Docstring:
Usage: qiime diversity beta [OPTIONS]
Computes a user-specified beta diversity metric for all pairs of samples
in a feature table.
Options:
--i-table ARTIFACT PATH FeatureTable[Frequency]
The feature table containing the samples
over which beta diversity should be
computed. [required]
--p-metric [seuclidean|dice|sokalsneath|yule|aitchison|braycurtis|kulsinski|cityblock|russellrao|hamming|euclidean|cosine|canberra|wminkowski|rogerstanimoto|matching|sokalmichener|canberra_adkins|jaccard|chebyshev|sqeuclidean|mahalanobis|correlation]
The beta diversity metric to be computed.
[required]
--p-pseudocount INTEGER RANGE A pseudocount to handle zeros for
compositional metrics. This is ignored for
other metrics. [default: 1]
--p-n-jobs INTEGER The number of jobs to use for the
computation. This works by breaking down the
pairwise matrix into n_jobs even slices and
computing them in parallel. If -1 all CPUs
are used. If 1 is given, no parallel
computing code is used at all, which is
useful for debugging. For n_jobs below -1,
(n_cpus + 1 + n_jobs) are used. Thus for
n_jobs = -2, all CPUs but one are used.
(Description from
sklearn.metrics.pairwise_distances)
[default: 1]
--o-distance-matrix ARTIFACT PATH DistanceMatrix
The resulting distance matrix. [required if
not passing --output-dir]
--output-dir DIRECTORY Output unspecified results to a directory
--cmd-config FILE Use config file for command options
--verbose Display verbose output to stdout and/or
stderr during execution of this action.
[default: False]
--quiet Silence output if execution is successful
(silence is golden). [default: False]
--citations Show citations and exit.
--help Show this message and exit.
Import:
from qiime2.plugins.diversity.methods import beta
Docstring:
Beta diversity
Computes a user-specified beta diversity metric for all pairs of samples in
a feature table.
Parameters
----------
table : FeatureTable[Frequency]
The feature table containing the samples over which beta diversity
should be computed.
metric : Str % Choices({'aitchison', 'braycurtis', 'canberra', 'canberra_adkins', 'chebyshev', 'cityblock', 'correlation', 'cosine', 'dice', 'euclidean', 'hamming', 'jaccard', 'kulsinski', 'mahalanobis', 'matching', 'rogerstanimoto', 'russellrao', 'seuclidean', 'sokalmichener', 'sokalsneath', 'sqeuclidean', 'wminkowski', 'yule'})
The beta diversity metric to be computed.
pseudocount : Int % Range(1, None), optional
A pseudocount to handle zeros for compositional metrics. This is
ignored for other metrics.
n_jobs : Int, optional
The number of jobs to use for the computation. This works by breaking
down the pairwise matrix into n_jobs even slices and computing them in
parallel. If -1 all CPUs are used. If 1 is given, no parallel computing
code is used at all, which is useful for debugging. For n_jobs below
-1, (n_cpus + 1 + n_jobs) are used. Thus for n_jobs = -2, all CPUs but
one are used. (Description from sklearn.metrics.pairwise_distances)
Returns
-------
distance_matrix : DistanceMatrix
The resulting distance matrix.
