'the table, but all feature ids in the table must be '
'present in this tree.')
},
parameter_descriptions={
'metric': 'The beta diversity metric to be computed.'
},
output_descriptions={'distance_matrix': 'The resulting distance matrix.'},
name='Beta diversity (phylogenetic)',
description=("Computes a user-specified phylogenetic beta diversity metric"
" for all pairs of samples in a feature table.")
)
plugin.methods.register_function(
function=q2_diversity.beta,
inputs={'table': FeatureTable[Frequency] % Properties('uniform-sampling')},
parameters={'metric': Str % Choices(beta.non_phylogenetic_metrics())},
outputs=[('distance_matrix', DistanceMatrix)],
input_descriptions={
'table': ('The feature table containing the samples over which beta '
'diversity should be computed.')
},
parameter_descriptions={
'metric': 'The beta diversity metric to be computed.'
},
output_descriptions={'distance_matrix': 'The resulting distance matrix.'},
name='Beta diversity',
description=("Computes a user-specified beta diversity metric for all "
"pairs of samples in a feature table.")
)
plugin.methods.register_function(
"are available, including Generalized UniFrac (Chen et al. "
"2012), Variance Adjusted UniFrac (Chang et al. 2011), "
"as well as Weighted normalized and unnormalized UniFrac "
"(Lozupone et al. 2007) and unweighted UniFrac "
"(Lozupone et al. 2005)"),
citations=[
citations['lozupone2005unifrac'],
citations['lozupone2007quantitative'], citations['chang2011variance'],
citations['chen2012associating']
])
plugin.methods.register_function(
function=q2_diversity.beta,
inputs={'table': FeatureTable[Frequency]},
parameters={
'metric': Str % Choices(beta.non_phylogenetic_metrics()),
'n_jobs': Int
},
outputs=[('distance_matrix', DistanceMatrix)],
input_descriptions={
'table': ('The feature table containing the samples over which beta '
'diversity should be computed.')
},
parameter_descriptions={
'metric': 'The beta diversity metric to be computed.',
'n_jobs': sklearn_n_jobs_description
},
output_descriptions={'distance_matrix': 'The resulting distance matrix.'},
name='Beta diversity',
description=("Computes a user-specified beta diversity metric for all "
"pairs of samples in a feature table."))
'the table, but all feature ids in the table must be '
'present in this tree.')
},
parameter_descriptions={
'metric': 'The beta diversity metric to be computed.'
},
output_descriptions={'distance_matrix': 'The resulting distance matrix.'},
name='Beta diversity (phylogenetic)',
description=("Computes a user-specified phylogenetic beta diversity metric"
" for all pairs of samples in a feature table.")
)
plugin.methods.register_function(
function=q2_diversity.beta,
inputs={'table': FeatureTable[Frequency] % Properties('uniform-sampling')},
parameters={'metric': Str % Choices(beta.non_phylogenetic_metrics())},
outputs=[('distance_matrix', DistanceMatrix)],
input_descriptions={
'table': ('The feature table containing the samples over which beta '
'diversity should be computed.')
},
parameter_descriptions={
'metric': 'The beta diversity metric to be computed.'
},
output_descriptions={'distance_matrix': 'The resulting distance matrix.'},
name='Beta diversity',
description=("Computes a user-specified beta diversity metric for all "
"pairs of samples in a feature table.")
)
plugin.methods.register_function(
