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pcoa: Principal Coordinate AnalysisΒΆ

Docstring:

Usage: qiime diversity pcoa [OPTIONS]

  Apply principal coordinate analysis.

Options:
  --i-distance-matrix ARTIFACT PATH DistanceMatrix
                                  The distance matrix on which PCoA should be
                                  computed.  [required]
  --p-number-of-dimensions INTEGER RANGE
                                  Dimensions to reduce the distance matrix to.
                                  This number determines how many eigenvectors
                                  and eigenvalues are returned,and influences
                                  the choice of algorithm used to compute
                                  them. By default, uses the default
                                  eigendecomposition method, SciPy's eigh,
                                  which computes all eigenvectors and
                                  eigenvalues in an exact manner. For very
                                  large matrices, this is expected to be slow.
                                  If a value is specified for this parameter,
                                  then the fast, heuristic eigendecomposition
                                  algorithm fsvd is used, which only computes
                                  and returns the number of dimensions
                                  specified, but suffers some degree of
                                  accuracy loss, the magnitude of which varies
                                  across different datasets.  [optional]
  --o-pcoa ARTIFACT PATH PCoAResults
                                  The resulting PCoA matrix.  [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.diversity.methods import pcoa

Docstring:

Principal Coordinate Analysis

Apply principal coordinate analysis.

Parameters
----------
distance_matrix : DistanceMatrix
    The distance matrix on which PCoA should be computed.
number_of_dimensions : Int % Range(1, None), optional
    Dimensions to reduce the distance matrix to. This number determines how
    many eigenvectors and eigenvalues are returned,and influences the
    choice of algorithm used to compute them. By default, uses the default
    eigendecomposition method, SciPy's eigh, which computes all
    eigenvectors and eigenvalues in an exact manner. For very large
    matrices, this is expected to be slow. If a value is specified for this
    parameter, then the fast, heuristic eigendecomposition algorithm fsvd
    is used, which only computes and returns the number of dimensions
    specified, but suffers some degree of accuracy loss, the magnitude of
    which varies across different datasets.

Returns
-------
pcoa : PCoAResults
    The resulting PCoA matrix.