def test_pcoa_fsvd(self): # Run fsvd, computing all dimensions. fsvd_result = pcoa(self.dm, number_of_dimensions=self.dm.data.shape[0]) # Run eigh, which computes all dimensions by default. eigh_result = pcoa(self.dm) assert_ordination_results_equal(fsvd_result, eigh_result, ignore_directionality=True, ignore_method_names=True)
def test_pcoa(self): dm = skbio.DistanceMatrix([[0.0000000, 0.3333333, 0.6666667], [0.3333333, 0.0000000, 0.4285714], [0.6666667, 0.4285714, 0.0000000]], ids=['S1', 'S2', 'S3']) actual = pcoa(dm) expected = skbio.stats.ordination.pcoa(dm) skbio.util.assert_ordination_results_equal( actual, expected, ignore_directionality=True)
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_pcoa(self): dm = skbio.DistanceMatrix([[0.0000000, 0.3333333, 0.6666667], [0.3333333, 0.0000000, 0.4285714], [0.6666667, 0.4285714, 0.0000000]], ids=['S1', 'S2', 'S3']) actual = pcoa(dm) expected = skbio.stats.ordination.pcoa(dm) skbio.util.assert_ordination_results_equal(actual, expected, ignore_directionality=True)
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_pcoa(self): observed = pcoa(self.dm) skbio.util.assert_ordination_results_equal( observed, self.ordination, ignore_directionality=True)
def test_pcoa(self): observed = pcoa(self.dm) skbio.util.assert_ordination_results_equal(observed, self.ordination, ignore_directionality=True)