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_parallel_beta(self):
t = Table(np.array([[0, 1, 3], [1, 1, 2]]),
['O1', 'O2'],
['S1', 'S2', 'S3'])
parallel = beta(table=t, metric='braycurtis', n_jobs=-1)
single_thread = beta(table=t, metric='braycurtis', n_jobs=1)
# expected computed with scipy.spatial.distance.braycurtis
expected = skbio.DistanceMatrix([[0.0000000, 0.3333333, 0.6666667],
[0.3333333, 0.0000000, 0.4285714],
[0.6666667, 0.4285714, 0.0000000]],
ids=['S1', 'S2', 'S3'])
self.assertEqual(parallel.ids, expected.ids)
self.assertEqual(single_thread.ids, expected.ids)
for id1 in parallel.ids:
for id2 in parallel.ids:
npt.assert_almost_equal(parallel[id1, id2], expected[id1, id2])
for id1 in single_thread.ids:
for id2 in single_thread.ids:
npt.assert_almost_equal(single_thread[id1, id2],
expected[id1, id2])
def test_beta_canberra_adkins(self):
t = Table(np.array([[0, 0], [0, 1], [1, 2]]),
['O1', 'O2', 'O3'],
['S1', 'S2'])
d = (1. / 2.) * sum([abs(0. - 1.) / (0. + 1.),
abs(1. - 2.) / (1. + 2.)])
expected = skbio.DistanceMatrix(np.array([[0.0, d], [d, 0.0]]),
ids=['S1', 'S2'])
actual = beta(table=t, metric='canberra_adkins')
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_aitchison(self):
t = Table(np.array([[0, 1, 3], [1, 1, 2]]),
['O1', 'O2'],
['S1', 'S2', 'S3'])
actual = beta(table=t, metric='aitchison')
expected = skbio.DistanceMatrix([[0.0000000, 0.4901290, 0.6935510],
[0.4901290, 0.0000000, 0.2034219],
[0.6935510, 0.2034219, 0.0000000]],
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(self):
t = Table(np.array([[0, 1, 3], [1, 1, 2]]),
['O1', 'O2'],
['S1', 'S2', 'S3'])
actual = beta(table=t, metric='braycurtis')
# expected computed with scipy.spatial.distance.braycurtis
expected = skbio.DistanceMatrix([[0.0000000, 0.3333333, 0.6666667],
[0.3333333, 0.0000000, 0.4285714],
[0.6666667, 0.4285714, 0.0000000]],
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_unknown_metric(self):
t = Table(np.array([[0, 1, 3], [1, 1, 2]]),
['O1', 'O2'],
['S1', 'S2', 'S3'])
with self.assertRaises(ValueError):
beta(table=t, metric='not-a-metric')
def test_beta_phylo_metric(self):
t = Table(np.array([[0, 1, 3], [1, 1, 2]]),
['O1', 'O2'],
['S1', 'S2', 'S3'])
with self.assertRaises(ValueError):
beta(table=t, metric='unweighted_unifrac')
def test_beta_empty_table(self):
t = Table(np.array([]), [], [])
with self.assertRaisesRegex(ValueError, 'empty'):
beta(table=t, metric='braycurtis')
def test_beta_unknown_metric(self):
t = Table(np.array([[0, 1, 3], [1, 1, 2]]), ['O1', 'O2'],
['S1', 'S2', 'S3'])
with self.assertRaises(ValueError):
beta(table=t, metric='not-a-metric')
def test_beta_phylo_metric(self):
t = Table(np.array([[0, 1, 3], [1, 1, 2]]), ['O1', 'O2'],
['S1', 'S2', 'S3'])
with self.assertRaises(ValueError):
beta(table=t, metric='unweighted_unifrac')
