comparison clustering_from_distmat.xml @ 1:c0b01c55a0e0 draft

planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/clustering_from_distmat/ commit 65b5c6f177478883ce664aeb6f27d0bec7155fdc

0:8192b416f945

<tool id="clustering_from_distmat" name="Distance matrix-based hierarchical clustering" version="1.0" profile="23.0">
    <description>using Scipy</description>
    <edam_topics>
        <edam_topic>topic_2269</edam_topic>
        <edam_topic>topic_0084</edam_topic>
    </edam_topics>
    <edam_operations>

    <command detect_errors="exit_code"><![CDATA[
python '$__tool_directory__/clustering_from_distmat.py'
  '$distmat'
  result
  --method $method
  #if str($cluster_assignment.select) == 'n-cluster':
    --n-clusters $cluster_assignment.n_cluster
  #elif str($cluster_assignment.select) == 'height':
    --height $cluster_assignment.height
  #end if
    ]]></command>
    <inputs>
        <param name="distmat" type="data" format="tabular" label="Distance matrix" />
        <param name="method" type="select" label="Clustering method">

            <option value="weighted">WPGMA (scipy 'weighted' method)</option>
            <option value="centroid">UPGMC (scipy 'centroid' method)</option>
            <option value="median">WPGMC (scipy 'median' method)</option>
            <option value="ward">Ward/Incremental (scipy 'ward' method)</option>
        </param>
        <conditional name="cluster_assignment">
            <param name="select" type="select" label="Generate cluster assignments?">
                <option value="dendrogram-only">No, just generate the dendrogram of clustering results</option>
                <option value="n-cluster">Yes, and divide into specified number of clusters </option>
                <option value="height">Yes, and use distance threshold to divide into clusters</option>
            </param>
            <when value="dendrogram-only" />
            <when value="n-cluster">
                <param name="n_cluster" type="integer" value="5" min="1" label="How many clusters to divide into?" />
                <param name="generate_dendrogram" type="boolean" label="Produce also the dendrogram of clustering results" />
            </when>
            <when value="height">
                <param name="height" type="float" value="5.0" label="Distance threshold for clusters to be reported" />
                <param name="generate_dendrogram" type="boolean" label="Produce also the dendrogram of clustering results" />
            </when>
        </conditional>
    </inputs>
    <outputs>
        <data name="clustering_dendrogram" format="newick" from_work_dir="result.tree.newick" label="${tool.name} on ${on_string}: Dendrogram">

        <test expect_num_outputs="1">
            <param name="distmat" value="test_matrix.tsv"/>
            <conditional name="cluster_assignment">
                <param name="select" value="height" />
                <param name="height" value="18" />
            </conditional>
            <output name="clustering_assignment" ftype="tabular" file="test_assignment_average_h18.tsv" />
        </test>
        <test expect_num_outputs="2">
            <param name="distmat" value="test_matrix.tsv"/>
            <conditional name="cluster_assignment">
                <param name="select" value="n-cluster" />
                <param name="n_cluster" value="4" />
                <param name="generate_dendrogram" value="true" />
            </conditional>
            <output name="clustering_assignment" ftype="tabular" file="test_assignment_average_n4.tsv" />
        </test>
    </tests>

**What it does**

This tool lets you perform hierarchical clustering of samples using the `scipy.cluster.hierarchy.linkage`_ function and any of the clustering methods supported by it.

As input it expects a symmetrical distance matrix with sample names on the first row and in the first column.

The clustering result can be reported in the form of a dendrogram in newick format.

Additionally, the tool can report the assignment of the samples to clusters "cut" from the clustering tree using the `scipy.cluster.hierarchy.cut_tree`_ function.
Reflecting the parameters of that function, you can specify *how* to cut the tree by specifying either the number of clusters to cut into or a distance threshold, i.e., the height at which to cut the tree as SciPy calls it.

1:c0b01c55a0e0

<tool id="clustering_from_distmat" name="Distance matrix-based hierarchical clustering" version="1.1" profile="23.0">
    <description>using Scipy</description>
    <macros>
        <xml name="cluster_assignment_options">
            <param name="min_cluster_size" type="integer" value="2" min="1" label="Mask clusters with less than this number of samples" help="Samples assigned to clusters smaller than this threshold will have '-' in the corresponding cluster ID column" />
            <param name="generate_dendrogram" type="boolean" label="Produce also the dendrogram of clustering results" />
        </xml>
    </macros>
    <edam_topics>
        <edam_topic>topic_2269</edam_topic>
        <edam_topic>topic_0084</edam_topic>
    </edam_topics>
    <edam_operations>

    <command detect_errors="exit_code"><![CDATA[
python '$__tool_directory__/clustering_from_distmat.py'
  '$distmat'
  result
  --method $method
  $missing_names
  #if str($cluster_assignment.select) == 'n-cluster':
    --n-clusters $cluster_assignment.n_cluster
  #elif str($cluster_assignment.select) == 'height':
    --height $cluster_assignment.height
  #end if
  #if str($cluster_assignment.select) != 'dendrogram-only' and $cluster_assignment.min_cluster_size != 2:
    --min-cluster-size $cluster_assignment.min_cluster_size
  #end if
    ]]></command>
    <inputs>
        <param name="distmat" type="data" format="tabular" label="Distance matrix" />
        <param name="method" type="select" label="Clustering method">

            <option value="weighted">WPGMA (scipy 'weighted' method)</option>
            <option value="centroid">UPGMC (scipy 'centroid' method)</option>
            <option value="median">WPGMC (scipy 'median' method)</option>
            <option value="ward">Ward/Incremental (scipy 'ward' method)</option>
        </param>
        <param name="missing_names" type="select" label="How does the input specify sample names?">
            <option value="">First line and first column specify sample names (fully symmetric input)</option>
            <option value="--nr">First line specifies sample names, subsequent lines only data</option>
            <option value="--nc">Each line specifies sample name in first column, first line is not special</option>
        </param>
        <conditional name="cluster_assignment">
            <param name="select" type="select" label="Generate cluster assignments?">
                <option value="dendrogram-only">No, just generate the dendrogram of clustering results</option>
                <option value="n-cluster">Yes, and divide into specified number of clusters </option>
                <option value="height">Yes, and use distance threshold to divide into clusters</option>
            </param>
            <when value="dendrogram-only" />
            <when value="n-cluster">
                <param name="n_cluster" type="integer" value="5" min="1" label="How many clusters to divide into?" />
                <expand macro="cluster_assignment_options" />
            </when>
            <when value="height">
                <param name="height" type="float" value="5.0" label="Distance threshold for clusters to be reported" />
                <expand macro="cluster_assignment_options" />
            </when>
        </conditional>
    </inputs>
    <outputs>
        <data name="clustering_dendrogram" format="newick" from_work_dir="result.tree.newick" label="${tool.name} on ${on_string}: Dendrogram">

        <test expect_num_outputs="1">
            <param name="distmat" value="test_matrix.tsv"/>
            <conditional name="cluster_assignment">
                <param name="select" value="height" />
                <param name="height" value="18" />
                <param name="min_cluster_size" value="1" />
            </conditional>
            <output name="clustering_assignment" ftype="tabular" file="test_assignment_average_h18.tsv" />
        </test>
        <test expect_num_outputs="1">
            <param name="distmat" value="test_matrix.tsv"/>
            <conditional name="cluster_assignment">
                <param name="select" value="height" />
                <param name="height" value="18" />
            </conditional>
            <output name="clustering_assignment" ftype="tabular" file="test_assignment_average_h18_s2.tsv" />
        </test>
        <test expect_num_outputs="2">
            <param name="distmat" value="test_matrix.tsv"/>
            <conditional name="cluster_assignment">
                <param name="select" value="n-cluster" />
                <param name="n_cluster" value="4" />
                <param name="min_cluster_size" value="1" />
                <param name="generate_dendrogram" value="true" />
            </conditional>
            <output name="clustering_assignment" ftype="tabular" file="test_assignment_average_n4.tsv" />
        </test>
        <test expect_num_outputs="2">
            <param name="distmat" value="test_matrix_nr.tsv" />
            <param name="missing_names" value="--nr" />
            <conditional name="cluster_assignment">
                <param name="select" value="n-cluster" />
                <param name="n_cluster" value="4" />
                <param name="min_cluster_size" value="1" />
                <param name="generate_dendrogram" value="true" />
            </conditional>
            <output name="clustering_assignment" ftype="tabular" file="test_assignment_average_n4.tsv" />
        </test>
        <test expect_num_outputs="2">
            <param name="distmat" value="test_matrix_nc.tsv" />
            <param name="missing_names" value="--nc" />
            <conditional name="cluster_assignment">
                <param name="select" value="n-cluster" />
                <param name="n_cluster" value="4" />
                <param name="min_cluster_size" value="1" />
                <param name="generate_dendrogram" value="true" />
            </conditional>
            <output name="clustering_assignment" ftype="tabular" file="test_assignment_average_n4.tsv" />
        </test>
    </tests>

**What it does**

This tool lets you perform hierarchical clustering of samples using the `scipy.cluster.hierarchy.linkage`_ function and any of the clustering methods supported by it.

As input it expects a symmetrical distance matrix with sample names on the first row and/or in the first column.

The clustering result can be reported in the form of a dendrogram in newick format.

Additionally, the tool can report the assignment of the samples to clusters "cut" from the clustering tree using the `scipy.cluster.hierarchy.cut_tree`_ function.
Reflecting the parameters of that function, you can specify *how* to cut the tree by specifying either the number of clusters to cut into or a distance threshold, i.e., the height at which to cut the tree as SciPy calls it.