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pcoa: Principal Coordinate AnalysisΒΆ

Citations
  • Nathan Halko, Per-Gunnar Martinsson, Yoel Shkolnisky, and Mark Tygert. An algorithm for the principal component analysis of large data sets. arXiv e-prints, 2010. arXiv:1007.5510.

  • Pierre Legendre and Louis Legendre. Numerical Ecology, pages 499. Elsevier, Third edition, 2012.

Docstring:

Usage: qiime diversity pcoa [OPTIONS]

  Apply principal coordinate analysis.

Inputs:
  --i-distance-matrix ARTIFACT
    DistanceMatrix     The distance matrix on which PCoA should be computed.
                                                                    [required]
Parameters:
  --p-number-of-dimensions INTEGER
    Range(1, None)     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]
Outputs:
  --o-pcoa ARTIFACT    The resulting PCoA matrix.
    PCoAResults                                                     [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.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.