def test_diff_abundance(self):
# test using defulat values
dd = diff_abundance(self.test1,
'group',
val1='1',
val2='2',
random_seed=2017)
expected_ids = [0, 1, 2, 3, 4, 7, 10]
self.assertEqual(len(dd.feature_metadata), 7)
for cid in expected_ids:
self.assertIn(self.test1.feature_metadata.index[cid],
dd.feature_metadata.index)
# test using no group 2 using only group 1
dd = diff_abundance(self.test1, 'group', val1='1')
expected_ids = [0, 1, 2, 3, 4, 10]
# we get 1 less since now we also include badsample sample (not in the mapping file, so gets na)
self.assertEqual(len(dd.feature_metadata), 6)
for cid in expected_ids:
self.assertIn(self.test1.feature_metadata.index[cid],
dd.feature_metadata.index)
# test using no val 2 using only group 2
dd = diff_abundance(self.test1, 'group', val1='2')
expected_ids = [0, 1, 2, 3, 4, 7, 10]
self.assertEqual(len(dd.feature_metadata), 7)
for cid in expected_ids:
self.assertIn(self.test1.feature_metadata.index[cid],
dd.feature_metadata.index)
def test_diff_abundance_paired(self):
# Do the paired test (we should get 4 features)
dd = diff_abundance_paired(self.test_paired,
'subj',
field='group',
val1='1',
val2='2',
alpha=0.1,
random_seed=2020)
self.assertEqual(len(dd.feature_metadata), 4)
expected_ids = ['AG', 'AA', 'TA', 'TT']
for cid in expected_ids:
self.assertIn(cid, dd.feature_metadata._feature_id)
# and the unpaired test (which is less sensitive for this dataset - we should get only 2)
dd = diff_abundance(self.test_paired,
field='group',
val1='1',
val2='2',
alpha=0.1,
random_seed=2020)
self.assertEqual(len(dd.feature_metadata), 2)
expected_ids = ['AG', 'TT']
for cid in expected_ids:
self.assertIn(cid, dd.feature_metadata._feature_id)
# test with binary transforming the pairs
dd = diff_abundance_paired(self.test_paired,
'subj',
transform='pair_rank',
field='group',
val1='1',
val2='2',
alpha=0.1,
random_seed=2020)
self.assertEqual(len(dd.feature_metadata), 4)
expected_ids = ['AA', 'AG', 'TA', 'TT']
for cid in expected_ids:
self.assertIn(cid, dd.feature_metadata._feature_id)
# test with more than 2 samples per group
dd = diff_abundance_paired(self.test_paired,
'group3',
field='group2',
val1='1',
val2='2',
alpha=0.1,
random_seed=2020,
numperm=1000)
self.assertEqual(len(dd.feature_metadata), 4)
expected_ids = ['AA', 'AT', 'GG', 'TA']
for cid in expected_ids:
self.assertIn(cid, dd.feature_metadata._feature_id)
def test_diff_abundance_alpha0(self):
# Test when we should get 0 features (setting FDR level to 0)
dd = diff_abundance(self.test1, 'group', val1='1', val2='2', alpha=0)
self.assertEqual(dd.shape, (self.test1.shape[0], 0))