comparison scanpy-run-umap.xml @ 1:0ac2f9f2313b draft

"planemo upload for repository https://github.com/ebi-gene-expression-group/container-galaxy-sc-tertiary/tree/develop/tools/tertiary-analysis/scanpy commit 4846776f55931e176f7e77af7c185ec6fec7d142"

0:88c1516e25e0

<?xml version="1.0" encoding="utf-8"?>
<tool id="scanpy_run_umap" name="Scanpy RunUMAP" version="@TOOL_VERSION@+galaxy1">
  <description>visualise cell clusters using UMAP</description>
  <macros>
    <import>scanpy_macros.xml</import>
  </macros>
  <expand macro="requirements"/>
  <command detect_errors="exit_code"><![CDATA[
ln -s '${input_obj_file}' input.h5 &&
PYTHONIOENCODING=utf-8 scanpy-run-umap.py
    -i input.h5
    -f '${input_format}'
    -o output.h5
    -F '${output_format}'
    #if $embeddings
        --output-embeddings-file embeddings.csv
    #end if
    #if $settings.default == "false"
        -n '${settings.n_components}'
        --min-dist '${settings.min_dist}'
        --spread '${settings.spread}'
        --alpha '${settings.alpha}'
        --gamma '${settings.gamma}'
        --negative-sample-rate '${settings.negative_sample_rate}'
        #if $settings.init_pos
            --init-pos '${settings.init_pos}'
        #end if
        #if $settings.maxiter
            --maxiter '${settings.maxiter}'
        #end if
        #if $settings.a
            -a '${settings.a}'
        #end if
        #if $settings.b
            -b '${settings.b}'
        #end if
        #if $settings.random_seed is not None
            -s '${settings.random_seed}'
        #end if
   #end if

@PLOT_OPTS@
]]></command>

  <inputs>
    <expand macro="input_object_params"/>
    <expand macro="output_object_params"/>
    <param name="embeddings" type="boolean" checked="true" label="Output embeddings in csv format"/>
    <conditional name="settings">
      <param name="default" type="boolean" checked="true" label="Use programme defaults"/>
      <when value="true"/>
      <when value="false">
        <param name="n_components" argument="--n-components" type="integer" value="2" label="The number of dimensions of the embedding"/>

          <option value="spectral" selected="true">spectral</option>
          <option value="paga">paga</option>
          <option value="random">random</option>
        </param>
        <param name="maxiter" argument="--maxiter" type="integer" optional="true" label="Number of iterations of optimisation"/>
        <param name="a" argument="-a" type="float" optional="true" label="More specific parameter controlling embedding, automatically determined from --min-dist and --spread if unset"/>
        <param name="b" argument="-b" type="float" optional="true" label="More specific parameter controlling embedding, automatically determined from --min-dist and --spread if unset"/>
        <param name="random_seed" argument="--random-seed" type="integer" value="0" label="Seed for numpy random number generator"/>
      </when>
    </conditional>
    <conditional name="do_plotting">
      <param name="plot" type="boolean" checked="false" label="Make UMAP plot"/>
      <when value="true">
        <expand macro="output_plot_params"/>
        <param name="color_by" argument="--color-by" type="text" value="louvain" label="Color by attributes, comma separated strings"/>
      </when>
      <when value="false"/>
    </conditional>
  </inputs>

  <outputs>
    <data name="output_h5" format="h5" from_work_dir="output.h5" label="${tool.name} on ${on_string}: UMAP object"/>
    <data name="output_png" format="png" from_work_dir="output.png" label="${tool.name} on ${on_string}: UMAP plot">
      <filter>do_plotting['plot']</filter>
    </data>
    <data name="output_embed" format="csv" from_work_dir="embeddings.csv" label="${tool.name} on ${on_string}: UMAP embeddings">
      <filter>embeddings</filter>
    </data>
  </outputs>

      <param name="input_format" value="anndata"/>
      <param name="output_format" value="anndata"/>
      <param name="default" value="false"/>
      <param name="embeddings" value="true"/>
      <param name="random_seed" value="0"/>
      <param name="plot" value="true"/>
      <param name="color_by" value="louvain"/>
      <output name="output_h5" file="run_umap.h5" ftype="h5" compare="sim_size"/>
      <output name="output_png" file="run_umap.png" ftype="png" compare="sim_size"/>
      <output name="output_embed" file="run_umap.embeddings.csv" ftype="csv" compare="sim_size">
        <assert_contents>
          <has_n_columns n="2" sep=","/>
        </assert_contents>
      </output>
    </test>
  </tests>

  <help><![CDATA[
========================================================
Embed the neighborhood graph using UMAP (`tl.umap`)
========================================================

UMAP (Uniform Manifold Approximation and Projection) is a manifold learning
technique suitable for visualizing high-dimensional data. Besides tending to
be faster than tSNE, it optimizes the embedding such that it best reflects
the topology of the data, which we represent throughout Scanpy using a

1:0ac2f9f2313b

<?xml version="1.0" encoding="utf-8"?>
<tool id="scanpy_run_umap" name="Scanpy RunUMAP" version="@TOOL_VERSION@+galaxy1">
  <description>visualise cell clusters using UMAP</description>
  <macros>
    <import>scanpy_macros2.xml</import>
  </macros>
  <expand macro="requirements"/>
  <command detect_errors="exit_code"><![CDATA[
ln -s '${input_obj_file}' input.h5 &&
PYTHONIOENCODING=utf-8 scanpy-run-umap
    --use-graph '${use_graph}'
    --key-added '${key_added}'
#if $embeddings
    --export-embedding embeddings.csv
#end if
#if $settings.default == "false"
    --n-components ${settings.n_components}
    --min-dist ${settings.min_dist}
    --spread ${settings.spread}
    --alpha ${settings.alpha}
    --gamma ${settings.gamma}
    --negative-sample-rate ${settings.negative_sample_rate}
    --random-state ${settings.random_seed}
    #if $settings.init_pos
        --init-pos '${settings.init_pos}'
    #end if
    #if $settings.maxiter
        --maxiter ${settings.maxiter}
    #end if
#end if
    @INPUT_OPTS@
    @OUTPUT_OPTS@

]]></command>

  <inputs>
    <expand macro="input_object_params"/>
    <expand macro="output_object_params"/>
    <param name="embeddings" type="boolean" checked="true" label="Output embeddings in csv format"/>
    <param name="use_graph" argument="--use-graph" value="neighbors" type="text"
           label="Name of the slot that holds the KNN graph"/>
    <param name="key_added" argument="--key-added" type="text" optional="true"
           label="Additional suffix to the name of the slot to save the embedding"/>

    <conditional name="settings">
      <param name="default" type="boolean" checked="true" label="Use programme defaults"/>
      <when value="true"/>
      <when value="false">
        <param name="n_components" argument="--n-components" type="integer" value="2" label="The number of dimensions of the embedding"/>

          <option value="spectral" selected="true">spectral</option>
          <option value="paga">paga</option>
          <option value="random">random</option>
        </param>
        <param name="maxiter" argument="--maxiter" type="integer" optional="true" label="Number of iterations of optimisation"/>
        <param name="random_seed" argument="--random-state" type="integer" value="0" label="Seed for numpy random number generator"/>
      </when>
    </conditional>
  </inputs>

  <outputs>
    <data name="output_h5" format="h5" from_work_dir="output.h5" label="${tool.name} on ${on_string}: UMAP object"/>
    <data name="output_embed" format="csv" from_work_dir="embeddings.csv" label="${tool.name} on ${on_string}: UMAP embeddings">
      <filter>embeddings</filter>
    </data>
  </outputs>

      <param name="input_format" value="anndata"/>
      <param name="output_format" value="anndata"/>
      <param name="default" value="false"/>
      <param name="embeddings" value="true"/>
      <param name="random_seed" value="0"/>
      <output name="output_h5" file="run_umap.h5" ftype="h5" compare="sim_size"/>
      <output name="output_embed" file="run_umap.embeddings.csv" ftype="csv" compare="sim_size">
        <assert_contents>
          <has_n_columns n="2" sep=","/>
        </assert_contents>
      </output>
    </test>
  </tests>

  <help><![CDATA[
==========================================================
Embed the neighborhood graph using UMAP (`scanpy.tl.umap`)
==========================================================

For making UMAP plots, please use `Scanpy PlotEmbed` with the output of this tool and enter "umap" as the
name of the embedding to plot.

UMAP (Uniform Manifold Approximation and Projection) is a manifold learning
technique suitable for visualizing high-dimensional data. Besides tending to
be faster than tSNE, it optimizes the embedding such that it best reflects
the topology of the data, which we represent throughout Scanpy using a