def test_filter_sample_categories(self):
test = self.test1.filter_ids(['badsample'], axis=0, negate=True)
# does not filter anything
assert_experiment_equal(test.filter_sample_categories('group', 9), test)
# filter group of 2
assert_experiment_equal(test.filter_sample_categories('group', 10),
test.filter_samples('group', '1'))
def test_filter_features_edge_cases(self):
# none filtered
obs = self.test2.filter_features('oxygen', ['facultative'])
self.assertEqual(obs.shape, (9, 0))
obs = self.test2.filter_features('oxygen', ['facultative'],
negate=True)
assert_experiment_equal(obs, self.test2)
def test_copy(self):
exp = self.test1.copy()
assert_experiment_equal(exp, self.test1)
self.assertIsNot(exp, self.test1)
# make sure it is a deep copy - not sharing the data
exp.data[0, 0] = exp.data[0, 0] + 1
self.assertNotEqual(exp.data[0, 0], self.test1.data[0, 0])
def test_join_metadata_fields_complex(self):
# test join feature fields with new field name, separator and inplace
exp = deepcopy(self.test1)
newexp = exp.join_metadata_fields('taxonomy',
'taxonomy',
'test',
axis=1,
sep=';',
inplace=True)
self.assertIs(newexp, exp)
self.assertIn('test', exp.feature_metadata.columns)
self.assertNotIn('test', exp.sample_metadata.columns)
self.assertEqual(exp.feature_metadata['test'].iloc[11],
'bad_bacteria;bad_bacteria')
# test we didn't change anything besides the new sample metadata column
assert_experiment_equal(exp, self.test1, ignore_md_fields=['test'])
# test join feature fields with new field name, sepparator, inplace and align
exp = deepcopy(self.test1)
newexp = exp.join_metadata_fields('taxonomy',
'ph',
'test',
axis=1,
sep=';',
align='<',
inplace=True)
self.assertIs(newexp, exp)
self.assertIn('test', exp.feature_metadata.columns)
self.assertNotIn('test', exp.sample_metadata.columns)
self.assertEqual(
exp.feature_metadata.loc['AT', 'test'],
'k__Bacteria; p__Tenericutes; c__Mollicutes; o__Mycoplasmatales; f__Mycoplasmataceae; g__Mycoplasma; s__ ;4.1'
)
# test we didn't change anything besides the new sample metadata column
assert_experiment_equal(exp, self.test1, ignore_md_fields=['test'])
def test_read_amplicon(self):
# test loading a taxonomy biom table and filtering/normalizing
exp1 = ca.read_amplicon(self.test1_biom, min_reads=1000, normalize=10000)
exp2 = ca.read(self.test1_biom, normalize=None)
exp2.filter_by_data('abundance', axis=0, cutoff=1000, inplace=True, mean_or_sum='sum')
exp2.normalize(inplace=True)
assert_experiment_equal(exp1, exp2)
self.assertIn('taxonomy', exp1.feature_metadata.columns)
def test_filter_by_data_sample_edge_cases(self):
# all samples are filtered out
obs = self.test2.filter_by_data('abundance', axis=0, cutoff=100000, mean_or_sum='sum')
self.assertEqual(obs.shape, (0, 8))
# none is filtered out
obs = self.test2.filter_by_data('abundance', axis=0, cutoff=1, mean_or_sum='sum')
assert_experiment_equal(obs, self.test2)
self.assertIsNot(obs, self.test2)
def test_sort_by_metadata_feature(self):
obs = self.test2.sort_by_metadata(
field='level2', axis=1).sort_by_metadata(field='level1', axis=1)
self.assertIsNot(obs, self.test2)
assert_experiment_equal(
obs, self.test2.reorder(obs.feature_metadata['ori.order'], axis=1))
self.assertListEqual(obs.feature_metadata['new.order'].tolist(),
list(range(8)))
def test_split_train_test(self):
train, test = self.test2_dense.split_train_test(
test_size=3, stratify='categorical', random_state=7)
assert_experiment_equal(
test, self.test2_dense.filter_ids(['S3', 'S8', 'S1'], axis='s'))
assert_experiment_equal(
train, self.test2_dense.filter_ids(['S9', 'S6', 'S5', 'S2', 'S4', 'S7'], axis='s'))
def test_downsample_sample(self):
obs = self.test2.downsample('group')
# should be down to 4 samples; feature number is the same
self.assertEqual(obs.shape, (4, 8))
sid = obs.sample_metadata.index.tolist()
all_sid = self.test2.sample_metadata.index.tolist()
exp = self.test2.reorder([all_sid.index(i) for i in sid])
assert_experiment_equal(obs, exp)
def test_filter_by_data_feature_edge_cases(self):
# all features are filtered out
obs = self.test2.filter_by_data('sum_abundance', axis=1, cutoff=10000)
self.assertEqual(obs.shape, (9, 0))
# none is filtered out
obs = self.test2.filter_by_data('sum_abundance', axis=1, cutoff=1)
assert_experiment_equal(obs, self.test2)
self.assertIsNot(obs, self.test2)
def test_save(self):
exp = ca.read(self.test2_biom, self.test2_samp, normalize=None)
d = mkdtemp()
f = join(d, 'test1.save')
# test the json biom format
exp.save(f, fmt='json')
newexp = ca.read(f + '.biom', f + '_sample.txt', normalize=None)
assert_experiment_equal(newexp, exp, ignore_md_fields=['#SampleID.1'])
shutil.rmtree(d)
def test_join_metadata_fields(self):
# test the default params
newexp = self.test1.join_metadata_fields('id', 'group', inplace=False)
self.assertIn('id_group', newexp.sample_metadata.columns)
self.assertEqual(newexp.sample_metadata.loc['S12', 'id_group'],
'12.0_2')
# test we didn't change anything besides the new sample metadata column
assert_experiment_equal(newexp,
self.test1,
ignore_md_fields=['id_group'])
def test_from_pandas_reorder(self):
df = self.test1.to_pandas(sparse=False)
# let's reorder the dataframe
df = df.sort_values(self.test1.feature_metadata.index.values[10])
df = df.sort_values(df.index.values[0], axis=1)
res = ca.Experiment.from_pandas(df, self.test1)
# we need to reorder the original experiment
exp = self.test1.sort_by_data(subset=[10], key='mean')
exp = exp.sort_by_data(subset=[0], key='mean', axis=1)
assert_experiment_equal(res, exp)
def test_read_amplicon(self):
# test loading a taxonomy biom table and filtering/normalizing
exp = ca.read_amplicon(self.test1_biom,
filter_reads=1000,
normalize=10000)
exp2 = ca.read(self.test1_biom, normalize=None)
exp2.filter_by_data('sum_abundance', cutoff=1000, inplace=True)
exp2.normalize(inplace=True)
assert_experiment_equal(exp, exp2)
self.assertIn('taxonomy', exp.feature_metadata)
def test_filter_samples_edge_cases(self):
# no group 3 - none filtered
test1 = ca.read(self.test1_biom,
self.test1_samp,
self.test1_feat,
normalize=None)
# group dtype is O
obs = test1.filter_samples('group', ['3'])
self.assertEqual(obs.shape, (0, 12))
obs = test1.filter_samples('group', ['3'], negate=True)
assert_experiment_equal(obs, test1)
def test_sort_by_data_sample(self):
# sort sample based on the first and last features
obs = self.test2.sort_by_data(subset=[0, 7])
# the order is the same with original
assert_experiment_equal(obs, self.test2)
obs = self.test2.sort_by_data(subset=[0, 3])
assert_experiment_equal(
obs, self.test2.reorder(obs.sample_metadata['ori.order'], axis=0))
self.assertListEqual(obs.sample_metadata['new.order'].tolist(),
list(range(9)))
def test_join_experiments(self):
# do the famous join experiment to itself trick
texp = deepcopy(self.test1)
texp.description = 't2'
newexp = self.test1.join_experiments(texp, prefixes=('c1', ''))
self.assertEqual(len(newexp.feature_metadata),
len(self.test1.feature_metadata))
self.assertEqual(len(newexp.sample_metadata),
len(self.test1.sample_metadata) * 2)
fexp = newexp.filter_samples('experiments', ['t2'])
assert_experiment_equal(fexp, texp, ignore_md_fields=['experiments'])
def test_filter_by_metadata_sample_edge_cases(self):
# no group 3 - none filtered
obs = self.test2.filter_by_metadata('group', [3])
self.assertEqual(obs.shape, (0, 8))
obs = self.test2.filter_by_metadata('group', [3], negate=True)
assert_experiment_equal(obs, self.test2)
# all samples are filtered
obs = self.test2.filter_by_metadata('group', [1, 2])
assert_experiment_equal(obs, self.test2)
obs = self.test2.filter_by_metadata('group', [1, 2], negate=True)
self.assertEqual(obs.shape, (0, 8))
def test_sort_by_metadata_sample(self):
# test sorting various fields (keeping the order)
obs = self.timeseries.sort_by_metadata(
field='MINUTES', inplace=True).sort_by_metadata(
field='HOUR', inplace=True).sort_by_metadata(field='DAY',
inplace=True)
self.assertIs(obs, self.timeseries)
exp = ca.read(join(self.test_data_dir, 'timeseries.sorted.time.biom'),
join(self.test_data_dir, 'timeseries.sample'),
normalize=None)
assert_experiment_equal(obs, exp, almost_equal=True)
self.assertListEqual(obs.sample_metadata['MF_SAMPLE_NUMBER'].tolist(),
list(range(1, 96)))
def test_cluster_data(self):
def log_and_scale(exp):
exp.log_n(inplace=True)
exp.scale(inplace=True, axis=1)
return exp
# no minimal filtering
obs = self.test1.cluster_data(transform=log_and_scale)
exp = ca.read(join(self.test_data_dir,
'test1.clustered.features.biom'),
self.test1_samp,
normalize=None)
assert_experiment_equal(obs, exp, almost_equal=True)
def test_sort_samples(self):
obs = self.timeseries.sort_samples(
'MINUTES',
inplace=True).sort_samples('HOUR',
inplace=True).sort_samples('DAY',
inplace=True)
self.assertIs(obs, self.timeseries)
exp = ca.read(join(self.test_data_dir, 'timeseries.sorted.time.biom'),
join(self.test_data_dir, 'timeseries.sample'),
normalize=None)
assert_experiment_equal(obs, exp, almost_equal=True)
self.assertListEqual(obs.sample_metadata['MF_SAMPLE_NUMBER'].tolist(),
list(range(1, 96)))
def test_reorder_round_trip(self):
# test double permuting of a bigger data set
exp = ca.read(self.timeseries_biom, self.timeseries_samp, normalize=None)
rand_perm_samples = np.random.permutation(exp.data.shape[0])
rand_perm_features = np.random.permutation(exp.data.shape[1])
rev_perm_samples = np.argsort(rand_perm_samples)
rev_perm_features = np.argsort(rand_perm_features)
new = exp.reorder(rand_perm_features, axis=1, inplace=False)
new.reorder(rand_perm_samples, axis=0, inplace=True)
new.reorder(rev_perm_features, axis=1, inplace=True)
new.reorder(rev_perm_samples, axis=0, inplace=True)
assert_experiment_equal(new, exp)
def test_filter_by_data_feature(self):
# one feature is filtered out when cutoff is set to 25
for sparse, inplace in [(True, False), (True, True), (False, False), (False, True)]:
obs = self.test2.filter_by_data(
'sum_abundance', axis=1, inplace=inplace, cutoff=25)
self.assertEqual(obs.shape, (9, 7))
exp = ca.read(*[get_data_path(i) for i in [
'test2.biom.filter.feature',
'test2.sample',
'test2.feature']],
normalize=None)
assert_experiment_equal(obs, exp)
if inplace:
self.assertIs(obs, self.test2)
else:
self.assertIsNot(obs, self.test2)
def test_log_n(self):
obs = self.test2.log_n()
self.test2.data = np.log2([[10., 20., 1., 20., 5., 100., 844., 100.],
[10., 20., 2., 19., 1., 100., 849., 200.],
[10., 20., 3., 18., 5., 100., 844., 300.],
[10., 20., 4., 17., 1., 100., 849., 400.],
[10., 20., 5., 16., 4., 100., 845., 500.],
[10., 20., 6., 15., 1., 100., 849., 600.],
[10., 20., 7., 14., 3., 100., 846., 700.],
[10., 20., 8., 13., 1., 100., 849., 800.],
[10., 20., 9., 12., 7., 100., 842., 900.]])
assert_experiment_equal(obs, self.test2)
self.assertIsNot(obs, self.test2)
obs = self.test2.log_n(inplace=True)
self.assertIs(obs, self.test2)
def test_join_fields_complex(self):
# test join feature fields with new field name, separator and inplace
exp = deepcopy(self.test1)
newexp = exp.join_fields('taxonomy',
'taxonomy',
newname='test',
axis=1,
sep=';',
inplace=True)
self.assertIs(newexp, exp)
self.assertIn('test', exp.feature_metadata.columns)
self.assertNotIn('test', exp.sample_metadata.columns)
self.assertEqual(exp.feature_metadata['test'].iloc[11],
'bad_bacteria;bad_bacteria')
# test we didn't change anything besides the new sample metadata column
assert_experiment_equal(exp, self.test1, ignore_md_fields=['test'])
def test_filter_by_data_sample(self):
for sparse, inplace in [(True, False), (True, True), (False, False), (False, True)]:
test2 = ca.read(self.test2_biom, self.test2_samp, self.test2_feat,
sparse=sparse, normalize=None)
# filter out samples with abundance < 1200. only the last sample is filtered out.
obs = test2.filter_by_data(
'sum_abundance', axis=0, inplace=inplace, cutoff=1200)
self.assertEqual(obs.shape, (8, 8))
exp = ca.read(*[get_data_path(i) for i in [
'test2.biom.filter.sample',
'test2.sample',
'test2.feature']],
normalize=None)
assert_experiment_equal(obs, exp)
if inplace:
self.assertIs(obs, test2)
else:
self.assertIsNot(obs, test2)
def test_join_experiments_featurewise(self):
otu1 = ca.Experiment(np.array([[0, 9], [7, 4]]),
sparse=False,
sample_metadata=pd.DataFrame(
{
'category': ['B', 'A'],
'ph': [7.7, 6.6]
},
index=['s2', 's1']),
feature_metadata=pd.DataFrame(
{'motile': ['y', 'n']},
index=['16S1', '16S2']))
otu2 = ca.Experiment(np.array([[6], [8], [10]]),
sparse=False,
sample_metadata=pd.DataFrame(
{
'category': ['A', 'B', 'C'],
'ph': [6.6, 7.7, 8.8]
},
index=['s1', 's2', 's3']),
feature_metadata=pd.DataFrame({'motile': [None]},
index=['ITS1']))
combined_obs = otu1.join_experiments_featurewise(
otu2, 'origin', ('16S', 'ITS'))
combined_exp = ca.Experiment(np.array([[7, 4, 6], [0, 9, 8]]),
sparse=False,
sample_metadata=pd.DataFrame(
{
'category': ['A', 'B'],
'ph': [6.6, 7.7]
},
index=['s1', 's2']),
feature_metadata=pd.DataFrame(
{
'motile': ['y', 'n', None],
'origin': ['16S', '16S', 'ITS']
},
index=['16S1', '16S2', 'ITS1']))
# reorder the samples
combined_obs = combined_obs.filter_ids(
combined_exp.sample_metadata.index, axis=0)
assert_experiment_equal(combined_obs, combined_exp)
def test_save_biom(self):
# NOTE: Currently not testing the save biom hdf with taxonomy
# as there is a bug there!
exp = ca.read_amplicon(self.test1_biom,
self.test1_samp,
normalize=None,
min_reads=None)
d = mkdtemp()
f = join(d, 'test1.save.biom')
# test the json biom format
exp.save_biom(f, fmt='hdf5')
newexp = ca.read_amplicon(f,
self.test1_samp,
normalize=None,
min_reads=None)
assert_experiment_equal(newexp, exp)
# test the txt biom format
exp.save_biom(f, fmt='txt')
newexp = ca.read_amplicon(f,
self.test1_samp,
normalize=None,
min_reads=None)
assert_experiment_equal(newexp, exp, ignore_md_fields=['taxonomy'])
# test the hdf5 biom format with no taxonomy
exp.save_biom(f, add_metadata=None)
newexp = ca.read(f, self.test1_samp, normalize=None)
self.assertTrue('taxonomy' not in newexp.feature_metadata)
assert_experiment_equal(newexp, exp, ignore_md_fields=['taxonomy'])
shutil.rmtree(d)
def test_from_pandas_with_experiment(self):
df = self.test1.to_pandas(sparse=False)
res = ca.Experiment.from_pandas(df, self.test1)
assert_experiment_equal(res, self.test1)
def test_deep_copy_experiment(self):
exp = deepcopy(self.test1)
assert_experiment_equal(exp, self.test1)
self.assertIsNot(exp, self.test1)
