def test_alpha_phylogenetic_empty_table(self):
t = biom.Table(np.array([]), [], [])
tree = skbio.TreeNode.read(io.StringIO(
'((O1:0.25):0.25, O3:0.75)root;'))
with self.assertRaisesRegex(ValueError, "empty"):
alpha_phylogenetic(table=t, phylogeny=tree, metric='faith_pd')
def test_alpha_phylogenetic_unknown_metric(self):
t = biom.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):
alpha_phylogenetic(table=t, phylogeny=tree, metric='not-a-metric')
def test_alpha_phylogenetic_unknown_metric(self):
t = biom.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):
alpha_phylogenetic(table=t, phylogeny=tree, metric='not-a-metric')
def test_alpha_phylogenetic_skbio_error_rewriting(self):
t = biom.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'):
alpha_phylogenetic(table=t, phylogeny=tree, metric='faith_pd')
def test_alpha_phylogenetic_skbio_error_rewriting(self):
t = biom.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'):
alpha_phylogenetic(table=t, phylogeny=tree, metric='faith_pd')
def test_alpha_phylogenetic(self):
t = biom.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 = alpha_phylogenetic(table=t, phylogeny=tree, metric='faith_pd')
# expected computed with skbio.diversity.alpha_diversity
expected = pd.Series({'S1': 0.75, 'S2': 1.0, 'S3': 1.0},
name='faith_pd')
pdt.assert_series_equal(actual, expected)
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)
