls_dict
lineage_result, lineage_calls = self.predict_lineage()
self.out_json[self.sample]["lineage"] = {
"all_calls": lineage_calls,
"result": lineage_result,
}