# correlation-clustering: Hierarchical clustering using feature correlation.¶

#### Docstring:

Usage: qiime gneiss correlation-clustering [OPTIONS]

Build a bifurcating tree that represents a hierarchical clustering of
features.  The hiearchical clustering uses Ward hierarchical clustering
based on the degree of proportionality between features.

Options:
--i-table ARTIFACT PATH FeatureTable[Frequency]
The feature table containing the samples in
which the columns will be clustered.
[required]
--p-pseudocount FLOAT           The value to add to zero counts in the
feature table.  [default: 0.5]
--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 correlation_clustering


#### Docstring:

Hierarchical clustering using feature correlation.

Build a bifurcating tree that represents a hierarchical clustering of
features.  The hiearchical clustering uses Ward hierarchical clustering
based on the degree of proportionality between features.

Parameters
----------
table : FeatureTable[Frequency]
The feature table containing the samples in which the columns will be
clustered.
pseudocount : Float, optional
The value to add to zero counts in the feature table.

Returns
-------
clustering : 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.