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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. SIAM Journal on Scientific Computing, oct 2011. Publisher: Society for Industrial and Applied Mathematics. URL: https://epubs.siam.org/doi/abs/10.1137/100804139 (visited on 2022-02-22), doi:10.1137/100804139.

  • 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).
  --example-data PATH    Write example data and exit.
  --citations            Show citations and exit.
  --use-cache DIRECTORY  Specify the cache to be used for the intermediate
                         work of this action. If not provided, the default
                         cache under $TMP/qiime2/ will be used.
                         IMPORTANT FOR HPC USERS: If you are on an HPC system
                         and are using parallel execution it is important to
                         set this to a location that is globally accessible to
                         all nodes in the cluster.
  --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.