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pcoa: Principal Coordinate Analysis¶
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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.