def test_beta_phylogenetic_empty_table(self):
t = self.get_data_path('empty.biom')
tree = self.get_data_path('three_feature.tree')
with self.assertRaisesRegex(ValueError, 'empty'):
beta_phylogenetic(table=t, phylogeny=tree,
metric='unweighted_unifrac')
def test_beta_phylogenetic_unknown_metric(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(ValueError):
beta_phylogenetic(table=t, phylogeny=tree, metric='not-a-metric')
def test_beta_phylogenetic_unknown_metric(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(ValueError):
beta_phylogenetic(table=t, phylogeny=tree, metric='not-a-metric')
def test_beta_phylogenetic_empty_table(self):
t = Table(np.array([]), [], [])
tree = skbio.TreeNode.read(io.StringIO(
'((O1:0.25, O2:0.50):0.25, O3:0.75)root;'))
with self.assertRaisesRegex(ValueError, 'empty'):
beta_phylogenetic(table=t, phylogeny=tree,
metric='unweighted_unifrac')
def test_beta_phylogenetic_empty_table(self):
t = Table(np.array([]), [], [])
tree = skbio.TreeNode.read(
io.StringIO('((O1:0.25, O2:0.50):0.25, O3:0.75)root;'))
with self.assertRaisesRegex(ValueError, 'empty'):
beta_phylogenetic(table=t,
phylogeny=tree,
metric='unweighted_unifrac')
def test_beta_phylogenetic_too_many_jobs(self):
bt_fp = self.get_data_path('crawford.biom')
tree_fp = self.get_data_path('tree.nwk')
with self.assertRaises(ValueError):
# cannot guarantee that this will always be true, but it would be
# odd to see a machine with these many CPUs
beta_phylogenetic(table=bt_fp, phylogeny=tree_fp,
metric='unweighted_unifrac', n_jobs=11117)
def test_beta_phylogenetic_skbio_error_rewriting(self):
t = self.get_data_path('two_feature_table.biom')
tree = self.get_data_path('vaw.nwk')
# Verify through regex that there is a ``feature_ids`` substring
# followed by a ``phylogeny``
with self.assertRaisesRegex(ValueError,
'represented by the phylogeny'):
beta_phylogenetic(table=t, phylogeny=tree,
metric='weighted_unifrac')
def test_beta_phylogenetic_alpha_on_non_generalized(self):
bt_fp = self.get_data_path('crawford.biom')
tree_fp = self.get_data_path('tree.nwk')
with self.assertRaisesRegex(ValueError, 'The alpha parameter is only '
'allowed when the choice of metric is '
'generalized_unifrac'):
beta_phylogenetic(table=bt_fp, phylogeny=tree_fp,
metric='unweighted_unifrac',
alpha=0.11)
def test_beta_phylogenetic_weighted_unifrac_threads_error(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.assertRaisesRegex(ValueError, 'parallelizable'):
beta_phylogenetic(table=t, phylogeny=tree,
metric='weighted_unifrac', n_jobs=-1)
def test_beta_phylogenetic_weighted_unifrac_threads_error(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.assertRaisesRegex(ValueError, 'parallelizable'):
beta_phylogenetic(table=t,
phylogeny=tree,
metric='weighted_unifrac',
n_jobs=-1)
def test_beta_phylogenetic_skbio_error_rewriting(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):0.25, O3:0.75)root;'))
# Verify through regex that there is a ``feature_ids`` substring
# followed by a ``phylogeny``
with self.assertRaisesRegex(skbio.tree.MissingNodeError,
'feature_ids.*phylogeny'):
beta_phylogenetic(table=t,
phylogeny=tree,
metric='weighted_unifrac')
def test_beta_phylogenetic_skbio_error_rewriting(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):0.25, O3:0.75)root;'))
# Verify through regex that there is a ``feature_ids`` substring
# followed by a ``phylogeny``
with self.assertRaisesRegex(skbio.tree.MissingNodeError,
'feature_ids.*phylogeny'):
beta_phylogenetic(table=t, phylogeny=tree,
metric='weighted_unifrac')
def test_generalized_unifrac_no_alpha(self):
bt_fp = self.get_data_path('crawford.biom')
tree_fp = self.get_data_path('crawford.nwk')
actual = beta_phylogenetic(table=bt_fp,
phylogeny=tree_fp,
metric='generalized_unifrac',
alpha=None)
# alpha=1 should be equal to weighted normalized UniFrac
data = np.array([0.2821874, 0.16148405, 0.20186143, 0.1634832,
0.40351108, 0.29135056, 0.24790944, 0.41967404,
0.24642185, 0.22218489, 0.34007547, 0.27722011,
0.20963881, 0.16897221, 0.3217958, 0.15237816,
0.16899207, 0.36445044, 0.25408941, 0.23358681,
0.4069374, 0.24615927, 0.28573888, 0.20578184,
0.20742006, 0.31249151, 0.46169893, 0.35294595,
0.32522355, 0.48437103, 0.21534558, 0.30558908,
0.12091004, 0.19817777, 0.24792853, 0.34293674])
ids = ('10084.PC.481', '10084.PC.593', '10084.PC.356', '10084.PC.355',
'10084.PC.354', '10084.PC.636', '10084.PC.635', '10084.PC.607',
'10084.PC.634')
expected = skbio.DistanceMatrix(data, ids=ids)
self.assertEqual(actual.ids, expected.ids)
for id1 in actual.ids:
for id2 in actual.ids:
npt.assert_almost_equal(actual[id1, id2], expected[id1, id2])
def test_generalized_unifrac(self):
bt_fp = self.get_data_path('vaw.biom')
tree_fp = self.get_data_path('vaw.nwk')
actual = beta_phylogenetic(table=bt_fp,
phylogeny=tree_fp,
metric='generalized_unifrac',
alpha=0.5)
data = np.array([[0.0000000, 0.4040518, 0.6285560, 0.5869439,
0.4082483, 0.2995673],
[0.4040518, 0.0000000, 0.4160597, 0.7071068,
0.7302479, 0.4860856],
[0.6285560, 0.4160597, 0.0000000, 0.8005220,
0.9073159, 0.5218198],
[0.5869439, 0.7071068, 0.8005220, 0.0000000,
0.4117216, 0.3485667],
[0.4082483, 0.7302479, 0.9073159, 0.4117216,
0.0000000, 0.6188282],
[0.2995673, 0.4860856, 0.5218198, 0.3485667,
0.6188282, 0.0000000]])
ids = ('Sample1', 'Sample2', 'Sample3', 'Sample4', 'Sample5',
'Sample6')
expected = skbio.DistanceMatrix(data, ids=ids)
self.assertEqual(actual.ids, expected.ids)
for id1 in actual.ids:
for id2 in actual.ids:
npt.assert_almost_equal(actual[id1, id2], expected[id1, id2])
def test_variance_adjusted_normalized(self):
bt_fp = self.get_data_path('vaw.biom')
tree_fp = self.get_data_path('vaw.nwk')
actual = beta_phylogenetic(table=bt_fp,
phylogeny=tree_fp,
metric='weighted_normalized_unifrac',
variance_adjusted=True)
data = np.array([[0.0000000, 0.4086040, 0.6240185, 0.4639481,
0.2857143, 0.2766318],
[0.4086040, 0.0000000, 0.3798594, 0.6884992,
0.6807616, 0.4735781],
[0.6240185, 0.3798594, 0.0000000, 0.7713254,
0.8812897, 0.5047114],
[0.4639481, 0.6884992, 0.7713254, 0.0000000,
0.6666667, 0.2709298],
[0.2857143, 0.6807616, 0.8812897, 0.6666667,
0.0000000, 0.4735991],
[0.2766318, 0.4735781, 0.5047114, 0.2709298,
0.4735991, 0.0000000]])
ids = ('Sample1', 'Sample2', 'Sample3', 'Sample4', 'Sample5',
'Sample6')
expected = skbio.DistanceMatrix(data, ids=ids)
self.assertEqual(actual.ids, expected.ids)
for id1 in actual.ids:
for id2 in actual.ids:
npt.assert_almost_equal(actual[id1, id2], expected[id1, id2])
def test_beta_weighted(self):
bt_fp = self.get_data_path('crawford.biom')
tree_fp = self.get_data_path('crawford.nwk')
actual = beta_phylogenetic(table=bt_fp,
phylogeny=tree_fp,
metric='weighted_unifrac')
# computed with beta-phylogenetic (weighted_unifrac)
data = np.array([0.44656238, 0.23771096, 0.30489123, 0.23446002,
0.65723575, 0.44911772, 0.381904, 0.69144829,
0.39611776, 0.36568012, 0.53377975, 0.48908025,
0.35155196, 0.28318669, 0.57376916, 0.23395746,
0.24658122, 0.60271637, 0.39802552, 0.36567394,
0.68062701, 0.36862049, 0.48350632, 0.33024631,
0.33266697, 0.53464744, 0.74605075, 0.53951035,
0.49680733, 0.79178838, 0.37109012, 0.52629343,
0.22118218, 0.32400805, 0.43189708, 0.59705893])
ids = ('10084.PC.481', '10084.PC.593', '10084.PC.356', '10084.PC.355',
'10084.PC.354', '10084.PC.636', '10084.PC.635', '10084.PC.607',
'10084.PC.634')
expected = skbio.DistanceMatrix(data, ids=ids)
self.assertEqual(actual.ids, expected.ids)
for id1 in actual.ids:
for id2 in actual.ids:
npt.assert_almost_equal(actual[id1, id2], expected[id1, id2])
def test_beta_unweighted_parallel(self):
bt_fp = self.get_data_path('crawford.biom')
tree_fp = self.get_data_path('crawford.nwk')
actual = beta_phylogenetic(table=bt_fp,
phylogeny=tree_fp,
metric='unweighted_unifrac',
n_jobs=2)
# computed with beta-phylogenetic
data = np.array([0.71836067, 0.71317361, 0.69746044, 0.62587207,
0.72826674, 0.72065895, 0.72640581, 0.73606053,
0.70302967, 0.73407301, 0.6548042, 0.71547381,
0.78397813, 0.72318399, 0.76138933, 0.61041275,
0.62331299, 0.71848305, 0.70416337, 0.75258475,
0.79249029, 0.64392779, 0.70052733, 0.69832716,
0.77818938, 0.72959894, 0.75782689, 0.71005144,
0.75065046, 0.78944369, 0.63593642, 0.71283615,
0.58314638, 0.69200762, 0.68972056, 0.71514083])
ids = ('10084.PC.481', '10084.PC.593', '10084.PC.356', '10084.PC.355',
'10084.PC.354', '10084.PC.636', '10084.PC.635', '10084.PC.607',
'10084.PC.634')
expected = skbio.DistanceMatrix(data, ids=ids)
self.assertEqual(actual.ids, expected.ids)
for id1 in actual.ids:
for id2 in actual.ids:
npt.assert_almost_equal(actual[id1, id2], expected[id1, id2])
def core_metrics(table: biom.Table, phylogeny: skbio.TreeNode,
sampling_depth: int) -> (pd.Series,
pd.Series,
pd.Series,
pd.Series,
skbio.DistanceMatrix,
skbio.DistanceMatrix,
skbio.DistanceMatrix,
skbio.DistanceMatrix,
skbio.OrdinationResults,
skbio.OrdinationResults,
skbio.OrdinationResults,
skbio.OrdinationResults):
rarefied_table = rarefy(table=table, sampling_depth=sampling_depth)
faith_pd_vector = alpha_phylogenetic(
table=rarefied_table, phylogeny=phylogeny, metric='faith_pd')
observed_otus_vector = alpha(table=rarefied_table, metric='observed_otus')
shannon_vector = alpha(table=rarefied_table, metric='shannon')
evenness_vector = alpha(table=rarefied_table, metric='pielou_e')
unweighted_unifrac_distance_matrix = beta_phylogenetic(
table=rarefied_table, phylogeny=phylogeny, metric='unweighted_unifrac')
weighted_unifrac_distance_matrix = beta_phylogenetic(
table=rarefied_table, phylogeny=phylogeny, metric='weighted_unifrac')
jaccard_distance_matrix = beta(table=rarefied_table, metric='jaccard')
bray_curtis_distance_matrix = beta(
table=rarefied_table, metric='braycurtis')
unweighted_unifrac_pcoa_results = pcoa(
distance_matrix=unweighted_unifrac_distance_matrix)
weighted_unifrac_pcoa_results = pcoa(
distance_matrix=weighted_unifrac_distance_matrix)
jaccard_pcoa_results = pcoa(distance_matrix=jaccard_distance_matrix)
bray_curtis_pcoa_results = pcoa(
distance_matrix=bray_curtis_distance_matrix)
return (
faith_pd_vector, observed_otus_vector, shannon_vector, evenness_vector,
unweighted_unifrac_distance_matrix, weighted_unifrac_distance_matrix,
jaccard_distance_matrix, bray_curtis_distance_matrix,
unweighted_unifrac_pcoa_results, weighted_unifrac_pcoa_results,
jaccard_pcoa_results, bray_curtis_pcoa_results
)
def core_metrics(
table: biom.Table, phylogeny: skbio.TreeNode, sampling_depth: int
) -> (pd.Series, pd.Series, pd.Series, pd.Series, skbio.DistanceMatrix,
skbio.DistanceMatrix, skbio.DistanceMatrix, skbio.DistanceMatrix,
skbio.OrdinationResults, skbio.OrdinationResults,
skbio.OrdinationResults, skbio.OrdinationResults):
rarefied_table = rarefy(table=table, sampling_depth=sampling_depth)
faith_pd_vector = alpha_phylogenetic(table=rarefied_table,
phylogeny=phylogeny,
metric='faith_pd')
observed_otus_vector = alpha(table=rarefied_table, metric='observed_otus')
shannon_vector = alpha(table=rarefied_table, metric='shannon')
evenness_vector = alpha(table=rarefied_table, metric='pielou_e')
unweighted_unifrac_distance_matrix = beta_phylogenetic(
table=rarefied_table, phylogeny=phylogeny, metric='unweighted_unifrac')
weighted_unifrac_distance_matrix = beta_phylogenetic(
table=rarefied_table, phylogeny=phylogeny, metric='weighted_unifrac')
jaccard_distance_matrix = beta(table=rarefied_table, metric='jaccard')
bray_curtis_distance_matrix = beta(table=rarefied_table,
metric='braycurtis')
unweighted_unifrac_pcoa_results = pcoa(
distance_matrix=unweighted_unifrac_distance_matrix)
weighted_unifrac_pcoa_results = pcoa(
distance_matrix=weighted_unifrac_distance_matrix)
jaccard_pcoa_results = pcoa(distance_matrix=jaccard_distance_matrix)
bray_curtis_pcoa_results = pcoa(
distance_matrix=bray_curtis_distance_matrix)
return (faith_pd_vector, observed_otus_vector, shannon_vector,
evenness_vector, unweighted_unifrac_distance_matrix,
weighted_unifrac_distance_matrix, jaccard_distance_matrix,
bray_curtis_distance_matrix, unweighted_unifrac_pcoa_results,
weighted_unifrac_pcoa_results, jaccard_pcoa_results,
bray_curtis_pcoa_results)
def test_beta_phylogenetic(self):
t = self.get_data_path('two_feature_table.biom')
tree = self.get_data_path('three_feature.tree')
actual = beta_phylogenetic(
table=t, phylogeny=tree, metric='unweighted_unifrac')
# expected computed with skbio.diversity.beta_diversity
expected = skbio.DistanceMatrix([[0.00, 0.25, 0.25],
[0.25, 0.00, 0.00],
[0.25, 0.00, 0.00]],
ids=['S1', 'S2', 'S3'])
self.assertEqual(actual.ids, expected.ids)
for id1 in actual.ids:
for id2 in actual.ids:
npt.assert_almost_equal(actual[id1, id2], expected[id1, id2])
def test_beta_phylogenetic(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;'))
actual = beta_phylogenetic(table=t,
phylogeny=tree,
metric='unweighted_unifrac')
# expected computed with skbio.diversity.beta_diversity
expected = skbio.DistanceMatrix(
[[0.00, 0.25, 0.25], [0.25, 0.00, 0.00], [0.25, 0.00, 0.00]],
ids=['S1', 'S2', 'S3'])
self.assertEqual(actual.ids, expected.ids)
for id1 in actual.ids:
for id2 in actual.ids:
npt.assert_almost_equal(actual[id1, id2], expected[id1, id2])
def test_beta_phylogenetic(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;'))
actual = beta_phylogenetic(
table=t, phylogeny=tree, metric='unweighted_unifrac')
# expected computed with skbio.diversity.beta_diversity
expected = skbio.DistanceMatrix([[0.00, 0.25, 0.25],
[0.25, 0.00, 0.00],
[0.25, 0.00, 0.00]],
ids=['S1', 'S2', 'S3'])
self.assertEqual(actual.ids, expected.ids)
for id1 in actual.ids:
for id2 in actual.ids:
npt.assert_almost_equal(actual[id1, id2], expected[id1, id2])
