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:
--m-gradient-file METADATA
--m-gradient-column COLUMN MetadataColumn[Numeric]
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).
--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 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.
gradient : MetadataColumn[Numeric]
Contains gradient values to sort the features and samples.
ignore_missing_samples : Bool, optional
weighted : Bool, optional
Specifies if abundance or presence/absence information should be used
to perform the clustering.
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.
