def test_97_percent_clustering_feature4_most_abundant(self):
input_table = biom.Table(np.array([[4, 5, 6],
[1, 1, 2],
[7, 8, 9],
[100, 101, 103]]),
['feature1', 'feature2', 'feature3',
'feature4'],
['sample1', 'sample2', 'sample3'])
exp_table = biom.Table(np.array([[111, 114, 118],
[1, 1, 2]]),
['feature4', 'feature2'],
['sample1', 'sample2', 'sample3'])
with redirected_stdio(stderr=os.devnull):
obs_table, obs_sequences = cluster_features_de_novo(
sequences=self.input_sequences, table=input_table,
perc_identity=0.97)
# order of identifiers is important for biom.Table equality
obs_table = \
obs_table.sort_order(exp_table.ids(axis='observation'),
axis='observation')
self.assertEqual(obs_table, exp_table)
# sequences are reverse-sorted by abundance in output
obs_seqs = _read_seqs(obs_sequences)
exp_seqs = [self.input_sequences_list[3], self.input_sequences_list[1]]
self.assertEqual(obs_seqs, exp_seqs)
def test_extra_features_in_sequences(self):
input_table = biom.Table(np.array([[0, 1, 3], [1, 1, 2], [4, 5, 6]]),
['feature1', 'feature2', 'feature3'],
['sample1', 'sample2', 'sample3'])
with self.assertRaisesRegex(ValueError,
expected_regex='Feature feature4 is pre'):
clustered_table, clustered_sequences = cluster_features_de_novo(
sequences=self.input_sequences, table=input_table,
perc_identity=1.0)
def test_no_overlapping_feature_ids(self):
input_table = biom.Table(
np.array([[0, 1, 3], [1, 1, 2], [4, 5, 6], [7, 8, 9], [1, 1, 1]]),
['f1', 'f2', 'f3', 'f4', 'f5'], ['sample1', 'sample2', 'sample3'])
with self.assertRaisesRegex(ValueError,
expected_regex='Feature feature1 is pre'):
clustered_table, clustered_sequences = cluster_features_de_novo(
sequences=self.input_sequences,
table=input_table,
perc_identity=1.0)
def test_no_clustering(self):
with redirected_stdio(stderr=os.devnull):
obs_table, obs_sequences = cluster_features_de_novo(
sequences=self.input_sequences, table=self.input_table,
perc_identity=1.0)
# order of identifiers is important for biom.Table equality
obs_table = \
obs_table.sort_order(self.input_table.ids(axis='observation'),
axis='observation')
self.assertEqual(obs_table, self.input_table)
obs_seqs = _read_seqs(obs_sequences)
# sequences are reverse-sorted by abundance in output
exp_seqs = [self.input_sequences_list[0], self.input_sequences_list[3],
self.input_sequences_list[2], self.input_sequences_list[1]]
self.assertEqual(obs_seqs, exp_seqs)
def test_1_percent_clustering(self):
exp_table = biom.Table(np.array([[112, 115, 120]]),
['feature1'],
['sample1', 'sample2', 'sample3'])
with redirected_stdio(stderr=os.devnull):
obs_table, obs_sequences = cluster_features_de_novo(
sequences=self.input_sequences, table=self.input_table,
perc_identity=0.01)
# order of identifiers is important for biom.Table equality
obs_table = \
obs_table.sort_order(exp_table.ids(axis='observation'),
axis='observation')
self.assertEqual(obs_table, exp_table)
# sequences are reverse-sorted by abundance in output
obs_seqs = _read_seqs(obs_sequences)
exp_seqs = [self.input_sequences_list[0]]
self.assertEqual(obs_seqs, exp_seqs)
def test_short_sequences(self):
input_sequences_fp = self.get_data_path('dna-sequences-short.fasta')
input_sequences = DNAFASTAFormat(input_sequences_fp, mode='r')
input_table = biom.Table(np.array([[0, 1, 3], [1, 1, 2]]),
['feature1', 'feature2'],
['sample1', 'sample2', 'sample3'])
exp_table = biom.Table(np.array([[1, 2, 5]]), ['feature1'],
['sample1', 'sample2', 'sample3'])
with redirected_stdio(stderr=os.devnull):
obs_table, obs_sequences = cluster_features_de_novo(
sequences=input_sequences,
table=input_table,
perc_identity=0.01)
# order of identifiers is important for biom.Table equality
obs_table = \
obs_table.sort_order(exp_table.ids(axis='observation'),
axis='observation')
