comparison remove_confounders.xml @ 3:00305dc2dd6f draft

"planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/scanpy/ commit 3b41d687ff30583540d055f6995de00530cca81d"

2:94c8f42efc47

<tool id="scanpy_remove_confounders" name="Remove confounders" version="@version@" profile="@profile@">
    <description>with scanpy</description>
    <macros>
        <import>macros.xml</import>
    </macros>
    <expand macro="requirements"/>

    </configfiles>
    <inputs>
        <expand macro="inputs_anndata"/>
        <conditional name="method">
            <param argument="method" type="select" label="Method used for plotting">
                <option value="pp.regress_out">Regress out unwanted sources of variation, using `pp.regress_out`</option>
                <option value="pp.mnn_correct">Correct batch effects by matching mutual nearest neighbors, using `pp.mnn_correct`</option>
                <option value="pp.combat">Correct batch effects with ComBat function, using `pp.combat`</option>
            </param>
            <when value="pp.regress_out">
                <param argument="keys" type="text" value="" label="Keys for observation annotation on which to regress on" help="Keys separated by a comma"/>
            </when>
            <when value="pp.mnn_correct">
                <param name="extra_adata" type="data" multiple="true" optional="true" format="h5ad" label="Extra annotated data matrix" help="They should have same number of variables."/>
                <param argument="var_subset" type="text" value="" optional="true" label="The subset of vars to be used when performing MNN correction" help="List of comma-separated key from `.var_names`. If not set, all vars are used"/>
                <param argument="batch_key" type="text" value="batch" label="Batch key for the concatenate"/>
                <param name="index_unique" type="select" label="Separator to join the existing index names with the batch category" help="Leave it empty to keep existing indices">
                    <option value="-">-</option>
                    <option value="_">_</option>
                    <option value=" "> </option>

3:00305dc2dd6f

<tool id="scanpy_remove_confounders" name="Remove confounders" version="@galaxy_version@" profile="@profile@">
    <description>with scanpy</description>
    <macros>
        <import>macros.xml</import>
    </macros>
    <expand macro="requirements"/>

    </configfiles>
    <inputs>
        <expand macro="inputs_anndata"/>
        <conditional name="method">
            <param argument="method" type="select" label="Method used for plotting">
                <option value="pp.regress_out">Regress out unwanted sources of variation, using 'pp.regress_out'</option>
                <option value="pp.mnn_correct">Correct batch effects by matching mutual nearest neighbors, using 'pp.mnn_correct'</option>
                <option value="pp.combat">Correct batch effects with ComBat function, using 'pp.combat'</option>
            </param>
            <when value="pp.regress_out">
                <param argument="keys" type="text" value="" label="Keys for observation annotation on which to regress on" help="Keys separated by a comma"/>
            </when>
            <when value="pp.mnn_correct">
                <param name="extra_adata" type="data" multiple="true" optional="true" format="h5ad" label="Extra annotated data matrix" help="They should have same number of variables."/>
                <param argument="var_subset" type="text" value="" optional="true" label="The subset of vars to be used when performing MNN correction" help="List of comma-separated key from '.var_names'. If not set, all vars are used"/>
                <param argument="batch_key" type="text" value="batch" label="Batch key for the concatenate"/>
                <param name="index_unique" type="select" label="Separator to join the existing index names with the batch category" help="Leave it empty to keep existing indices">
                    <option value="-">-</option>
                    <option value="_">_</option>
                    <option value=" "> </option>