<?xml version='1.0' encoding='utf-8'?>
<!--
Copyright (c) 2024, QIIME 2 development team.

Distributed under the terms of the Modified BSD License. (SPDX: BSD-3-Clause)
-->
<!--
This tool was automatically generated by:
    q2galaxy (version: 2024.10.0)
for:
    qiime2 (version: 2024.10.1)
-->
<tool name="qiime2 diversity pcoa" id="qiime2__diversity__pcoa" version="2024.10.0+q2galaxy.2024.10.0" profile="22.05" license="BSD-3-Clause">
    <description>Principal Coordinate Analysis</description>
    <xrefs>
        <xref type="bio.tools">qiime2</xref>
    </xrefs>
    <requirements>
        <container type="docker">quay.io/qiime2/amplicon:2024.10</container>
    </requirements>
    <version_command>q2galaxy version diversity</version_command>
    <command detect_errors="exit_code">q2galaxy run diversity pcoa '$inputs'</command>
    <configfiles>
        <inputs name="inputs" data_style="staging_path_and_source_path"/>
    </configfiles>
    <inputs>
        <param name="distance_matrix" type="data" format="qza" label="distance_matrix: DistanceMatrix" help="[required]  The distance matrix on which PCoA should be computed.">
            <options options_filter_attribute="metadata.semantic_type">
                <filter type="add_value" value="DistanceMatrix"/>
            </options>
            <validator type="expression" message="Incompatible type">hasattr(value.metadata, "semantic_type") and value.metadata.semantic_type in ['DistanceMatrix']</validator>
        </param>
        <section name="__q2galaxy__GUI__section__extra_opts__" title="Click here for additional options">
            <param name="number_of_dimensions" type="integer" min="1" optional="true" label="number_of_dimensions: Int % Range(1, None)" help="[optional]  Dimensions to reduce the distance matrix to. This number determines how many eigenvectors and eigenvalues are returned,and influences the choice of algorithm used to compute them. By default, uses the default eigendecomposition method, SciPy's eigh, which computes all eigenvectors and eigenvalues in an exact manner. For very large matrices, this is expected to be slow. If a value is specified for this parameter, then the fast, heuristic eigendecomposition algorithm fsvd is used, which only computes and returns the number of dimensions specified, but suffers some degree of accuracy loss, the magnitude of which varies across different datasets."/>
        </section>
    </inputs>
    <outputs>
        <data name="pcoa" format="qza" label="${tool.name} on ${on_string}: pcoa.qza" from_work_dir="pcoa.qza"/>
    </outputs>
    <tests/>
    <help>
QIIME 2: diversity pcoa
=======================
Principal Coordinate Analysis


Outputs:
--------
:pcoa.qza: The resulting PCoA matrix.

|  

Description:
------------
Apply principal coordinate analysis.


|  

</help>
    <citations>
        <citation type="bibtex">@inbook{cite1,
 author = {Pierre Legendre and Louis Legendre},
 edition = {Third},
 isbn = {0444-89249},
 pages = {499},
 publisher = {Elsevier},
 title = {Numerical Ecology},
 year = {2012}
}
</citation>
        <citation type="doi">10.1137/100804139</citation>
        <citation type="doi">10.1038/s41587-019-0209-9</citation>
    </citations>
</tool>