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gradient-clustering: Hierarchical clustering using gradient information.ΒΆ

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-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).
  --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.
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.