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.