#### Docstring:

Usage: qiime gneiss gradient-clustering [OPTIONS]

Build a bifurcating tree that represents a hierarchical clustering of
features.  The hiearchical clustering uses Ward hierarchical clustering
based on the mean difference of gradients that each feature is observed
in. This method is primarily used to sort the table to reveal the
underlying block-like structures.

Inputs:
--i-table ARTIFACT FeatureTable[Frequency | RelativeFrequency |
Composition]       The feature table containing the samples in which the
columns will be clustered.                   [required]
Parameters:
Contains gradient values to sort the features and
samples.                                     [required]
--p-ignore-missing-samples / --p-no-ignore-missing-samples
[default: False]
--p-weighted / --p-no-weighted
Specifies if abundance or presence/absence information
should be used to perform the clustering.
[default: True]
Outputs:
--o-clustering ARTIFACT
Hierarchy          A hierarchy of feature identifiers where each tip
corresponds to the feature identifiers in the table.
This tree can contain tip ids that are not present in
the table, but all feature ids in the table must be
present in this tree.                        [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).
--examples           Show usage examples and exit.
--citations          Show citations and exit.
--help               Show this message and exit.

#### Import:

from qiime2.plugins.gneiss.methods import gradient_clustering


#### Docstring:

Hierarchical clustering using gradient information.

Build a bifurcating tree that represents a hierarchical clustering of
features.  The hiearchical clustering uses Ward hierarchical clustering
based on the mean difference of gradients that each feature is observed in.
This method is primarily used to sort the table to reveal the underlying
block-like structures.

Parameters
----------
table : FeatureTable[Frequency | RelativeFrequency | Composition]
The feature table containing the samples in which the columns will be
clustered.
present in this tree.