#### 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.