comparison cluster_reduce_dimension.xml @ 5:6f2d2c7f77ee draft

"planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/scanpy/ commit 6b5d0d6f038ebd0fae5dbca02ada51555518ed85"

4:766be978777a

<tool id="scanpy_cluster_reduce_dimension" name="Cluster," version="@galaxy_version@" profile="@profile@">
    <description>infer trajectories and embed with scanpy</description>
    <macros>
        <import>macros.xml</import>
        <xml name="pca_inputs">
            <param argument="n_comps" type="integer" min="0" value="50" label="Number of principal components to compute" help=""/>
            <param argument="dtype" type="text" value="float32" label="Numpy data type string to which to convert the result" help=""/>

of Traag et al,2017. The Louvain algorithm has been proposed for single-cell
analysis by Levine et al, 2015.

This requires to run `pp.neighbors`, first.

More details on the `scanpy documentation
<https://icb-scanpy.readthedocs-hosted.com/en/stable/api/scanpy.tl.louvain.html>`_

Cluster cells into subgroups (`tl.leiden`)
==========================================

Cluster cells using the Leiden algorithm (Traag et al, 2018), an improved version of the Louvain algorithm (Blondel et al, 2008).

The Louvain algorithm has been proposed for single-cell analysis by Levine et al, 2015.

More details on the `scanpy documentation
<https://icb-scanpy.readthedocs-hosted.com/en/stable/api/scanpy.tl.leiden.html>`_

Computes PCA (principal component analysis) coordinates, loadings and variance decomposition, using `pp.pca`
============================================================================================================

@CMD_pca_outputs@

More details on the `scanpy documentation
<https://icb-scanpy.readthedocs-hosted.com/en/stable/api/scanpy.pp.pca.html>`__

Computes PCA (principal component analysis) coordinates, loadings and variance decomposition, using `tl.pca`
============================================================================================================

@CMD_pca_outputs@

More details on the `scanpy documentation
<https://icb-scanpy.readthedocs-hosted.com/en/stable/api/scanpy.tl.pca.html>`__

Diffusion Maps, using `tl.diffmap`
==================================

Diffusion maps (Coifman et al  2005) has been proposed for visualizing single-cell

The diffusion map representation of data are added to the return AnnData in the multi-dimensional 
observations annotation (obsm). It is the right eigen basis of the transition matrix with eigenvectors 
as colum. It can be accessed using the inspect tool for AnnData

More details on the `scanpy documentation
<https://icb-scanpy.readthedocs-hosted.com/en/stable/api/scanpy.tl.diffmap.html>`__

t-distributed stochastic neighborhood embedding (tSNE), using `tl.tsne`
=======================================================================

t-distributed stochastic neighborhood embedding (tSNE) (Maaten et al, 2008) has been
proposed for visualizating single-cell data by (Amir et al, 2013). Here, by default,
we use the implementation of *scikit-learn* (Pedregosa et al, 2011). 

It returns `X_tsne`, tSNE coordinates of data.

More details on the `scanpy documentation
<https://icb-scanpy.readthedocs-hosted.com/en/stable/api/scanpy.tl.tsne.html>`__

Embed the neighborhood graph using UMAP, using `tl.umap`
========================================================

UMAP (Uniform Manifold Approximation and Projection) is a manifold learning

<https://doi.org/10.1101/298430>`__.

The UMAP coordinates of data are added to the return AnnData in the multi-dimensional 
observations annotation (obsm). This data is accessible using the inspect tool for AnnData

More details on the `scanpy documentation
<https://icb-scanpy.readthedocs-hosted.com/en/stable/api/scanpy.tl.umap.html>`__

Force-directed graph drawing, using `tl.draw_graph`
===================================================

Force-directed graph drawing describes a class of long-established algorithms for visualizing graphs. 

The default layout (ForceAtlas2) uses the package fa2.

The coordinates of graph layout are added to the return AnnData in the multi-dimensional 
observations annotation (obsm). This data is accessible using the inspect tool for AnnData.

More details on the `scanpy documentation
<https://icb-scanpy.readthedocs-hosted.com/en/stable/api/scanpy.tl.draw_graph.html>`__

Infer progression of cells through geodesic distance along the graph (`tl.dpt`)
===============================================================================

Reconstruct the progression of a biological process from snapshot

- dpt_groups : Array of dim (number of samples) that stores the subgroup id ('0','1', ...) for each cell. The groups  typically correspond to 'progenitor cells', 'undecided cells' or 'branches' of a process.

The tool is similar to the R package `destiny` of Angerer et al (2016).

More details on the `tl.dpt scanpy documentation
<https://icb-scanpy.readthedocs-hosted.com/en/stable/api/scanpy.tl.dpt.html>`_


Generate cellular maps of differentiation manifolds with complex topologies (`tl.paga`)
=======================================================================================

- Adjacency matrix of the tree-like subgraph that best explains the topology (connectivities_tree)

These datasets are stored in the unstructured annotation (uns) and can be accessed using the inspect tool for AnnData objects

More details on the `tl.paga scanpy documentation
<https://icb-scanpy.readthedocs-hosted.com/en/stable/api/scanpy.tl.paga.html>`_
    ]]></help>
    <expand macro="citations"/>
</tool>

5:6f2d2c7f77ee

<tool id="scanpy_cluster_reduce_dimension" name="Cluster, infer trajectories and embed" version="@galaxy_version@" profile="@profile@">
    <description>with scanpy</description>
    <macros>
        <import>macros.xml</import>
        <xml name="pca_inputs">
            <param argument="n_comps" type="integer" min="0" value="50" label="Number of principal components to compute" help=""/>
            <param argument="dtype" type="text" value="float32" label="Numpy data type string to which to convert the result" help=""/>

of Traag et al,2017. The Louvain algorithm has been proposed for single-cell
analysis by Levine et al, 2015.

This requires to run `pp.neighbors`, first.

More details on the `tl.louvain scanpy documentation
<https://icb-scanpy.readthedocs-hosted.com/en/@version@/api/scanpy.tl.louvain.html>`_

Cluster cells into subgroups (`tl.leiden`)
==========================================

Cluster cells using the Leiden algorithm (Traag et al, 2018), an improved version of the Louvain algorithm (Blondel et al, 2008).

The Louvain algorithm has been proposed for single-cell analysis by Levine et al, 2015.

More details on the `tl.leiden scanpy documentation
<https://icb-scanpy.readthedocs-hosted.com/en/@version@/api/scanpy.tl.leiden.html>`_

Computes PCA (principal component analysis) coordinates, loadings and variance decomposition, using `pp.pca`
============================================================================================================

@CMD_pca_outputs@

More details on the `pp.pca scanpy documentation
<https://icb-scanpy.readthedocs-hosted.com/en/@version@/api/scanpy.pp.pca.html>`__

Computes PCA (principal component analysis) coordinates, loadings and variance decomposition, using `tl.pca`
============================================================================================================

@CMD_pca_outputs@

More details on the `tl.pca scanpy documentation
<https://icb-scanpy.readthedocs-hosted.com/en/@version@/api/scanpy.tl.pca.html>`__

Diffusion Maps, using `tl.diffmap`
==================================

Diffusion maps (Coifman et al  2005) has been proposed for visualizing single-cell

The diffusion map representation of data are added to the return AnnData in the multi-dimensional 
observations annotation (obsm). It is the right eigen basis of the transition matrix with eigenvectors 
as colum. It can be accessed using the inspect tool for AnnData

More details on the `tl.diffmap scanpy documentation
<https://icb-scanpy.readthedocs-hosted.com/en/@version@/api/scanpy.tl.diffmap.html>`__

t-distributed stochastic neighborhood embedding (tSNE), using `tl.tsne`
=======================================================================

t-distributed stochastic neighborhood embedding (tSNE) (Maaten et al, 2008) has been
proposed for visualizating single-cell data by (Amir et al, 2013). Here, by default,
we use the implementation of *scikit-learn* (Pedregosa et al, 2011). 

It returns `X_tsne`, tSNE coordinates of data.

More details on the `tl.tsne scanpy documentation
<https://icb-scanpy.readthedocs-hosted.com/en/@version@/api/scanpy.tl.tsne.html>`__

Embed the neighborhood graph using UMAP, using `tl.umap`
========================================================

UMAP (Uniform Manifold Approximation and Projection) is a manifold learning

<https://doi.org/10.1101/298430>`__.

The UMAP coordinates of data are added to the return AnnData in the multi-dimensional 
observations annotation (obsm). This data is accessible using the inspect tool for AnnData

More details on the `tl.umap scanpy documentation
<https://icb-scanpy.readthedocs-hosted.com/en/@version@/api/scanpy.tl.umap.html>`__

Force-directed graph drawing, using `tl.draw_graph`
===================================================

Force-directed graph drawing describes a class of long-established algorithms for visualizing graphs. 

The default layout (ForceAtlas2) uses the package fa2.

The coordinates of graph layout are added to the return AnnData in the multi-dimensional 
observations annotation (obsm). This data is accessible using the inspect tool for AnnData.

More details on the `tl.draw_graph scanpy documentation
<https://icb-scanpy.readthedocs-hosted.com/en/@version@/api/scanpy.tl.draw_graph.html>`__

Infer progression of cells through geodesic distance along the graph (`tl.dpt`)
===============================================================================

Reconstruct the progression of a biological process from snapshot

- dpt_groups : Array of dim (number of samples) that stores the subgroup id ('0','1', ...) for each cell. The groups  typically correspond to 'progenitor cells', 'undecided cells' or 'branches' of a process.

The tool is similar to the R package `destiny` of Angerer et al (2016).

More details on the `tl.dpt scanpy documentation
<https://icb-scanpy.readthedocs-hosted.com/en/@version@/api/scanpy.tl.dpt.html>`_


Generate cellular maps of differentiation manifolds with complex topologies (`tl.paga`)
=======================================================================================

- Adjacency matrix of the tree-like subgraph that best explains the topology (connectivities_tree)

These datasets are stored in the unstructured annotation (uns) and can be accessed using the inspect tool for AnnData objects

More details on the `tl.paga scanpy documentation
<https://icb-scanpy.readthedocs-hosted.com/en/@version@/api/scanpy.tl.paga.html>`_
    ]]></help>
    <expand macro="citations"/>
</tool>