def test_missing_feature_ids(self):
feature_mc = qiime2.CategoricalMetadataColumn(
pd.Series(['g0', 'g1', 'g2', 'g1', 'g2', 'extra'],
name='foo',
index=pd.Index(['a', 'c', 'd', 'e', 'f', 'g'],
name='featureid')))
data = np.array([[1, 0, 0], [1, 10, 10], [0, 0, 100], [5, 5, 5],
[0, 1, 100], [7, 8, 9]])
# g is missing on purpose
table = biom.Table(data,
sample_ids=['s1', 's2', 's3'],
observation_ids=['a', 'b', 'c', 'd', 'e', 'f'])
with self.assertRaisesRegex(ValueError, "not present.*'b'"):
group(table, axis='feature', metadata=feature_mc, mode='sum')
def test_extra_metadata(self):
dm = skbio.DistanceMatrix(
[[0.00, 0.25, 0.25], [0.25, 0.00, 0.00], [0.25, 0.00, 0.00]],
ids=['sample1', 'sample2', 'sample3'])
md = qiime2.CategoricalMetadataColumn(
pd.Series(['a', 'b', 'b', 'c'],
name='a or b',
index=pd.Index(
['sample1', 'sample2', 'sample3', 'sample4'],
name='id')))
with tempfile.TemporaryDirectory() as output_dir:
beta_group_significance(output_dir, dm, md, permutations=42)
index_fp = os.path.join(output_dir, 'index.html')
self.assertTrue('<td>2</td>' in open(index_fp).read())
def test_filtered_samples_str_metadata(self):
dm = skbio.DistanceMatrix(
[[0.00, 0.25, 0.25, 0.66], [0.25, 0.00, 0.00, 0.66],
[0.25, 0.00, 0.00, 0.66], [0.66, 0.66, 0.66, 0.00]],
ids=['sample1', 'sample2', 'sample3', 'sample4'])
md = qiime2.CategoricalMetadataColumn(
pd.Series(['a', 'b', 'b', np.nan],
name='a or b',
index=pd.Index(
['sample1', 'sample2', 'sample3', 'sample4'],
name='id')))
with tempfile.TemporaryDirectory() as output_dir:
beta_group_significance(output_dir, dm, md)
index_fp = os.path.join(output_dir, 'index.html')
self.assertTrue('Warning' in open(index_fp).read())
def test_empty_metadata_values(self):
# Trusting that the code is sane enough to not invent a distinction
# between feature and sample metadata where there is none
sample_mc = qiime2.CategoricalMetadataColumn(
pd.Series(['a_new', 'a_new', None],
name='foo',
index=pd.Index(['a', 'b', 'c'], name='sampleid')))
sample_ids = sample_mc.to_series().index
data = np.array([[1, 2, 3], [30, 20, 10]])
table = biom.Table(data,
sample_ids=sample_ids,
observation_ids=['x', 'y'])
with self.assertRaisesRegex(ValueError, "missing.*value.*'c'"):
group(table, axis='sample', metadata=sample_mc, mode='sum')
nan_mc = qiime2.CategoricalMetadataColumn(
pd.Series(['a_new', float('nan'), 'a_new'],
name='foo',
index=pd.Index(['a', 'b', 'c'], name='id')))
with self.assertRaisesRegex(ValueError, "missing.*value.*'b'"):
group(table, axis='sample', metadata=nan_mc, mode='sum')
def setUp(self):
super().setUp()
self.md = qiime2.CategoricalMetadataColumn(
pd.Series(['a', 'a', 'a', 'b', 'b', 'b'],
index=pd.Index([c for c in 'abcdef'], name='id'),
name='foo'))
tab = biom.Table(np.array([[13, 26, 37, 3, 6,
1], [33, 24, 23, 5, 6, 2],
[38, 26, 33, 4, 1,
0], [3, 2, 1, 22, 25, 31],
[2, 1, 3, 44, 46, 42]]),
observation_ids=[c for c in 'vwxyz'],
sample_ids=[c for c in 'abcdef'])
self.tab = qiime2.Artifact.import_data('FeatureTable[Frequency]', tab)
def setUp(self):
super().setUp()
def _load_biom(table_fp):
table_fp = self.get_data_path(table_fp)
table = qiime2.Artifact.load(table_fp)
table = table.view(biom.Table)
return table
def _load_md(md_fp):
md_fp = self.get_data_path(md_fp)
md = pd.read_csv(md_fp, sep='\t', header=0, index_col=0)
md = qiime2.Metadata(md)
return md
def _load_nmc(md_fp, column):
md_fp = self.get_data_path(md_fp)
md = pd.read_csv(md_fp, sep='\t', header=0, index_col=0)
md = qiime2.NumericMetadataColumn(md[column])
return md
def _load_cmc(md_fp, column):
md_fp = self.get_data_path(md_fp)
md = pd.read_csv(md_fp, sep='\t', header=0, index_col=0)
md = qiime2.CategoricalMetadataColumn(md[column])
return md
self.table_chard_fp = _load_biom('chardonnay.table.qza')
self.md_chard_fp = _load_md('chardonnay.map.txt')
self.mdc_chard_fp = _load_cmc('chardonnay.map.txt', 'Region')
self.table_ecam_fp = _load_biom('ecam-table-maturity.qza')
self.md_ecam_fp = _load_md('ecam_map_maturity.txt')
self.mdc_ecam_fp = _load_nmc('ecam_map_maturity.txt', 'month')
self.exp_imp = pd.read_csv(
self.get_data_path('importance.tsv'), sep='\t', header=0,
index_col=0)
self.exp_pred = pd.read_csv(
self.get_data_path('predictions.tsv'), sep='\t', header=0,
index_col=0, squeeze=True)
index = pd.Index(['A', 'B', 'C', 'D'], name='id')
self.table_percnorm = qiime2.Artifact.import_data(
FeatureTable[PercentileNormalized], pd.DataFrame(
[[20.0, 20.0, 50.0, 10.0], [10.0, 10.0, 70.0, 10.0],
[90.0, 8.0, 1.0, 1.0], [30.0, 15.0, 20.0, 35.0]],
index=index,
columns=['feat1', 'feat2', 'feat3', 'feat4'])).view(biom.Table)
self.mdc_percnorm = qiime2.CategoricalMetadataColumn(
pd.Series(['X', 'X', 'Y', 'Y'], index=index, name='name'))
def setUp(self):
super().setUp()
self.exp_results = pd.read_csv(
self.get_data_path('mock-3-results.tsv'), sep='\t', index_col=0)
self.exp = qiime2.Artifact.load(
self.get_data_path('qc-mock-3-expected.qza')).view(pd.DataFrame)
self.obs = qiime2.Artifact.load(
self.get_data_path('qc-mock-3-observed.qza')).view(pd.DataFrame)
self.false_neg = pd.DataFrame(
{'HMPMockV1.1.Even1': [0.047619, 0.047619, 0.047619],
'HMPMockV1.1.Even2': [0.047619, 0.047619, 0.047619],
'HMPMockV1.2.Staggered1': [0.2143622714, 0.0214362274,
0.0002143626],
'HMPMockV1.2.Staggered2': [0.2143622714, 0.0214362274,
0.0002143626]},
index=['k__Bacteria;p__Firmicutes;c__Bacilli;o__Bacillales;'
'f__Staphylococcaceae;g__Staphylococcus;s__aureus',
'k__Bacteria;p__Firmicutes;c__Bacilli;o__Bacillales;'
'f__Staphylococcaceae;g__Staphylococcus;s__epidermidis',
'k__Bacteria;p__Thermi;c__Deinococci;o__Deinococcales;'
'f__Deinococcaceae;g__Deinococcus;s__'])
self.false_neg.index.name = 'Taxon'
self.misclassified = pd.DataFrame(
{'HMPMockV1.1.Even1': [0.08634],
'HMPMockV1.1.Even2': [0.0533176566813],
'HMPMockV1.2.Staggered1': [0.],
'HMPMockV1.2.Staggered2': [0.]},
index=['k__Bacteria;p__[Thermi];c__Deinococci;o__Deinococcales;'
'f__Deinococcaceae;g__Deinococcus;s__'])
self.misclassified.index.name = 'Taxon'
self.underclassified = pd.DataFrame(
{'HMPMockV1.1.Even1': [0.536876],
'HMPMockV1.1.Even2': [0.577293],
'HMPMockV1.2.Staggered1': [0.639295],
'HMPMockV1.2.Staggered2': [0.666156]},
index=['k__Bacteria;p__Firmicutes;c__Bacilli;o__Bacillales;'
'f__Staphylococcaceae;g__Staphylococcus;__'])
self.underclassified.index.name = 'Taxon'
self.metadata = qiime2.CategoricalMetadataColumn(
pd.Series(['HMPMockV1.1.Even1',
'HMPMockV1.1.Even1',
'HMPMockV1.2.Staggered1',
'HMPMockV1.2.Staggered1'], name='mock_id',
index=pd.Index(['HMPMockV1.1.Even1',
'HMPMockV1.1.Even2',
'HMPMockV1.2.Staggered1',
'HMPMockV1.2.Staggered2'], name='id')))
def setUp(self):
self.barcodes = [('@s1/2 abc/2', 'AAAA', '+', 'YYYY'),
('@s2/2 abc/2', 'TTAA', '+', 'PPPP'),
('@s3/2 abc/2', 'AACC', '+', 'PPPP'),
('@s4/2 abc/2', 'TTAA', '+', 'PPPP'),
('@s5/2 abc/2', 'AACC', '+', 'PPPP'),
('@s6/2 abc/2', 'AAAA', '+', 'PPPP'),
('@s7/2 abc/2', 'CGGC', '+', 'PPPP'),
('@s8/2 abc/2', 'GGAA', '+', 'PPPP'),
('@s9/2 abc/2', 'CGGC', '+', 'PPPP'),
('@s10/2 abc/2', 'CGGC', '+', 'PPPP'),
('@s11/2 abc/2', 'GGAA', '+', 'PPPP')]
self.forward = [('@s1/1 abc/1', 'GGG', '+', 'YYY'),
('@s2/1 abc/1', 'CCC', '+', 'PPP'),
('@s3/1 abc/1', 'AAA', '+', 'PPP'),
('@s4/1 abc/1', 'TTT', '+', 'PPP'),
('@s5/1 abc/1', 'ATA', '+', 'PPP'),
('@s6/1 abc/1', 'TAT', '+', 'PPP'),
('@s7/1 abc/1', 'CGC', '+', 'PPP'),
('@s8/1 abc/1', 'GCG', '+', 'PPP'),
('@s9/1 abc/1', 'ACG', '+', 'PPP'),
('@s10/1 abc/1', 'GCA', '+', 'PPP'),
('@s11/1 abc/1', 'TGA', '+', 'PPP')]
self.reverse = [('@s1/1 abc/1', 'CCC', '+', 'YYY'),
('@s2/1 abc/1', 'GGG', '+', 'PPP'),
('@s3/1 abc/1', 'TTT', '+', 'PPP'),
('@s4/1 abc/1', 'AAA', '+', 'PPP'),
('@s5/1 abc/1', 'TAT', '+', 'PPP'),
('@s6/1 abc/1', 'ATA', '+', 'PPP'),
('@s7/1 abc/1', 'GCG', '+', 'PPP'),
('@s8/1 abc/1', 'CGC', '+', 'PPP'),
('@s9/1 abc/1', 'CGT', '+', 'PPP'),
('@s10/1 abc/1', 'TGC', '+', 'PPP'),
('@s11/1 abc/1', 'TCA', '+', 'PPP')]
self.bpsi = BarcodePairedSequenceFastqIterator(self.barcodes,
self.forward,
self.reverse)
barcode_map = pd.Series(
['AAAA', 'AACC', 'TTAA', 'GGAA', 'CGGC'],
name='bc',
index=pd.Index(
['sample1', 'sample2', 'sample3', 'sample4', 'sample5'],
name='id'))
self.barcode_map = qiime2.CategoricalMetadataColumn(barcode_map)
def test_empty_only_in_superset(self):
# Trusting that the code is sane enough to not invent a distinction
# between feature and sample metadata where there is none
sample_mc = qiime2.CategoricalMetadataColumn(
pd.Series(['a_new', 'a_new', 'b_new', None], name='foo',
index=pd.Index(['a', 'b', 'c', 'd'], name='sampleid')))
data = np.array([[1, 2, 3], [30, 20, 10]])
table = biom.Table(data, sample_ids=['a', 'b', 'c'],
observation_ids=['x', 'y'])
expected = biom.Table(np.array([[2, 3], [25, 10]]),
sample_ids=['a_new', 'b_new'],
observation_ids=['x', 'y'])
result = group(table, axis='sample', metadata=sample_mc,
mode='mean-ceiling')
self.assertEqual(expected, result)
def test_seqs_restrict_metadata(self):
context_seqs = self.get_data_path('context-seqs-4.fasta')
context_seqs = DNAFASTAFormat(context_seqs, 'r')
s = pd.Series(['2019-11-01', '2020-01-17'], index=['B', 'U'])
s.index.name = 'id'
s.name = 'date-md'
exp_md = qiime2.CategoricalMetadataColumn(s)
for _ in range(self._N_TEST_ITERATIONS):
sel = sample_longitudinal(self.md2, context_seqs)
self.assertEqual(sel.inclusion.sum(), 2)
self.assertTrue(sel.inclusion['B'])
self.assertTrue(sel.inclusion['U'])
self.assertEqual(sel.metadata.get_column('date-md'), exp_md)
self.assertEqual(sel.label, 'sample_longitudinal')
def test_confusion_matrix_dtype_coercion(self):
predictions = pd.Series([1, 1, 1, 2, 2, 2],
index=pd.Index(['a', 'b', 'c', 'd', 'e', 'f'],
name='sample_id'),
name='features')
# NOTE: the targets are numbers but represented as str
truth = qiime2.CategoricalMetadataColumn(
pd.Series(['1', '2', '1', '2', '1', '2'],
index=pd.Index(['a', 'b', 'c', 'd', 'e', 'f'],
name='sample-id'),
name='target'))
confusion_matrix(self.tmpd, predictions, truth)
self.assertTrue('index.html' in listdir(self.tmpd))
def test_ancom_no_volcano_plot(self):
t = pd.DataFrame([[1, 1], [1, 1], [1, 1], [1, 1]],
index=['S1', 'S2', 'S3', 'S4'],
columns=['O1', 'O2'])
c = qiime2.CategoricalMetadataColumn(
pd.Series(['0', '0', '1', '2'],
name='n',
index=pd.Index(['S1', 'S2', 'S3', 'S4'], name='id')))
ancom(output_dir=self.temp_dir.name, table=t + 1, metadata=c)
index_fp = os.path.join(self.temp_dir.name, 'index.html')
self.assertTrue(os.path.exists(index_fp))
self.assertTrue(os.path.getsize(index_fp) > 0)
with open(index_fp) as fh:
f = fh.read()
self.assertTrue('Unable to generate volcano plot' in f)
def test_missing_sample_ids(self):
sample_mc = qiime2.CategoricalMetadataColumn(
pd.Series(['g0', 'g2', 'g0', 'g2'],
name='foo',
index=pd.Index(['s1', 's3', 's4', 's6'],
name='sampleid')))
data = np.array([[0, 1, 2, 3], [10, 11, 12, 13], [100, 110, 120, 130]])
table = biom.Table(data,
sample_ids=['s1', 's2', 's4', 's5'],
observation_ids=['x', 'y', 'z'])
with self.assertRaisesRegex(ValueError, 'not present.*s2.*s5') as e:
group(table, axis='sample', metadata=sample_mc, mode='sum')
self.assertIn('s2', str(e.exception))
self.assertIn('s5', str(e.exception))
def test_ancom_no_tables(self):
t = pd.DataFrame([[2, 1, 2], [2, 2, 2], [2, 2, 2]],
index=['S1', 'S2', 'S3'],
columns=['O1', 'O2', 'O3'])
c = qiime2.CategoricalMetadataColumn(
pd.Series(['0', '0', '1'],
name='n',
index=pd.Index(['S1', 'S2', 'S3'], name='id')))
ancom(output_dir=self.temp_dir.name, table=t + 1, metadata=c)
index_fp = os.path.join(self.temp_dir.name, 'index.html')
self.assertTrue(os.path.exists(index_fp))
self.assertTrue(os.path.getsize(index_fp) > 0)
with open(index_fp) as fh:
f = fh.read()
self.assertTrue('No significant features found' in f)
def test_numeric_strings(self):
data = np.array([[1, 2, 3], [30, 20, 10]])
table = biom.Table(data,
sample_ids=['a', 'b', 'c'],
observation_ids=['x', 'y'])
sample_mc = qiime2.CategoricalMetadataColumn(
pd.Series(['-4.2', '-4.2', '-4.2'],
name='foo',
index=pd.Index(['a', 'b', 'c'], name='sampleid')))
expected = biom.Table(np.array([[6], [60]]),
sample_ids=['-4.2'],
observation_ids=['x', 'y'])
result = group(table, axis='sample', metadata=sample_mc, mode='sum')
self.assertEqual(expected, result)
def test_superset_sample_group(self):
sample_mc = qiime2.CategoricalMetadataColumn(
pd.Series(['g0', 'g1', 'g2', 'g0', 'g1', 'g2'],
name='foo',
index=pd.Index(['s1', 's2', 's3', 's4', 's5', 's6'],
name='sampleid')))
data = np.array([[0, 1, 2, 3], [10, 11, 12, 13], [100, 110, 120, 130]])
table = biom.Table(data,
sample_ids=['s1', 's2', 's4', 's5'],
observation_ids=['x', 'y', 'z'])
expected = biom.Table(np.array([[2, 4], [22, 24], [220, 240]]),
sample_ids=['g0', 'g1'],
observation_ids=['x', 'y', 'z'])
result = group(table, axis='sample', metadata=sample_mc, mode='sum')
self.assertEqual(expected, result)
def test_ancom(self):
t = pd.DataFrame(
[[9, 9, 9, 19, 19, 19], [10, 11, 10, 20, 20, 20],
[9, 10, 9, 9, 10, 9], [9, 10, 9, 9, 9, 8], [9, 10, 9, 9, 9, 9],
[9, 10, 9, 9, 9, 10], [9, 12, 9, 9, 9, 11]],
index=['O1', 'O2', 'O3', 'O4', 'O5', 'O6', 'O7'],
columns=['S1', 'S2', 'S3', 'S4', 'S5', 'S6']).T
c = qiime2.CategoricalMetadataColumn(
pd.Series(['a', 'a', 'a', '1', '1', '1'],
name='n',
index=pd.Index(['S1', 'S2', 'S3', 'S4', 'S5', 'S6'],
name='id')))
ancom(output_dir=self.temp_dir.name, table=t + 1, metadata=c)
res = pd.read_csv(os.path.join(self.temp_dir.name, 'ancom.tsv'),
index_col=0,
sep='\t')
exp = pd.DataFrame(
{
'W':
np.array([5, 5, 2, 2, 2, 2, 2]),
'Reject null hypothesis':
np.array([True, True, False, False, False, False, False],
dtype=bool)
},
index=['O1', 'O2', 'O3', 'O4', 'O5', 'O6', 'O7'],
)
pdt.assert_frame_equal(res, exp)
index_fp = os.path.join(self.temp_dir.name, 'index.html')
self.assertTrue(os.path.exists(index_fp))
self.assertTrue(os.path.getsize(index_fp) > 0)
data_fp = os.path.join(self.temp_dir.name, 'data.tsv')
self.assertTrue(os.path.exists(data_fp))
self.assertTrue(os.path.getsize(data_fp) > 0)
tsv_fp = os.path.join(self.temp_dir.name, 'percent-abundances.tsv')
self.assertTrue(os.path.exists(tsv_fp))
self.assertTrue(os.path.getsize(tsv_fp) > 0)
with open(index_fp, 'r') as fh:
html = fh.read()
self.assertIn('<th>Percentile</th>', html)
self.assertIn('<th>Group</th>', html)
self.assertIn('<th>O1</th>', html)
def test_rev_comp_mapping_barcodes(self):
barcodes = pd.Series(
['TTTT', 'GGTT', 'TTAA', 'TTCC', 'GCCG'],
name='bc',
index=pd.Index(
['sample1', 'sample2', 'sample3', 'sample4', 'sample5'],
name='id'))
barcodes = qiime2.CategoricalMetadataColumn(barcodes)
actual = emp_single(self.bsi, barcodes, rev_comp_mapping_barcodes=True)
output_fastq = list(actual.sequences.iter_views(FastqGzFormat))
# five per-sample files were written
self.assertEqual(len(output_fastq), 5)
# sequences in sample1 are correct
self._validate_sample_fastq(output_fastq[0][1].open(), self.sequences,
[0, 5])
# sequences in sample2 are correct
self._validate_sample_fastq(output_fastq[1][1].open(), self.sequences,
[2, 4])
# sequences in sample3 are correct
self._validate_sample_fastq(output_fastq[2][1].open(), self.sequences,
[1, 3])
# sequences in sample4 are correct
self._validate_sample_fastq(output_fastq[3][1].open(), self.sequences,
[7, 10])
# sequences in sample5 are correct
self._validate_sample_fastq(output_fastq[4][1].open(), self.sequences,
[6, 8, 9])
# manifest is correct
act_manifest = list(actual.manifest.view(FastqManifestFormat).open())
exp_manifest = [
'sample-id,filename,direction\n',
'sample1,sample1_1_L001_R1_001.fastq.gz,forward\n',
'sample3,sample3_2_L001_R1_001.fastq.gz,forward\n',
'sample2,sample2_3_L001_R1_001.fastq.gz,forward\n',
'sample5,sample5_4_L001_R1_001.fastq.gz,forward\n',
'sample4,sample4_5_L001_R1_001.fastq.gz,forward\n'
]
self._compare_manifests(act_manifest, exp_manifest)
def test_superset_feature_group(self):
feature_mc = qiime2.CategoricalMetadataColumn(
pd.Series(['g0', 'g0', 'g1', 'g2', 'g1', 'g2', 'extra'],
name='foo',
index=pd.Index(['a', 'b', 'c', 'd', 'e', 'f', 'g'],
name='featureid')))
data = np.array([[1, 0, 0], [1, 10, 10], [0, 0, 100], [5, 5, 5],
[0, 1, 100], [7, 8, 9]])
# g is missing on purpose
table = biom.Table(data,
sample_ids=['s1', 's2', 's3'],
observation_ids=['a', 'b', 'c', 'd', 'e', 'f'])
expected = biom.Table(np.array([[2, 10, 10], [0, 1, 200], [12, 13,
14]]),
sample_ids=['s1', 's2', 's3'],
observation_ids=['g0', 'g1', 'g2'])
result = group(table, axis='feature', metadata=feature_mc, mode='sum')
self.assertEqual(expected, result)
def setUp(self):
super().setUp()
self.md = qiime2.CategoricalMetadataColumn(pd.Series(
['a', 'a', 'a', 'b', 'b', 'b'],
index=pd.Index([c for c in 'abcdef'], name='id'), name='foo'))
tab = biom.Table(np.array(
[[13, 26, 37, 3, 6, 1], [33, 24, 23, 5, 6, 2],
[38, 26, 33, 4, 1, 0], [3, 2, 1, 22, 25, 31],
[2, 1, 3, 44, 46, 42]]),
observation_ids=[c for c in 'vwxyz'],
sample_ids=[c for c in 'abcdef'])
self.tab = qiime2.Artifact.import_data('FeatureTable[Frequency]', tab)
dist = skbio.DistanceMatrix.from_iterable(
iterable=[1, 16, 2, 1, 16, 17],
metric=lambda x, y: abs(y-x), keys=[c for c in 'abcdef']
)
self.dist = qiime2.Artifact.import_data('DistanceMatrix', dist)
def test_ancom_zero_division(self):
t = pd.DataFrame([[10, 0], [11, 0], [12, 0], [13, 0],
[1000, 10], [1000, 10]],
index=['S1', 'S2', 'S3', 'S4', 'S5', 'S6'],
columns=['O1', 'O2'])
c = qiime2.CategoricalMetadataColumn(
pd.Series(['0', '0', '1', '1', '2', '2'], name='n',
index=pd.Index(['S1', 'S2', 'S3', 'S4', 'S5', 'S6'],
name='id'))
)
ancom(output_dir=self.temp_dir.name, table=t+1, metadata=c,
transform_function='log')
with open(os.path.join(self.temp_dir.name, 'index.html')) as fh:
f = fh.read()
self.assertFalse('Infinity' in f)
self.assertTrue(
'non-numeric results:\n <strong>O2</strong>' in f)
def test_subsample_higher_than_seqs_count(self):
barcodes = self.barcodes[:1]
sequences = self.sequences[:1]
bsi = BarcodeSequenceFastqIterator(barcodes, sequences)
barcode_map = pd.Series(['AAAA'],
name='bc',
index=pd.Index(['sample1'], name='id'))
barcode_map = qiime2.CategoricalMetadataColumn(barcode_map)
demux_data = emp_single(bsi, barcode_map)
with tempfile.TemporaryDirectory() as output_dir:
result = summarize(output_dir,
_PlotQualView(demux_data, paired=False),
n=50)
self.assertTrue(result is None)
plot_fp = os.path.join(output_dir, 'quality-plot.html')
with open(plot_fp, 'r') as fh:
html = fh.read()
self.assertIn('<strong>Warning:</strong>', html)
def test_basic(self):
bsi = BarcodeSequenceFastqIterator(self.barcodes, self.sequences)
barcode_map = pd.Series(['AAAA', 'AACC'],
name='bc',
index=pd.Index(['sample_1', 'sample2'],
name='id'))
barcode_map = qiime2.CategoricalMetadataColumn(barcode_map)
demux_data = emp_single(bsi, barcode_map)
# test that an index.html file is created and that it has size > 0
with tempfile.TemporaryDirectory() as output_dir:
# TODO: Remove _PlotQualView wrapper
result = summarize(output_dir,
_PlotQualView(demux_data, paired=False),
n=2)
self.assertTrue(result is None)
index_fp = os.path.join(output_dir, 'overview.html')
self.assertTrue(os.path.exists(index_fp))
self.assertTrue(os.path.getsize(index_fp) > 0)
csv_fp = os.path.join(output_dir, 'per-sample-fastq-counts.csv')
self.assertTrue(os.path.exists(csv_fp))
self.assertTrue(os.path.getsize(csv_fp) > 0)
pdf_fp = os.path.join(output_dir, 'demultiplex-summary.pdf')
self.assertTrue(os.path.exists(pdf_fp))
self.assertTrue(os.path.getsize(pdf_fp) > 0)
png_fp = os.path.join(output_dir, 'demultiplex-summary.png')
self.assertTrue(os.path.exists(png_fp))
self.assertTrue(os.path.getsize(png_fp) > 0)
qual_forward_fp = os.path.join(
output_dir, 'forward-seven-number-summaries.csv')
self.assertTrue(os.path.exists(qual_forward_fp))
self.assertTrue(os.path.getsize(qual_forward_fp) > 0)
with open(index_fp, 'r') as fh:
html = fh.read()
self.assertIn('<td>Minimum:</td><td>1</td>', html)
self.assertIn('<td>Maximum:</td><td>3</td>', html)
with open(csv_fp, 'r') as ch:
csv = ch.read()
self.assertIn('sample_1', csv)
def test_chain_with_metadata(self):
df = pd.DataFrame({'a': ['1', '2', '3']},
index=pd.Index(['0', '1', '2'], name='feature ID'))
a = qiime2.Artifact.import_data('IntSequence1', [1, 2, 3])
m = qiime2.Metadata(df)
mc = qiime2.CategoricalMetadataColumn(df['a'])
b = dummy_plugin.actions.identity_with_metadata(a, m).out
c = dummy_plugin.actions.identity_with_metadata_column(b, mc).out
p_dir = c._archiver.provenance_dir
new_m = qiime2.Metadata.load(
str(p_dir / 'artifacts' / str(b.uuid) / 'action' / 'metadata.tsv'))
pdt.assert_frame_equal(m.to_dataframe(), new_m.to_dataframe())
with (p_dir / 'action' / 'metadata.tsv').open() as fh:
self.assertEqual(
fh.read(),
'feature ID\ta\n#q2:types\tcategorical\n0\t1\n1\t2\n2\t3\n')
def setUp(self):
super().setUp()
md = pd.Series(['a', 'a', 'b', 'b', 'b'],
index=['a', 'b', 'c', 'd', 'e'], name='bugs')
md.index.name = 'SampleID'
self.md = qiime2.CategoricalMetadataColumn(md)
tab = biom.Table(
np.array([[3, 6, 7, 3, 6], [3, 4, 5, 6, 2], [8, 6, 4, 1, 0],
[8, 6, 4, 1, 0], [8, 6, 4, 1, 0]]),
observation_ids=['v', 'w', 'x', 'y', 'z'],
sample_ids=['a', 'b', 'c', 'd', 'e'])
self.tab = qiime2.Artifact.import_data('FeatureTable[Frequency]', tab)
md2 = pd.DataFrame({'trash': ['a', 'a', 'b', 'b', 'b', 'junk'],
'floats': [0.1, 0.1, 1.3, 1.8, 1000.1, 0.1],
'ints': [0, 1, 2, 2, 2, 0],
'nans': [1, 1, 2, 2, np.nan, np.nan],
'negatives': [-7, -3, -1.2, -4, -9, -1]},
index=['a', 'b', 'c', 'd', 'e', 'peanut'])
md2.index.name = 'SampleID'
self.md2 = qiime2.Metadata(md2)
def test_paired_end(self):
barcodes = self.barcodes[:3]
forward = self.sequences[:3]
reverse = [('@s1/1 abc/1', 'CCC', '+', 'YYY'),
('@s2/1 abc/1', 'GGG', '+', 'PPP'),
('@s3/1 abc/1', 'TTT', '+', 'PPP')]
bpsi = BarcodePairedSequenceFastqIterator(barcodes, forward, reverse)
barcode_map = pd.Series(['AAAA', 'AACC', 'TTAA'],
name='bc',
index=pd.Index(
['sample1', 'sample2', 'sample3'],
name='id'))
barcode_map = qiime2.CategoricalMetadataColumn(barcode_map)
demux_data = emp_paired(bpsi, barcode_map)
with tempfile.TemporaryDirectory() as output_dir:
result = summarize(output_dir,
_PlotQualView(demux_data, paired=True),
n=2)
self.assertTrue(result is None)
plot_fp = os.path.join(output_dir, 'quality-plot.html')
qual_forward_fp = os.path.join(
output_dir, 'forward-seven-number-summaries.csv')
self.assertTrue(os.path.exists(qual_forward_fp))
self.assertTrue(os.path.getsize(qual_forward_fp) > 0)
qual_reverse_fp = os.path.join(
output_dir, 'reverse-seven-number-summaries.csv')
self.assertTrue(os.path.exists(qual_reverse_fp))
self.assertTrue(os.path.getsize(qual_reverse_fp) > 0)
with open(plot_fp, 'r') as fh:
html = fh.read()
self.assertIn('<h5 class="text-center">Forward Reads</h5>',
html)
self.assertIn('<h5 class="text-center">Reverse Reads</h5>',
html)
def test_ancom_integer_indices(self):
# The idea behind this test is to use integer indices to confirm
# that the metadata column mapping is joining on labels, not on
# indices. If it was joining on the index, the metadata would map in
# the opposite direction, resulting in no significant results being
# rendered to the output HTML table.
t = pd.DataFrame(
[[9, 9, 9, 19, 19, 19], [10, 11, 10, 20, 20, 20],
[9, 10, 9, 9, 10, 9], [9, 10, 9, 9, 9, 8], [9, 10, 9, 9, 9, 9],
[9, 10, 9, 9, 9, 10], [9, 12, 9, 9, 9, 11]],
index=['O1', 'O2', 'O3', 'O4', 'O5', 'O6', 'O7'],
columns=['1', '2', '3', '4', '5', '6']).T
c = qiime2.CategoricalMetadataColumn(
pd.Series(['1', '0', '0', '0', '1', '0'],
name='n',
index=pd.Index(['6', '5', '4', '3', '2', '1'],
name='id')))
ancom(output_dir=self.temp_dir.name, table=t + 1, metadata=c)
index_fp = os.path.join(self.temp_dir.name, 'index.html')
with open(index_fp, 'r') as fh:
html = fh.read()
self.assertIn('<th>O7</th>', html)
def test_anosim_pairwise(self):
dm = skbio.DistanceMatrix(
[[0.00, 0.25, 0.25], [0.25, 0.00, 0.00], [0.25, 0.00, 0.00]],
ids=['sample1', 'sample2', 'sample3'])
md = qiime2.CategoricalMetadataColumn(
pd.Series(['a', 'b', 'b'],
name='a or b',
index=pd.Index(['sample1', 'sample2', 'sample3'],
name='id')))
with tempfile.TemporaryDirectory() as output_dir:
beta_group_significance(output_dir,
dm,
md,
method='anosim',
permutations=42,
pairwise=True)
index_fp = os.path.join(output_dir, 'index.html')
self.assertTrue(os.path.exists(index_fp))
# all expected boxplots are generated
self.assertTrue(
os.path.exists(os.path.join(output_dir, 'a-boxplots.pdf')))
self.assertTrue(
os.path.exists(os.path.join(output_dir, 'a-boxplots.png')))
self.assertTrue(
os.path.exists(os.path.join(output_dir, 'b-boxplots.pdf')))
self.assertTrue(
os.path.exists(os.path.join(output_dir, 'b-boxplots.png')))
# no extra boxplots are generated
self.assertEqual(len(glob.glob('%s/*-boxplots.pdf' % output_dir)),
2)
self.assertEqual(len(glob.glob('%s/*-boxplots.png' % output_dir)),
2)
self.assertTrue('ANOSIM results' in open(index_fp).read())
self.assertTrue('<td>42</td>' in open(index_fp).read())
self.assertFalse('Warning' in open(index_fp).read())
self.assertTrue('Pairwise anosim' in open(index_fp).read())
def test_inconsistent_sequence_length_paired(self):
forward = [('@s1/1 abc/1', 'G', '+', 'Y'),
('@s2/1 abc/1', 'CCC', '+', 'PPP'),
('@s3/1 abc/1', 'AAAAA', '+', 'PPPPP'),
('@s4/1 abc/1', 'TTTTTTT', '+', 'PPPPPPP')]
reverse = [('@s1/1 abc/1', 'AAAAAAA', '+', 'YYYYYYY'),
('@s2/1 abc/1', 'TTTTT', '+', 'PPPPP'),
('@s3/1 abc/1', 'GGG', '+', 'PPP'),
('@s4/1 abc/1', 'C', '+', 'P')]
bpsi = BarcodePairedSequenceFastqIterator(self.barcodes, forward,
reverse)
barcode_map = pd.Series(['AAAA', 'AACC'],
name='bc',
index=pd.Index(['sample1', 'sample2'],
name='id'))
barcode_map = qiime2.CategoricalMetadataColumn(barcode_map)
demux_data = emp_paired(bpsi, barcode_map)
lengths = [1, 3, 5, 7]
for n in range(1, 6):
with tempfile.TemporaryDirectory() as output_dir:
lengths_ = lengths[0:5 - n] if n < 4 else [1]
# TODO: Remove _PlotQualView wrapper
summarize(output_dir,
_PlotQualView(demux_data, paired=True),
n=n)
plot_fp = os.path.join(output_dir, 'data.jsonp')
with open(plot_fp, 'r') as fh:
jsonp = fh.read()
json_ = jsonp.replace('app.init(', '[').replace(');', ']')
payload = json.loads(json_)[0]
self.assertEqual(payload["totalSeqCount"], 4)
self.assertIn(payload["minSeqLen"]["forward"], lengths_)
self.assertIn(payload["minSeqLen"]["reverse"], lengths_)
self.assertEqual(payload["n"], min(n, 4))
def test_confusion_matrix_vmax_too_low(self):
b = qiime2.CategoricalMetadataColumn(self.a)
with self.assertRaisesRegex(
ValueError, r'vmax must be greater than.*'
r'\s\s0\.5.*less.*1\.0'):
confusion_matrix(self.tmpd, self.a, b, vmin=None, vmax=.5)
