def test_average(self):
t1 = Table(np.array([[1, 1, 1], [1, 1, 1]]), ['O1', 'O2'],
['S1', 'S2', 'S3'])
obs = merge([t1] * 3, 'average')
exp = Table(np.array([[1, 1, 1], [1, 1, 1]]), ['O1', 'O2'],
['S1', 'S2', 'S3'])
self.assertEqual(obs, exp)
def test_invalid_overlap_method(self):
t1 = Table(np.array([[0, 1, 3], [1, 1, 2]]), ['O1', 'O2'],
['S1', 'S2', 'S3'])
t2 = Table(np.array([[0, 2, 6], [2, 2, 4]]), ['O1', 'O3'],
['S1', 'S5', 'S6'])
with self.assertRaisesRegex(ValueError, 'overlap method'):
merge([t1, t2], 'peanut')
def test_invalid_overlapping_feature_ids(self):
t1 = Table(np.array([[0, 1, 3], [1, 1, 2]]), ['O1', 'O2'],
['S1', 'S2', 'S3'])
t2 = Table(np.array([[0, 2, 6], [2, 2, 4]]), ['O1', 'O3'],
['S4', 'S5', 'S6'])
with self.assertRaisesRegex(ValueError, 'features are present'):
merge([t1, t2], 'error_on_overlapping_feature')
def test_invalid_overlapping_sample_ids(self):
t1 = Table(np.array([[0, 1, 3], [1, 1, 2]]), ['O1', 'O2'],
['S1', 'S2', 'S3'])
t2 = Table(np.array([[0, 2, 6], [2, 2, 4]]), ['O1', 'O3'],
['S1', 'S5', 'S6'])
with self.assertRaisesRegex(ValueError, 'samples.*S1'):
merge([t1, t2])
def test_table_based_filtering_exclude_ids(self):
# filter all
table = Table(np.array([[0, 1, 3], [1, 1, 2], [42, 3, 0], [0, 0, 0]]),
['O1', 'O2', 'O3', 'O4'],
['S1', 'S2', 'S3'])
with self.assertRaisesRegex(ValueError, 'All.*filtered'):
obs = filter_seqs(self.seqs, table=table, exclude_ids=True)
# filter all (extra ids in table is ok)
table = Table(np.array([[0, 1, 3], [1, 1, 2],
[42, 3, 0], [0, 0, 0], [1, 0, 0]]),
['O1', 'O2', 'O3', 'O4', 'O5'],
['S1', 'S2', 'S3'])
with self.assertRaisesRegex(ValueError, 'All.*filtered'):
obs = filter_seqs(self.seqs, table=table, exclude_ids=True)
# filter three
table = Table(np.array([[1, 1, 2], [42, 3, 0], [0, 0, 0]]),
['O2', 'O3', 'O4'],
['S1', 'S2', 'S3'])
obs = filter_seqs(self.seqs, table=table, exclude_ids=True)
exp = pd.Series(['ACGT'], index=['O1'])
assert_series_equal(obs, exp)
# filter none
table = Table(np.array([[0, 1, 3], [1, 1, 2], [42, 3, 0], [0, 0, 0]]),
['O1-alt', 'O2-alt', 'O3-alt', 'O4-alt'],
['S1', 'S2', 'S3'])
obs = filter_seqs(self.seqs, table=table, exclude_ids=True)
assert_series_equal(obs, self.seqs)
def setUp(self):
"""define some top-level data"""
self.output_dir = '/tmp/'
otu_table_vals = array([[0, 0], [1, 5]])
self.otu_table = Table(
otu_table_vals,
['OTU1', 'OTU2'],
['Sample1', 'Sample2'],
[{
"taxonomy": ["Bacteria"]
}, {
"taxonomy": ["Archaea"]
}],
[None, None],
)
filt_otu_table_vals = array([[1, 5]])
self.filt_otu_table = Table(filt_otu_table_vals, ['OTU2'],
['Sample1', 'Sample2'], [{
"taxonomy": ["Archaea"]
}], [None, None])
self.num_otu_hits = 5
self._folders_to_cleanup = []
def test_combine_id_and_frequency_filters(self):
# no filtering
df = pd.DataFrame({'Subject': ['subject-1', 'subject-1', 'subject-2'],
'SampleType': ['gut', 'tongue', 'gut']},
index=pd.Index(['S1', 'S2', 'S3'], name='#SampleID'))
metadata = qiime2.Metadata(df)
table = Table(np.array([[0, 1, 3], [1, 1, 2]]),
['O1', 'O2'],
['S1', 'S2', 'S3'])
where = "Subject='subject-1' OR Subject='subject-2'"
actual = filter_samples(table, metadata=metadata, where=where,
min_frequency=1)
expected = Table(np.array([[0, 1, 3], [1, 1, 2]]),
['O1', 'O2'],
['S1', 'S2', 'S3'])
self.assertEqual(actual, expected)
# id and frequency filters active
df = pd.DataFrame({'Subject': ['subject-1', 'subject-1', 'subject-2'],
'SampleType': ['gut', 'tongue', 'gut']},
index=pd.Index(['S1', 'S2', 'S3'], name='#SampleID'))
metadata = qiime2.Metadata(df)
table = Table(np.array([[0, 1, 3], [1, 1, 2]]),
['O1', 'O2'],
['S1', 'S2', 'S3'])
where = "Subject='subject-1'"
actual = filter_samples(table, metadata=metadata, where=where,
min_frequency=2)
expected = Table(np.array([[1], [1]]),
['O1', 'O2'],
['S2'])
self.assertEqual(actual, expected)
def test_sum_triple_overlap(self):
t1 = Table(np.array([[1, 1, 1], [1, 1, 1]]), ['O1', 'O2'],
['S1', 'S2', 'S3'])
obs = merge([t1] * 3, 'sum')
exp = Table(np.array([[3, 3, 3], [3, 3, 3]]), ['O1', 'O2'],
['S1', 'S2', 'S3'])
self.assertEqual(obs, exp)
def setUp(self):
self.tmp_dir = get_qiime_temp_dir()
self.l19_data = np.array([[7, 1, 0, 0, 0, 0, 0, 0, 0],
[4, 2, 0, 0, 0, 1, 0, 0, 0],
[2, 4, 0, 0, 0, 1, 0, 0, 0],
[1, 7, 0, 0, 0, 0, 0, 0, 0],
[0, 8, 0, 0, 0, 0, 0, 0, 0],
[0, 7, 1, 0, 0, 0, 0, 0, 0],
[0, 4, 2, 0, 0, 0, 2, 0, 0],
[0, 2, 4, 0, 0, 0, 1, 0, 0],
[0, 1, 7, 0, 0, 0, 0, 0, 0],
[0, 0, 8, 0, 0, 0, 0, 0, 0],
[0, 0, 7, 1, 0, 0, 0, 0, 0],
[0, 0, 4, 2, 0, 0, 0, 3, 0],
[0, 0, 2, 4, 0, 0, 0, 1, 0],
[0, 0, 1, 7, 0, 0, 0, 0, 0],
[0, 0, 0, 8, 0, 0, 0, 0, 0],
[0, 0, 0, 7, 1, 0, 0, 0, 0],
[0, 0, 0, 4, 2, 0, 0, 0, 4],
[0, 0, 0, 2, 4, 0, 0, 0, 1],
[0, 0, 0, 1, 7, 0, 0, 0, 0]])
self.l19_sample_names = [
'sam1', 'sam2', 'sam3', 'sam4', 'sam5', 'sam6', 'sam7', 'sam8',
'sam9', 'sam_middle', 'sam11', 'sam12', 'sam13', 'sam14', 'sam15',
'sam16', 'sam17', 'sam18', 'sam19'
]
self.l19_taxon_names = [
'tax1', 'tax2', 'tax3', 'tax4', 'endbigtaxon', 'tax6', 'tax7',
'tax8', 'tax9'
]
self.l19_taxon_names_w_underscore = [
'ta_x1', 'tax2', 'tax3', 'tax4', 'endbigtaxon', 'tax6', 'tax7',
'tax8', 'tax9'
]
l19 = Table(self.l19_data.T, self.l19_taxon_names,
self.l19_sample_names)
fd, self.l19_fp = mkstemp(dir=self.tmp_dir,
prefix='test_bdiv_otu_table',
suffix='.blom')
os.close(fd)
write_biom_table(l19, self.l19_fp)
l19_w_underscore = Table(self.l19_data.T,
self.l19_taxon_names_w_underscore,
self.l19_sample_names)
fd, self.l19_w_underscore_fp = mkstemp(dir=self.tmp_dir,
prefix='test_bdiv_otu_table',
suffix='.blom')
os.close(fd)
write_biom_table(l19_w_underscore, self.l19_w_underscore_fp)
self.l19_tree_str = '((((tax7:0.1,tax3:0.2):.98,tax8:.3, tax4:.3):.4,\
((tax1:0.3, tax6:.09):0.43,tax2:0.4):0.5):.2, (tax9:0.3, endbigtaxon:.08));'
self.l19_tree = parse_newick(self.l19_tree_str, PhyloNode)
self.files_to_remove = [self.l19_fp, self.l19_w_underscore_fp]
self.folders_to_remove = []
def setUp(self):
self.ambiguities_json = '%s/ambiguities/json' % ROOT
self.read_counts_diff = {
"10317.000001778.57016": 1,
"10317.000002860.57016": 2,
"10317.000002860.58862": 1
}
self.read_counts_equal = {
"10317.000001778.57016": 1,
"10317.000002860.57016": 1,
"10317.000002860.58862": 1
}
self.feat_counts = {
"10317.000001778.57016": 1,
"10317.000002860.57016": 1,
"10317.000002860.58862": 2
}
self.biom = Table(np.array([[1, 1, 1], [1, 1, 1]]), ['sp1', 'sp2'], [
"10317.000001778.57016", "10317.000002860.57016",
"10317.000002860.58862"
])
self.biom_diff = Table(
np.array([[1, 1], [1, 1]]), ['sp1', 'sp2'],
["10317.000001778.57016", "10317.000002860.57016"])
self.biom_equal = Table(
np.array([[1, 1], [1, 1]]), ['sp1', 'sp2'],
["10317.000001778.57016", "10317.000002860.58862"])
def setUp(self):
self.sample_metadata_1 = \
{'s1': {'source_sink': 'source1', 'cat2': 'random_nonsense'},
's2': {'source_sink': 'sink', 'cat2': 'sink'},
's5': {'source_sink': 'source1', 'cat2': 'random_nonsense'},
's0': {'source_sink': 'source2', 'cat2': 'random_nonsense'},
's100': {'source_sink': 'sink', 'cat2': 'sink'}}
# Data for testing sinks_and_sources
self.sample_metadata_2 = \
{'s1': {'SourceSink': 'source', 'Env': 'source1'},
's2': {'SourceSink': 'sink', 'Env': 'e1'},
's5': {'SourceSink': 'source', 'Env': 'source1'},
's0': {'SourceSink': 'source', 'Env': 'source2'},
's100': {'SourceSink': 'sink', 'Env': 'e2'}}
self.sample_metadata_3 = \
{'s1': {'SourceSink': 'source', 'Env': 'source1'},
's2': {'SourceSink': 'source', 'Env': 'e1'},
's5': {'SourceSink': 'source', 'Env': 'source1'},
's0': {'SourceSink': 'source', 'Env': 'source2'},
's100': {'SourceSink': 'source', 'Env': 'e2'}}
# Data for testing _cli_sync_biom_and_sample_metadata
oids = ['o1', 'o2', 'o3']
# Data for an example where samples are removed from biom table only.
sids = ['Sample1', 'Sample2', 'Sample3', 'Sample4']
bt_1_data = np.arange(12).reshape(3, 4)
self.bt_1_in = Table(bt_1_data, oids, sids)
self.bt_1_out = Table(bt_1_data[:, :-1], oids, sids[:-1])
self.mf_1_in = \
{'Sample1': {'cat1': 'X', 'cat2': 'Y'},
'Sample2': {'cat1': 'X', 'cat2': 'Y'},
'Sample3': {'cat1': 'X', 'cat2': 'Y'}}
self.mf_1_out = self.mf_1_in
# Data for an example where sample are removed from mapping file only.
self.bt_2_in = self.bt_1_in
self.bt_2_out = self.bt_1_in
self.mf_2_in = \
{'Sample1': {'cat1': 'X', 'cat2': 'Y'},
'Sample6': {'cat1': 'X', 'cat2': 'Y'},
'Sample3': {'cat1': 'X', 'cat2': 'Y'},
'Sample4': {'cat1': 'X', 'cat2': 'Y'},
'Sample2': {'cat1': 'X', 'cat2': 'Y'}}
self.mf_2_out = \
{'Sample1': {'cat1': 'X', 'cat2': 'Y'},
'Sample3': {'cat1': 'X', 'cat2': 'Y'},
'Sample4': {'cat1': 'X', 'cat2': 'Y'},
'Sample2': {'cat1': 'X', 'cat2': 'Y'}}
# Data for an example where samples are removed from mapping file and
# biom file.
sids = ['Sample1', 'sampleA', 'Sample3', 'Sample4']
bt_3_data = np.arange(12).reshape(3, 4)
self.bt_3_in = Table(bt_3_data, oids, sids)
self.bt_3_out = Table(bt_1_data[:, [0, 2, 3]], oids,
[sids[0], sids[2], sids[3]])
self.mf_3_in = self.mf_2_out
self.mf_3_out = \
{'Sample1': {'cat1': 'X', 'cat2': 'Y'},
'Sample3': {'cat1': 'X', 'cat2': 'Y'},
'Sample4': {'cat1': 'X', 'cat2': 'Y'}}
def test_sum_full_overlap(self):
t1 = Table(np.array([[0, 1, 3], [1, 1, 2]]), ['O1', 'O2'],
['S1', 'S2', 'S3'])
t2 = Table(np.array([[0, 2, 6], [2, 2, 4]]), ['O1', 'O2'],
['S1', 'S2', 'S3'])
obs = merge([t1, t2], 'sum')
exp = Table(np.array([[0, 3, 9], [3, 3, 6]]), ['O1', 'O2'],
['S1', 'S2', 'S3'])
self.assertEqual(obs, exp)
def test_average_relative_frequency(self):
t1 = Table(np.array([[0.75, 0.75, 0.75], [0.75, 0.75, 0.75]]),
['O1', 'O2'], ['S1', 'S2', 'S3'])
t2 = Table(np.array([[0.25, 0.25, 0.25], [0.25, 0.25, 0.25]]),
['O1', 'O2'], ['S1', 'S2', 'S3'])
obs = merge([t1, t2], 'average')
exp = Table(np.array([[0.5, 0.5, 0.5], [0.5, 0.5, 0.5]]), ['O1', 'O2'],
['S1', 'S2', 'S3'])
self.assertEqual(obs, exp)
def test_sum_some_overlap(self):
# Did I stutter?
t1 = Table(np.array([[0, 1, 3], [1, 1, 2]]), ['O1', 'O2'],
['S1', 'S2', 'S3'])
t2 = Table(np.array([[0, 2, 6], [2, 2, 4]]), ['O1', 'O3'],
['S4', 'S2', 'S5'])
obs = merge([t1, t2], 'sum')
exp = Table(
np.array([[0, 3, 3, 0, 6], [1, 1, 2, 0, 0], [0, 2, 0, 2, 4]]),
['O1', 'O2', 'O3'], ['S1', 'S2', 'S3', 'S4', 'S5'])
self.assertEqual(obs, exp)
def test_valid_overlapping_sample_ids(self):
t1 = Table(np.array([[0, 1, 3], [1, 1, 2]]), ['O1', 'O2'],
['S1', 'S2', 'S3'])
t2 = Table(np.array([[0, 2, 6], [2, 2, 4]]), ['O3', 'O4'],
['S1', 'S5', 'S6'])
obs = merge([t1, t2], 'error_on_overlapping_feature')
exp = Table(
np.array([[0, 1, 3, 0, 0], [1, 1, 2, 0, 0], [0, 0, 0, 2, 6],
[2, 0, 0, 2, 4]]), ['O1', 'O2', 'O3', 'O4'],
['S1', 'S2', 'S3', 'S5', 'S6'])
self.assertEqual(obs, exp)
def setUp(self):
"""define some top-level data"""
self.otu_table_values = array([[0, 0, 9, 5, 3, 1], [1, 5, 4, 0, 3, 2],
[2, 3, 1, 1, 2, 5]])
{
(0, 2): 9.0,
(0, 3): 5.0,
(0, 4): 3.0,
(0, 5): 1.0,
(1, 0): 1.0,
(1, 1): 5.0,
(1, 2): 4.0,
(1, 4): 3.0,
(1, 5): 2.0,
(2, 0): 2.0,
(2, 1): 3.0,
(2, 2): 1.0,
(2, 3): 1.0,
(2, 4): 2.0,
(2, 5): 5.0
}
self.otu_table = Table(
self.otu_table_values, ['OTU1', 'OTU2', 'OTU3'],
['Sample1', 'Sample2', 'Sample3', 'Sample4', 'Sample5', 'Sample6'],
[{
"taxonomy": ['Bacteria']
}, {
"taxonomy": ['Archaea']
}, {
"taxonomy": ['Streptococcus']
}], [None, None, None, None, None, None])
self.otu_table_f = Table(
self.otu_table_values, ['OTU1', 'OTU2', 'OTU3'],
['Sample1', 'Sample2', 'Sample3', 'Sample4', 'Sample5', 'Sample6'],
[{
"taxonomy": ['1A', '1B', '1C', 'Bacteria']
}, {
"taxonomy": ['2A', '2B', '2C', 'Archaea']
}, {
"taxonomy": ['3A', '3B', '3C', 'Streptococcus']
}], [None, None, None, None, None, None])
self.full_lineages = [['1A', '1B', '1C', 'Bacteria'],
['2A', '2B', '2C', 'Archaea'],
['3A', '3B', '3C', 'Streptococcus']]
self.metadata = [[['Sample1', 'NA', 'A'], ['Sample2', 'NA', 'B'],
['Sample3', 'NA', 'A'], ['Sample4', 'NA', 'B'],
['Sample5', 'NA', 'A'], ['Sample6', 'NA', 'B']],
['SampleID', 'CAT1', 'CAT2'], []]
self.tree_text = ["('OTU3',('OTU1','OTU2'))"]
fh, self.tmp_heatmap_fpath = mkstemp(prefix='test_heatmap_',
suffix='.pdf')
close(fh)
def setUp(self):
THIS_DIR = os.path.dirname(os.path.abspath(__file__))
table = Table({}, [], [])
self.emptyfeatures = table
table = Table({}, ['a', 'b', 'c'], [])
self.wrongtips = table
self.goodtable = os.path.join(THIS_DIR, 'data/features_formated.biom')
self.goodcsi = os.path.join(THIS_DIR, 'data/goodcsi')
self.goodthresh = 0.5
tablefp = collate_fingerprint(self.goodcsi)
treeout = make_hierarchy(tablefp, prob_threshold=self.goodthresh)
self.goodtree = treeout
def test_sum_overlapping_feature_ids(self):
# This should produce the same result as `error_on_overlapping_sample`
t1 = Table(np.array([[0, 1, 3], [1, 1, 2]]), ['O1', 'O2'],
['S1', 'S2', 'S3'])
t2 = Table(np.array([[0, 2, 6], [2, 2, 4]]), ['O1', 'O3'],
['S4', 'S5', 'S6'])
obs = merge([t1, t2], 'sum')
exp = Table(
np.array([[0, 1, 3, 0, 2, 6], [1, 1, 2, 0, 0, 0],
[0, 0, 0, 2, 2, 4]]), ['O1', 'O2', 'O3'],
['S1', 'S2', 'S3', 'S4', 'S5', 'S6'])
self.assertEqual(obs, exp)
def test_get_overlapping_no_overlap(self):
t1 = Table(np.array([[0, 1, 3], [1, 1, 2]]), ['O1', 'O2'],
['S1', 'S2', 'S3'])
t2 = Table(np.array([[0, 2, 6], [2, 2, 4]]), ['O3', 'O4'],
['S4', 'S5', 'S6'])
# samples
obs = _get_overlapping([t1, t2], 'sample')
self.assertEqual(set(), obs)
# features
obs = _get_overlapping([t1, t2], 'observation')
self.assertEqual(set(), obs)
def test_sample_metadata_extra_ids(self):
df = pd.DataFrame({'Subject': ['subject-1', 'subject-1', 'subject-2'],
'SampleType': ['gut', 'tongue', 'gut']},
index=['S-not-in-table', 'S2', 'S3'])
metadata = qiime2.Metadata(df)
table = Table(np.array([[0, 1, 3], [1, 1, 2]]),
['O1', 'O2'],
['S1', 'S2', 'S3'])
actual = filter_samples(table, sample_metadata=metadata)
expected = Table(np.array([[1, 3], [1, 2]]),
['O1', 'O2'],
['S2', 'S3'])
self.assertEqual(actual, expected)
def test_feature_metadata(self):
# no filtering
df = pd.DataFrame({'SequencedGenome': ['yes', 'yes']},
index=pd.Index(['O1', 'O2'], name='id'))
metadata = qiime2.Metadata(df)
table = Table(np.array([[0, 1, 3], [1, 1, 2]]), ['O1', 'O2'],
['S1', 'S2', 'S3'])
actual = filter_features(table, metadata=metadata)
expected = Table(np.array([[0, 1, 3], [1, 1, 2]]), ['O1', 'O2'],
['S1', 'S2', 'S3'])
self.assertEqual(actual, expected)
# filter one
df = pd.DataFrame({'SequencedGenome': ['yes']},
index=pd.Index(['O1'], name='id'))
metadata = qiime2.Metadata(df)
table = Table(np.array([[0, 1, 3], [1, 1, 2]]), ['O1', 'O2'],
['S1', 'S2', 'S3'])
actual = filter_features(table, metadata=metadata)
expected = Table(np.array([[1, 3]]), ['O1'], ['S2', 'S3'])
self.assertEqual(actual, expected)
# filter all
df = pd.DataFrame({}, index=pd.Index(['foo'], name='id'))
metadata = qiime2.Metadata(df)
table = Table(np.array([[0, 1, 3], [1, 1, 2]]), ['O1', 'O2'],
['S1', 'S2', 'S3'])
actual = filter_features(table, metadata=metadata)
expected = Table(np.array([]), [], [])
self.assertEqual(actual, expected)
# exclude one
df = pd.DataFrame({'SequencedGenome': ['yes']},
index=pd.Index(['O1'], name='id'))
metadata = qiime2.Metadata(df)
table = Table(np.array([[0, 1, 3], [1, 1, 2]]), ['O1', 'O2'],
['S1', 'S2', 'S3'])
actual = filter_features(table, metadata=metadata, exclude_ids=True)
expected = Table(np.array([[1, 1, 2]]), ['O2'], ['S1', 'S2', 'S3'])
self.assertEqual(actual, expected)
# exclude all
df = pd.DataFrame({'SequencedGenome': ['yes', 'yes']},
index=pd.Index(['O1', 'O2'], name='id'))
metadata = qiime2.Metadata(df)
table = Table(np.array([[0, 1, 3], [1, 1, 2]]), ['O1', 'O2'],
['S1', 'S2', 'S3'])
actual = filter_features(table, metadata=metadata, exclude_ids=True)
expected = Table(np.array([]), [], [])
self.assertEqual(actual, expected)
def test_max_frequency(self):
# no filtering
table = Table(np.array([[0, 1, 3], [1, 1, 2]]), ['O1', 'O2'],
['S1', 'S2', 'S3'])
actual = filter_samples(table, max_frequency=42)
expected = Table(np.array([[0, 1, 3], [1, 1, 2]]), ['O1', 'O2'],
['S1', 'S2', 'S3'])
self.assertEqual(actual, expected)
# filter one
table = Table(np.array([[0, 1, 3], [1, 1, 2]]), ['O1', 'O2'],
['S1', 'S2', 'S3'])
actual = filter_samples(table, max_frequency=4)
expected = Table(np.array([[0, 1], [1, 1]]), ['O1', 'O2'],
['S1', 'S2'])
self.assertEqual(actual, expected)
# filter two
table = Table(np.array([[0, 1, 3], [1, 1, 2]]), ['O1', 'O2'],
['S1', 'S2', 'S3'])
actual = filter_samples(table, max_frequency=1)
expected = Table(np.array([[1]]), ['O2'], ['S1'])
self.assertEqual(actual, expected)
# filter all
table = Table(np.array([[0, 1, 3], [1, 1, 2]]), ['O1', 'O2'],
['S1', 'S2', 'S3'])
actual = filter_samples(table, max_frequency=0)
expected = Table(np.array([]), [], [])
self.assertEqual(actual, expected)
def test_get_overlapping_multiple(self):
t1 = Table(np.array([[0, 1, 3], [1, 1, 2]]), ['O1', 'O2'],
['S1', 'S2', 'S3'])
t2 = Table(np.array([[0, 2, 6], [2, 2, 4]]), ['O1', 'O3'],
['S1', 'S5', 'S6'])
t3 = Table(np.array([[3, 3, 1], [0, 2, 1]]), ['O1', 'O2'],
['S1', 'S3', 'S6'])
# samples
obs = _get_overlapping([t1, t2, t3], 'sample')
self.assertEqual({'S1', 'S3', 'S6'}, obs)
# features
obs = _get_overlapping([t1, t2, t3], 'observation')
self.assertEqual({'O1', 'O2'}, obs)
def test_where(self):
# no filtering
df = pd.DataFrame({'Subject': ['subject-1', 'subject-1', 'subject-2'],
'SampleType': ['gut', 'tongue', 'gut']},
index=pd.Index(['S1', 'S2', 'S3'], name='#SampleID'))
metadata = qiime2.Metadata(df)
table = Table(np.array([[0, 1, 3], [1, 1, 2]]),
['O1', 'O2'],
['S1', 'S2', 'S3'])
where = "Subject='subject-1' OR Subject='subject-2'"
actual = filter_samples(table, sample_metadata=metadata, where=where)
expected = Table(np.array([[0, 1, 3], [1, 1, 2]]),
['O1', 'O2'],
['S1', 'S2', 'S3'])
self.assertEqual(actual, expected)
# filter one
df = pd.DataFrame({'Subject': ['subject-1', 'subject-1', 'subject-2'],
'SampleType': ['gut', 'tongue', 'gut']},
index=pd.Index(['S1', 'S2', 'S3'], name='#SampleID'))
metadata = qiime2.Metadata(df)
table = Table(np.array([[0, 1, 3], [1, 1, 2]]),
['O1', 'O2'],
['S1', 'S2', 'S3'])
where = "Subject='subject-1'"
actual = filter_samples(table, sample_metadata=metadata, where=where)
expected = Table(np.array([[0, 1], [1, 1]]),
['O1', 'O2'],
['S1', 'S2'])
self.assertEqual(actual, expected)
# filter two
df = pd.DataFrame({'Subject': ['subject-1', 'subject-1', 'subject-2'],
'SampleType': ['gut', 'tongue', 'gut']},
index=pd.Index(['S1', 'S2', 'S3'], name='#SampleID'))
metadata = qiime2.Metadata(df)
table = Table(np.array([[0, 1, 3], [1, 1, 2]]),
['O1', 'O2'],
['S1', 'S2', 'S3'])
where = "Subject='subject-1' AND SampleType='gut'"
actual = filter_samples(table, sample_metadata=metadata, where=where)
expected = Table(np.array([[1]]),
['O2'],
['S1'])
self.assertEqual(actual, expected)
# filter all
df = pd.DataFrame({'Subject': ['subject-1', 'subject-1', 'subject-2'],
'SampleType': ['gut', 'tongue', 'gut']},
index=pd.Index(['S1', 'S2', 'S3'], name='#SampleID'))
metadata = qiime2.Metadata(df)
table = Table(np.array([[0, 1, 3], [1, 1, 2]]),
['O1', 'O2'],
['S1', 'S2', 'S3'])
where = "Subject='subject-1' AND Subject='subject-2'"
actual = filter_samples(table, sample_metadata=metadata, where=where)
expected = Table(np.array([]), [], [])
self.assertEqual(actual, expected)
def setUp(self):
self.qiime_config = load_qiime_config()
self.tmp_dir = self.qiime_config['temp_dir'] or '/tmp/'
self.otu_table_data = np.array([[2, 1, 0], [0, 5, 0], [0, 3, 0],
[1, 2, 0]])
self.sample_names = list('YXZ')
self.taxon_names = list('bacd')
self.otu_metadata = [{
'domain': 'Archaea'
}, {
'domain': 'Bacteria'
}, {
'domain': 'Bacteria'
}, {
'domain': 'Bacteria'
}]
self.otu_table = Table(self.otu_table_data,
self.taxon_names,
self.sample_names,
observation_metadata=[{}, {}, {}, {}],
sample_metadata=[{}, {}, {}])
self.otu_table_meta = Table(self.otu_table_data,
self.taxon_names,
self.sample_names,
observation_metadata=self.otu_metadata)
fd, self.otu_table_fp = mkstemp(dir=self.tmp_dir,
prefix='test_rarefaction',
suffix='.biom')
close(fd)
fd, self.otu_table_meta_fp = mkstemp(dir=self.tmp_dir,
prefix='test_rarefaction',
suffix='.biom')
close(fd)
self.rare_dir = mkdtemp(dir=self.tmp_dir,
prefix='test_rarefaction_dir',
suffix='')
write_biom_table(self.otu_table, self.otu_table_fp)
write_biom_table(self.otu_table_meta, self.otu_table_meta_fp)
self._paths_to_clean_up = [self.otu_table_fp, self.otu_table_meta_fp]
self._dirs_to_clean_up = [self.rare_dir]
def table_from_template(new_data,sample_ids,observation_ids,\
sample_metadata_source=None,observation_metadata_source=None,\
verbose=False):
"""Build a new BIOM table from new_data, and transfer metadata from 1-2 existing tables"""
#Build the BIOM table
result_table = Table(new_data,
observation_ids,
sample_ids,
type='Gene table')
#Transfer sample metadata from the OTU table
#to the metagenome table (samples are the same)
if sample_metadata_source:
result_table = transfer_metadata(sample_metadata_source,result_table,\
donor_metadata_type='sample',\
recipient_metadata_type='sample',verbose=verbose)
#Now transfer observation metadata (e.g. gene metadata)
#from the genome table to the result table
if observation_metadata_source:
result_table = transfer_metadata(observation_metadata_source,\
result_table,donor_metadata_type='observation',\
recipient_metadata_type='observation',verbose=verbose)
return result_table
def test_non_phylogenetic_invalid_input(self):
t = Table(np.array([[0, 1, 3], [1, 1, 2]]), ['O1', 'O2'],
['S1', 'S2', 'S3'])
tree = skbio.TreeNode.read(
io.StringIO('((O1:0.25, O2:0.50):0.25, O3:0.75)root;'))
with self.assertRaises(TypeError):
beta_diversity('bray_curtis', t, phylogeny=tree)
def test_generate_biom_table(self):
""" Test generating BIOM table
"""
seqs = [("s1_80;size=3;", "AGTCGTACGTGCATGCA"),
("s1_0;size=3;", "TGTGTAGCTGTGCTGAT"),
("s1_10;size=3;", "CGGGTGCATGTCGTGAC")]
uc_output = """S\t0\t100\t*\t*\t*\t*\t*\ts1_80\t*
H\t0\t100\t100.0\t*\t0\t0\t*\ts1_81\ts1_80
H\t0\t100\t100.0\t*\t0\t0\t*\ts1_82\ts1_80
S\t1\t100\t*\t*\t*\t*\t*\ts1_0\t*
H\t1\t100\t100.0\t*\t0\t0\t*\ts1_1\ts1_0
H\t1\t100\t100.0\t*\t0\t0\t*\ts1_60\ts1_0
S\t2\t100\t*\t*\t*\t*\t*\ts1_10\t*
H\t2\t100\t100.0\t*\t0\t0\t*\ts1_12\ts1_10
H\t2\t100\t100.0\t*\t0\t0\t*\ts1_13\ts1_10
"""
data = {(2, 0): 3, (1, 0): 3, (0, 0): 3}
otu_ids = [
'CGGGTGCATGTCGTGAC', 'TGTGTAGCTGTGCTGAT', 'AGTCGTACGTGCATGCA'
]
sample_ids = ['s1']
seqs_fp = join(self.working_dir, "seqs.fasta")
with open(seqs_fp, 'w') as seqs_f:
for seq in seqs:
seqs_f.write(">%s\n%s\n" % seq)
# temporary file for .uc output
uc_output_fp = join(self.working_dir, "derep.uc")
with open(uc_output_fp, 'w') as uc_output_f:
uc_output_f.write(uc_output)
table_exp = Table(data, otu_ids, sample_ids, sample_metadata=None)
clusters, table = generate_biom_table(seqs_fp, uc_output_fp)
self.assertEqual(table, table_exp)
def setUp(self):
super().setUp()
self.beta = self.plugin.pipelines['beta']
self.beta_phylogenetic = self.plugin.pipelines['beta_phylogenetic']
two_feature_table = self.get_data_path('two_feature_table.biom')
self.two_feature_table = Artifact.import_data(
'FeatureTable[Frequency]',
two_feature_table)
three_feature_tree = self.get_data_path('three_feature.tree')
self.three_feature_tree = Artifact.import_data('Phylogeny[Rooted]',
three_feature_tree)
crawford_table = self.get_data_path('crawford.biom')
self.crawford_table = Artifact.import_data('FeatureTable[Frequency]',
crawford_table)
crawford_tree = self.get_data_path('crawford.nwk')
self.crawford_tree = Artifact.import_data('Phylogeny[Rooted]',
crawford_tree)
t = Table(np.array([[0, 1, 3], [1, 1, 2]]),
['O1', 'O2'],
['S1', 'S2', 'S3'])
self.t = Artifact.import_data('FeatureTable[Frequency]', t)
tree = skbio.TreeNode.read(io.StringIO(
'((O1:0.25, O2:0.50):0.25, O3:0.75)root;'))
self.tree = Artifact.import_data('Phylogeny[Rooted]', tree)
def create_biom_table(sample_counts, taxa):
"""
Create a BIOM table from sample counts and taxonomy metadata.
:type sample_counts: dict
:param sample_counts: A dictionary of dictionaries with the first level
keyed on sample ID, and the second level keyed on
taxon ID with counts as values.
:type taxa: dict
:param taxa: A mapping between the taxon IDs from sample_counts to the
full representation of the taxonomy string. The values in
this dict will be used as metadata in the BIOM table.
:rtype: biom.Table
:return: A BIOM table containing the per-sample taxon counts and full
taxonomy identifiers as metadata for each taxon.
"""
data = [[
0 if taxid not in sample_counts[sid] else sample_counts[sid][taxid]
for sid in sample_counts
] for taxid in taxa]
data = np.array(data, dtype=int)
tax_meta = [{'taxonomy': taxa[taxid]} for taxid in taxa]
gen_str = "kraken-biom v{} ({})".format(__version__, __url__)
return Table(data,
list(taxa),
list(sample_counts),
tax_meta,
type="OTU table",
create_date=str(dt.now().isoformat()),
generated_by=gen_str,
input_is_dense=True)
