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.
Inputs:
--i-table ARTIFACT FeatureTable[Frequency]
The feature table containing the samples in which
the columns will be clustered. [required]
Parameters:
--p-pseudocount NUMBER The value to add to zero counts in the feature
table. [default: 0.5]
Outputs:
--o-clustering ARTIFACT A hierarchy of feature identifiers where each tip
Hierarchy 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).
--example-data PATH Write example data and exit.
--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.
