def test_biallelic(self):
pipeline = Pipeline()
hdf5 = VariationsH5(join(TEST_DATA_DIR, 'ril.hdf5'), mode='r')
flt = NonBiallelicFilter()
pipeline.append(flt)
vars_out = VariationsArrays()
pipeline.run(hdf5, vars_out)
# check same result with no pipeline
result2 = flt(hdf5)
assert numpy.allclose(vars_out['/calls/GT'],
result2[FLT_VARS]['/calls/GT'])
def test_low_dp_gt(self):
pipeline = Pipeline()
hdf5 = VariationsH5(join(TEST_DATA_DIR, 'ril.hdf5'), mode='r')
flt = LowDPGTsToMissingSetter(min_dp=5)
pipeline.append(flt)
vars_out = VariationsArrays()
pipeline.run(hdf5, vars_out)
# check same result with no pipeline
result2 = flt(hdf5)
assert numpy.allclose(vars_out['/calls/GT'],
result2[FLT_VARS]['/calls/GT'])
def test_filter_samples(self):
pipeline = Pipeline()
hdf5 = VariationsH5(join(TEST_DATA_DIR, 'ril.hdf5'), mode='r')
samples = hdf5.samples[:20]
flt = SampleFilter(samples)
pipeline.append(flt)
vars_out = VariationsArrays()
pipeline.run(hdf5, vars_out)
# check same result with no pipeline
result2 = flt(hdf5)
assert numpy.allclose(vars_out['/calls/GT'],
result2[FLT_VARS]['/calls/GT'])
def test_no_filtering(self):
pipeline = Pipeline()
hdf5 = VariationsH5(join(TEST_DATA_DIR, 'ril.hdf5'), mode='r')
flt = MafFilter(min_maf=0.1, max_maf=0.9, do_histogram=True,
do_filtering=False)
pipeline.append(flt, id_='filter1')
vars_out = None
result = pipeline.run(hdf5, vars_out)
# check same result with no pipeline
result2 = flt(hdf5)
assert numpy.allclose(result['filter1']['counts'], result2['counts'])
assert numpy.allclose(result['filter1']['edges'], result2['edges'])
def test_min_mac(self):
pipeline = Pipeline()
hdf5 = VariationsH5(join(TEST_DATA_DIR, 'ril.hdf5'), mode='r')
flt = MacFilter(min_mac=10, max_mac=30, do_histogram=True)
pipeline.append(flt, id_='filter1')
vars_out = VariationsArrays()
result = pipeline.run(hdf5, vars_out)
# check same result with no pipeline
result2 = flt(hdf5)
assert numpy.allclose(result['filter1']['counts'], result2['counts'])
assert numpy.allclose(result['filter1']['edges'], result2['edges'])
assert vars_out['/calls/GT'].shape[0] == 0
assert result2[FLT_VARS]['/calls/GT'].shape[0] == 0
def test_field_filter(self):
pipeline = Pipeline()
hdf5 = VariationsH5(join(TEST_DATA_DIR, 'ril.hdf5'), mode='r')
flt = FieldFilter(kept_fields=[GT_FIELD])
pipeline.append(flt)
vars_out = VariationsArrays()
pipeline.run(hdf5, vars_out)
# check same result with no pipeline
result2 = flt(hdf5)
assert numpy.allclose(vars_out['/calls/GT'],
result2[FLT_VARS]['/calls/GT'])
assert list(vars_out.keys()) == [GT_FIELD]
assert list(result2[FLT_VARS].keys()) == [GT_FIELD]
def test_snp_qual(self):
pipeline = Pipeline()
hdf5 = VariationsH5(join(TEST_DATA_DIR, 'ril.hdf5'), mode='r')
flt = SNPQualFilter(min_qual=100, max_qual=50000, do_histogram=True)
pipeline.append(flt)
vars_out = VariationsArrays()
result = pipeline.run(hdf5, vars_out)
# check same result with no pipeline
result2 = flt(hdf5)
assert numpy.allclose(result['0']['counts'], result2['counts'])
assert numpy.allclose(result['0']['edges'], result2['edges'])
assert numpy.allclose(vars_out['/calls/GT'],
result2[FLT_VARS]['/calls/GT'])
def test_snp_qual(self):
pipeline = Pipeline()
hdf5 = VariationsH5(join(TEST_DATA_DIR, 'ril.hdf5'), mode='r')
flt = SNPQualFilter(min_qual=100, max_qual=50000, do_histogram=True)
pipeline.append(flt)
vars_out = VariationsArrays()
result = pipeline.run(hdf5, vars_out)
# check same result with no pipeline
result2 = flt(hdf5)
assert numpy.allclose(result['0']['counts'], result2['counts'])
assert numpy.allclose(result['0']['edges'], result2['edges'])
assert numpy.allclose(vars_out['/calls/GT'],
result2[FLT_VARS]['/calls/GT'])
def test_min_maf(self):
pipeline = Pipeline()
hdf5 = VariationsH5(join(TEST_DATA_DIR, 'ril.hdf5'), mode='r')
flt = MafFilter(min_maf=0.1, max_maf=0.9, do_histogram=True)
pipeline.append(flt, id_='filter1')
vars_out = VariationsArrays()
result = pipeline.run(hdf5, vars_out)
# check same result with no pipeline
result2 = flt(hdf5)
assert numpy.allclose(result['filter1']['counts'], result2['counts'])
assert numpy.allclose(result['filter1']['edges'], result2['edges'])
assert numpy.allclose(vars_out['/calls/GT'],
result2[FLT_VARS]['/calls/GT'])
def test_het(self):
pipeline = Pipeline()
hdf5 = VariationsH5(join(TEST_DATA_DIR, 'ril.hdf5'), mode='r')
samples = hdf5.samples[:20]
flt = ObsHetFilter(min_het=0.02, max_het=0.5, samples=samples,
do_histogram=True)
pipeline.append(flt)
vars_out = VariationsArrays()
result = pipeline.run(hdf5, vars_out)
# check same result with no pipeline
result2 = flt(hdf5)
assert numpy.allclose(result['0']['counts'], result2['counts'])
assert numpy.allclose(result['0']['edges'], result2['edges'])
assert numpy.allclose(vars_out['/calls/GT'],
result2[FLT_VARS]['/calls/GT'])
def test_filter_chi2_gt_sample_sets(self):
pipeline = Pipeline()
hdf5 = VariationsH5(join(TEST_DATA_DIR, 'ril.hdf5'), mode='r')
samples1 = hdf5.samples[:20]
samples2 = hdf5.samples[20:]
flt = Chi2GtFreqs2SampleSetsFilter(samples1, samples2, min_pval=0.05,
do_histogram=True)
pipeline.append(flt)
vars_out = VariationsArrays()
result = pipeline.run(hdf5, vars_out)
# check same result with no pipeline
result2 = flt(hdf5)
assert numpy.allclose(result['0']['counts'], result2['counts'])
assert numpy.allclose(result['0']['edges'], result2['edges'])
assert numpy.allclose(vars_out['/calls/GT'],
result2[FLT_VARS]['/calls/GT'])
def test_het(self):
pipeline = Pipeline()
hdf5 = VariationsH5(join(TEST_DATA_DIR, 'ril.hdf5'), mode='r')
samples = hdf5.samples[:20]
flt = ObsHetFilter(min_het=0.02,
max_het=0.5,
samples=samples,
do_histogram=True)
pipeline.append(flt)
vars_out = VariationsArrays()
result = pipeline.run(hdf5, vars_out)
# check same result with no pipeline
result2 = flt(hdf5)
assert numpy.allclose(result['0']['counts'], result2['counts'])
assert numpy.allclose(result['0']['edges'], result2['edges'])
assert numpy.allclose(vars_out['/calls/GT'],
result2[FLT_VARS]['/calls/GT'])
def test_filter_chi2_gt_sample_sets(self):
pipeline = Pipeline()
hdf5 = VariationsH5(join(TEST_DATA_DIR, 'ril.hdf5'), mode='r')
samples1 = hdf5.samples[:20]
samples2 = hdf5.samples[20:]
flt = Chi2GtFreqs2SampleSetsFilter(samples1,
samples2,
min_pval=0.05,
do_histogram=True)
pipeline.append(flt)
vars_out = VariationsArrays()
result = pipeline.run(hdf5, vars_out)
# check same result with no pipeline
result2 = flt(hdf5)
assert numpy.allclose(result['0']['counts'], result2['counts'])
assert numpy.allclose(result['0']['edges'], result2['edges'])
assert numpy.allclose(vars_out['/calls/GT'],
result2[FLT_VARS]['/calls/GT'])
def test_fieldpath(self):
pipeline = Pipeline()
annot_id = 'test'
hdf5 = VariationsH5(join(TEST_DATA_DIR, 'ril.hdf5'), mode='r')
field = '/variations/info/{}'.format(annot_id)
annotator = IsVariableAnnotator(annot_id=annot_id,
samples=['1_14_1_gbs', '1_17_1_gbs'])
pipeline.append(annotator)
annotator = FieldValueFilter(field_path=field, value=0)
pipeline.append(annotator)
vars_out = VariationsArrays()
pipeline.run(hdf5, vars_out)
assert vars_out.num_variations == 484
def test_low_dp_gt(self):
pipeline = Pipeline()
hdf5 = VariationsH5(join(TEST_DATA_DIR, 'ril.hdf5'), mode='r')
flt = LowDPGTsToMissingSetter(min_dp=5)
pipeline.append(flt)
vars_out = VariationsArrays()
pipeline.run(hdf5, vars_out)
# check same result with no pipeline
result2 = flt(hdf5)
assert numpy.allclose(vars_out['/calls/GT'],
result2[FLT_VARS]['/calls/GT'])
def test_biallelic(self):
pipeline = Pipeline()
hdf5 = VariationsH5(join(TEST_DATA_DIR, 'ril.hdf5'), mode='r')
flt = NonBiallelicFilter()
pipeline.append(flt)
vars_out = VariationsArrays()
pipeline.run(hdf5, vars_out)
# check same result with no pipeline
result2 = flt(hdf5)
assert numpy.allclose(vars_out['/calls/GT'],
result2[FLT_VARS]['/calls/GT'])
def test_filter_samples(self):
pipeline = Pipeline()
hdf5 = VariationsH5(join(TEST_DATA_DIR, 'ril.hdf5'), mode='r')
samples = hdf5.samples[:20]
flt = SampleFilter(samples)
pipeline.append(flt)
vars_out = VariationsArrays()
pipeline.run(hdf5, vars_out)
# check same result with no pipeline
result2 = flt(hdf5)
assert numpy.allclose(vars_out['/calls/GT'],
result2[FLT_VARS]['/calls/GT'])
def test_field_filter(self):
pipeline = Pipeline()
hdf5 = VariationsH5(join(TEST_DATA_DIR, 'ril.hdf5'), mode='r')
flt = FieldFilter(kept_fields=[GT_FIELD])
pipeline.append(flt)
vars_out = VariationsArrays()
pipeline.run(hdf5, vars_out)
# check same result with no pipeline
result2 = flt(hdf5)
assert numpy.allclose(vars_out['/calls/GT'],
result2[FLT_VARS]['/calls/GT'])
assert list(vars_out.keys()) == [GT_FIELD]
assert list(result2[FLT_VARS].keys()) == [GT_FIELD]
def test_pipeline(self):
pipeline = Pipeline()
hdf5 = VariationsH5(join(TEST_DATA_DIR, 'ril.hdf5'), mode='r')
flt = MinCalledGTsFilter(min_called=0.1, range_=(0, 1))
pipeline.append(flt, id_='filter1')
vars_out = VariationsArrays()
result = pipeline.run(hdf5, vars_out)
# check same result with no pipeline
result2 = flt(hdf5)
assert numpy.allclose(result['filter1']['counts'], result2['counts'])
assert numpy.allclose(result['filter1']['edges'], result2['edges'])
assert numpy.allclose(vars_out['/calls/GT'],
result2[FLT_VARS]['/calls/GT'])
assert (result['filter1'][FLT_STATS][N_KEPT] ==
result2[FLT_STATS][N_KEPT])
assert result['filter1'][FLT_STATS][TOT] == result2[FLT_STATS][TOT]
assert (result['filter1'][FLT_STATS][N_FILTERED_OUT] ==
result2[FLT_STATS][N_FILTERED_OUT])
# check with no range set
pipeline = Pipeline()
flt = MinCalledGTsFilter(min_called=0.1, do_histogram=True)
pipeline.append(flt, id_='filter1')
vars_out = VariationsArrays()
result = pipeline.run(hdf5, vars_out)
result2 = flt(hdf5)
assert numpy.allclose(result['filter1']['counts'], result2['counts'])
assert numpy.allclose(result['filter1']['edges'], result2['edges'])
assert numpy.allclose(vars_out['/calls/GT'],
result2[FLT_VARS]['/calls/GT'])
# With rates False
pipeline = Pipeline()
flt = MinCalledGTsFilter(min_called=20, rates=False, do_histogram=True)
pipeline.append(flt, id_='filter1')
vars_out = VariationsArrays()
result = pipeline.run(hdf5, vars_out)
result2 = flt(hdf5)
assert result['filter1']['order'] == 0
assert numpy.allclose(result['filter1']['counts'], result2['counts'])
assert numpy.allclose(result['filter1']['edges'], result2['edges'])
assert numpy.allclose(vars_out['/calls/GT'],
result2[FLT_VARS]['/calls/GT'])
def test_pipeline(self):
pipeline = Pipeline()
hdf5 = VariationsH5(join(TEST_DATA_DIR, 'ril.hdf5'), mode='r')
flt = MinCalledGTsFilter(min_called=0.1, range_=(0, 1))
pipeline.append(flt, id_='filter1')
vars_out = VariationsArrays()
result = pipeline.run(hdf5, vars_out)
# check same result with no pipeline
result2 = flt(hdf5)
assert numpy.allclose(result['filter1']['counts'], result2['counts'])
assert numpy.allclose(result['filter1']['edges'], result2['edges'])
assert numpy.allclose(vars_out['/calls/GT'],
result2[FLT_VARS]['/calls/GT'])
assert (
result['filter1'][FLT_STATS][N_KEPT] == result2[FLT_STATS][N_KEPT])
assert result['filter1'][FLT_STATS][TOT] == result2[FLT_STATS][TOT]
assert (result['filter1'][FLT_STATS][N_FILTERED_OUT] ==
result2[FLT_STATS][N_FILTERED_OUT])
# check with no range set
pipeline = Pipeline()
flt = MinCalledGTsFilter(min_called=0.1, do_histogram=True)
pipeline.append(flt, id_='filter1')
vars_out = VariationsArrays()
result = pipeline.run(hdf5, vars_out)
result2 = flt(hdf5)
assert numpy.allclose(result['filter1']['counts'], result2['counts'])
assert numpy.allclose(result['filter1']['edges'], result2['edges'])
assert numpy.allclose(vars_out['/calls/GT'],
result2[FLT_VARS]['/calls/GT'])
# With rates False
pipeline = Pipeline()
flt = MinCalledGTsFilter(min_called=20, rates=False, do_histogram=True)
pipeline.append(flt, id_='filter1')
vars_out = VariationsArrays()
result = pipeline.run(hdf5, vars_out)
result2 = flt(hdf5)
assert result['filter1']['order'] == 0
assert numpy.allclose(result['filter1']['counts'], result2['counts'])
assert numpy.allclose(result['filter1']['edges'], result2['edges'])
assert numpy.allclose(vars_out['/calls/GT'],
result2[FLT_VARS]['/calls/GT'])
