#### 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.

Options:
--i-table ARTIFACT PATH FeatureTable[Composition | Frequency | RelativeFrequency]
The feature table containing the samples in
which the columns will be clustered.
[required]
Metadata file or artifact viewable as
metadata. This option may be supplied
[required]
Column from metadata file or artifact
values to sort the features and samples.
[required]
--p-weighted / --p-no-weighted  Specifies if abundance or presence/absence
information should be used to perform the
clustering.  [default: True]
--o-clustering ARTIFACT PATH 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 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.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[Composition | Frequency | RelativeFrequency]
The feature table containing the samples in which the columns will be
clustered.
present in this tree.