comparison scHicClusterSVL.xml @ 1:eeb26f378f34 draft

"planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/schicexplorer commit 72e1e90ac05a32dbd6fc675073429c0086048b18"

0:7cbd84a1f985

    </macros>
    <expand macro="requirements" />
    <command detect_errors="exit_code"><![CDATA[
        @BINARY@

        --matrix '$matrix_mcooler'

        --numberOfClusters $numberOfClusters
        --distanceShortRange $distanceShortRange
        --distanceLongRange $distanceLongRange

        --threads @THREADS@
    ]]></command>
    <inputs>
        
        <expand macro="matrix_mcooler_macro"/>
        <param name="clusterMethod_selector" type="select" label="Cluster method:">
                <option value="kmeans" selected="True">K-means</option>
                <option value="spectral" >Spectral clustering</option>
        </param>

    <outputs>
        <data name="outFileName" from_work_dir="cluster_list.txt" format="txt" label="${tool.name} on ${on_string}: Cluster results"/>
    </outputs>
    <tests>
        <test>
            <param name='matrix_mcooler' value='test_matrix.mcool' />
            <param name='clusterMethod_selector' value='kmeans' />
            <param name='numberOfClusters' value='3' />
            <param name='distanceShortRange' value='2000000' />
            <param name='distanceLongRange' value='12000000' />

            <output name="outFileName" file="scHicClusterSVL/cluster_kmeans.txt" ftype="txt" compare="sim_size" delta="4000"/>
        </test>

        <test>
            <param name='matrix_mcooler' value='test_matrix.mcool' />
            <param name='clusterMethod_selector' value='spectral' />
            <param name='numberOfClusters' value='3' />
            <param name='distanceShortRange' value='2000000' />
            <param name='distanceLongRange' value='12000000' />

Clustering with dimension reduction via short vs long range ratio
=================================================================

scHicClusterSVL uses kmeans or spectral clustering to associate each cell to a cluster and therefore to its cell cycle. 
The clustering is applied on dimension reduced data based on the ratio of short vs long range contacts per chromosome. This approach reduces the number of dimensions from samples * (number of bins)^2 to samples * (number of chromosomes). 
Please consider also the other clustering and dimension reduction approaches of the scHicExplorer suite. They can give you better results, 
can be faster or less memory demanding.

For more information about scHiCExplorer please consider our documentation on readthedocs.io_

.. _readthedocs.io: http://schicexplorer.readthedocs.io/

1:eeb26f378f34

    </macros>
    <expand macro="requirements" />
    <command detect_errors="exit_code"><![CDATA[
        @BINARY@

        --matrix '$matrix_scooler'

        --numberOfClusters $numberOfClusters
        --distanceShortRange $distanceShortRange
        --distanceLongRange $distanceLongRange

        --threads @THREADS@
    ]]></command>
    <inputs>
        
        <expand macro="matrix_scooler_macro"/>
        <param name="clusterMethod_selector" type="select" label="Cluster method:">
                <option value="kmeans" selected="True">K-means</option>
                <option value="spectral" >Spectral clustering</option>
        </param>

    <outputs>
        <data name="outFileName" from_work_dir="cluster_list.txt" format="txt" label="${tool.name} on ${on_string}: Cluster results"/>
    </outputs>
    <tests>
        <test>
            <param name='matrix_scooler' value='test_matrix.scool' />
            <param name='clusterMethod_selector' value='kmeans' />
            <param name='numberOfClusters' value='3' />
            <param name='distanceShortRange' value='2000000' />
            <param name='distanceLongRange' value='12000000' />

            <output name="outFileName" file="scHicClusterSVL/cluster_kmeans.txt" ftype="txt" compare="sim_size" delta="4000"/>
        </test>

        <test>
            <param name='matrix_scooler' value='test_matrix.scool' />
            <param name='clusterMethod_selector' value='spectral' />
            <param name='numberOfClusters' value='3' />
            <param name='distanceShortRange' value='2000000' />
            <param name='distanceLongRange' value='12000000' />

Clustering with dimension reduction via short vs long range ratio
=================================================================

scHicClusterSVL uses kmeans or spectral clustering to associate each cell to a cluster and therefore to its cell cycle. 
The clustering is applied on dimension reduced data based on the ratio of short vs long range contacts per chromosome. This approach reduces the number of dimensions from samples * (number of bins)^2 to samples * (number of chromosomes). 
Please consider also the other clustering and dimension reduction approaches of the scHicExplorer suite such as `scHicCluster`, `scHicClusterMinHash` and `scHicClusterSVL`. They can give you better results, 
can be faster or less memory demanding.

For more information about scHiCExplorer please consider our documentation on readthedocs.io_

.. _readthedocs.io: http://schicexplorer.readthedocs.io/