annotate vitessce_spatial.xml @ 0:9f60ef2d586e draft

planemo upload for repository https://github.com/goeckslab/tools-mti/tree/main/tools/vitessce commit 9b2dc921e692af8045773013d9f87d4d790e2ea1

<tool id="vitessce_spatial" name="Vitessce Visualization" version="@TOOL_VERSION@+galaxy@VERSION_SUFFIX@" profile="@PROFILE@">
    <description>integrative visualization of multi-modal, spatial single-cell data</description>

    <macros>
        <import>main_macros.xml</import>
    </macros>

    <expand macro="vitessce_requirements"/>
    <expand macro="macro_stdio" />
    <version_command> "@VERSION@"</version_command>
    <expand macro="vitessce_cmd" />

    <configfiles>
        <inputs name="inputs" />
    </configfiles>

    <inputs>
        <param name="image" type="data" format="ome.tiff" label="Select the OME Tiff image" />
        <param name="masks" type="data" format="tiff,ome.tiff" optional="true" label="Select masks for the OME Tiff image (Optional)" />
        <conditional name="do_phenotyping">
            <param name="phenotyping_choice" type="boolean" checked="true" label="Whether to do phenotyping">
            </param>
            <when value="true">
                <param name="anndata" type="data" format="h5ad" label="Select the anndata contaning phenotyping info" />
                <conditional name="scatterplot_embeddings">
                    <param name="embedding" type="select" label="Select an embedding algorithm for scatterplot">
                        <option value="umap" selected="true">UMAP</option>
                        <option value="tsne">tSNE</option>
                        <option value="pca">PCA</option>
                    </param>
                    <when value="umap">
                        <section name="options" title="Advance Options for neighbor search">
                            <param argument="n_neighbors" type="integer" value="30" label="The size of local neighborhood used for manifold approximation" />
                            <param argument="n_pcs" type="integer" value="10" label="Number of PCs" />
                            <param argument="knn" type="boolean" checked="true" label="Whether to use knn graph" help="If false, use a Gaussian Kernel to assign low weights to neighbors more distant than the n_neighbors nearest neighbor." />
                            <param argument="random_state" type="integer" value="0" optional="true" label="Randomness seed" />
                        </section>
                    </when>
                    <when value="tsne">
                        <section name="options" title="Advance Options for computing tSNE">
                            <param argument="n_pcs" type="integer" value="10" label="Number of PCs" />
                            <param argument="learning_rate" type="float" value="1000" label="Learning rate" help="Should be 100-1000." />
                            <param argument="random_state" type="integer" value="0" optional="true" label="Randomness seed" />
                        </section>
                    </when>
                    <when value="pca">
                        <section name="options" title="Advance Options for computing PCA">
                            <param argument="n_comps" type="integer" value="" optional="true" label="Number of principal components to compute" help="Defaults to 50, or 1 - minimum dimension size of selected representation." />
                            <param argument="zero_center" type="boolean" checked="true" label="Whether to compute standard PCA from covariance matrix" help="If False, omit zero-centering variables (uses TruncatedSVD)" />
                            <param argument="svd_solver" type="select" label="Select the SVD solver">
                                <option value="arpack" selected="true">arpack</option>
                                <option value="randomized">randomized</option>
                                <option value="auto">auto</option>
                                <option value="lobpcg">lobpcg</option>
                            </param>
                            <param argument="random_state" type="integer" value="0" optional="true" label="Randomness seed" />
                        </section>
                    </when>
                </conditional>
                <conditional name="phenotype_factory">
                    <param name="phenotype_mode" type="select" label="Input phenotyping keys">
                        <option value="choices" selected="true">Multiple choices</option>
                        <option value="type_in">Type in</option>
                    </param>
                    <when value="choices">
                        <param name="phenotypes" type="select" multiple="true" display="checkboxes" label="Select the key(s)" >
                            <option value="phenotype" selected="true">'phenotype' (via scimap phenotyping)</option>
                            <option value="kmeans">'kmeans' (via clustering)</option>
                            <option value="leiden">'leiden' (via clustering)</option>
                            <option value="phenograph">'phenograph' (via clustering)</option>
                            <option value="parc">'parc' (via clustering)</option>
                        </param>
                    </when>
                    <when value="type_in">
                        <param name="phenotypes" type="text" value="" label="Type in the keys storing phenotypes" help="Comma delimited for multiple keys."/>
                    </when>
                </conditional>
            </when>
            <when value="false">
            </when>
        </conditional>
    </inputs>
    <outputs>
        <data format="html" name="output" />
    </outputs>
    <tests>
        <test>
            <param name="image" value="cropped_reactive_core.ome.tiff" ftype="ome.tiff" />
            <conditional name="do_phenotyping">
                <param name="phenotyping_choice" value="yes" />
                <param name="anndata" value="cropped_tutorial_data_pheno.h5ad" ftype="h5ad" />
                <conditional name="phenotype_factory">
                    <param name="phenotype_mode" value="type_in" />
                    <param name="phenotypes" value="leiden" />
                </conditional>
            </conditional>
            <output name="output" file="tutorial_vitessce.html" compare="sim_size" delta="20" />
        </test>
        <test>
            <param name="image" value="cropped_reactive_core.ome.tiff" ftype="ome.tiff" />
            <conditional name="do_phenotyping">
                <param name="phenotyping_choice" value="yes" />
                <param name="anndata" value="cropped_tutorial_data_pheno.h5ad" ftype="h5ad" />
                <conditional name="scatterplot_embeddings">
                    <param name="embedding" value="pca" />
                </conditional>
                <conditional name="phenotype_factory">
                    <param name="phenotype_mode" value="type_in" />
                    <param name="phenotypes" value="leiden" />
                </conditional>
            </conditional>
            <output name="output" file="tutorial_vitessce.html" compare="sim_size" delta="20" />
        </test>
        <test>
            <param name="image" value="cropped_reactive_core.ome.tiff" ftype="ome.tiff" />
            <conditional name="do_phenotyping">
                <param name="phenotyping_choice" value="no" />
            </conditional>
            <output name="output" file="tutorial_vitessce.html" compare="sim_size" delta="20" />
        </test>
    </tests>
    <help>
        <![CDATA[
**What it does**
This tools provides web-based, interactive and scalable visualizations of single cell data.

**Input**

OME-TIFF image.
Segmentation masks (optional).
AnnData with marker intensities.

**Output**

An HTML file with Vitessce component. 

        ]]>
    </help>
    <citations>
        <citation type="doi">10.31219/osf.io/y8thv</citation>
    </citations>
</tool>