def test_ancom_3class_anova(self):
t = pd.DataFrame([[9, 9, 19, 19, 29, 29],
[10, 11, 20, 20, 29, 28],
[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(['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)
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, 3, 3, 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)
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_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_ancom_3class_anova(self):
t = pd.DataFrame([[9, 9, 19, 19, 29, 29],
[10, 11, 20, 20, 29, 28],
[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(['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)
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, 3, 3, 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)
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_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_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_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_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_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_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)
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_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.MetadataCategory(
pd.Series([0, 0, 0, 1, 1, 1],
index=['S1', 'S2', 'S3', 'S4', 'S5', 'S6']))
ancom(self.results, t + 1, c)
res = pd.read_csv(os.path.join(self.results, 'ancom.csv'), index_col=0)
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.results, 'index.html')
self.assertTrue(os.path.exists(index_fp))
self.assertTrue(os.path.getsize(index_fp) > 0)
csv_fp = os.path.join(self.results, 'percent-abundances.csv')
self.assertTrue(os.path.exists(csv_fp))
self.assertTrue(os.path.getsize(csv_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_ancom_3class_anova(self):
t = pd.DataFrame(
[[9, 9, 19, 19, 29, 29], [10, 11, 20, 20, 29, 28],
[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.MetadataCategory(
pd.Series([0, 0, 1, 1, 2, 2],
index=['S1', 'S2', 'S3', 'S4', 'S5', 'S6']))
ancom(self.results, t + 1, c)
res = pd.read_csv(os.path.join(self.results, 'ancom.csv'), index_col=0)
exp = pd.DataFrame(
{
'W':
np.array([5, 5, 3, 3, 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)
