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beta-group-significance: Beta diversity group significance¶
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Docstring:
Usage: qiime diversity beta-group-significance [OPTIONS]
Determine whether groups of samples are significantly different from one
another using a permutation-based statistical test.
Inputs:
--i-distance-matrix ARTIFACT
DistanceMatrix Matrix of distances between pairs of samples.
[required]
Parameters:
--m-metadata-file METADATA
--m-metadata-column COLUMN MetadataColumn[Categorical]
Categorical sample metadata column. [required]
--p-method TEXT Choices('permanova', 'anosim', 'permdisp')
The group significance test to be applied.
[default: 'permanova']
--p-pairwise / --p-no-pairwise
Perform pairwise tests between all pairs of groups
in addition to the test across all groups. This can
be very slow if there are a lot of groups in the
metadata column. [default: False]
--p-permutations INTEGER
The number of permutations to be run when computing
p-values. [default: 999]
Outputs:
--o-visualization VISUALIZATION
[required]
Miscellaneous:
--output-dir PATH Output unspecified results to a directory
--verbose / --quiet Display verbose output to stdout and/or stderr
during execution of this action. Or silence output if
execution is successful (silence is golden).
--example-data PATH Write example data and exit.
--citations Show citations and exit.
--use-cache DIRECTORY Specify the cache to be used for the intermediate
work of this action. If not provided, the default
cache under $TMP/qiime2/ will be used.
IMPORTANT FOR HPC USERS: If you are on an HPC system
and are using parallel execution it is important to
set this to a location that is globally accessible to
all nodes in the cluster.
--help Show this message and exit.
Import:
from qiime2.plugins.diversity.visualizers import beta_group_significance
Docstring:
Beta diversity group significance
Determine whether groups of samples are significantly different from one
another using a permutation-based statistical test.
Parameters
----------
distance_matrix : DistanceMatrix
Matrix of distances between pairs of samples.
metadata : MetadataColumn[Categorical]
Categorical sample metadata column.
method : Str % Choices('permanova', 'anosim', 'permdisp'), optional
The group significance test to be applied.
pairwise : Bool, optional
Perform pairwise tests between all pairs of groups in addition to the
test across all groups. This can be very slow if there are a lot of
groups in the metadata column.
permutations : Int, optional
The number of permutations to be run when computing p-values.
Returns
-------
visualization : Visualization