comparison rgcca.xml @ 1:4e73ea176c34 draft default tip

"planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/rgcca commit ce05b5eb018ae1c4d580ab5ce1a33896c1aa8c5b"

0:067d45e6caa9

<tool id="rgcca" name="RGCCA" version="@TOOL_VERSION@+galaxy0">

    <description>performs multiblock data analysis of several sets of variables (blocks) observed on the same group of individuals.</description>

    <macros>
        <import>macro.xml</import>

        <edam_operation>operation_0337</edam_operation>
    </edam_operations>

    <requirements>
        <requirement type="package" version="@TOOL_VERSION@">rgccacmd</requirement>
    </requirements>

    <command detect_errors="exit_code"><![CDATA[
        #set data_paths = ",".join([str(_.file_name) for _ in $blocks])
        #set data_names = ",".join([str(_.element_identifier).replace(',', '_') for _ in $blocks])
        Rscript '$__tool_directory__/launcher.R'
            --datasets '${data_paths}'
            --names '${data_names}'
            --o1 '$individual_plot' --o2 '$corcircle' --o3 '$top_variables' --o4 '$ave' --o5 '$design' --o6 '$individual_table' --o7 '$variable_table' --o8 '$rdata'
            $parse.header
            --separator $parse.separator
            $analyse.superblock
            $analyse.scale
            #if $analyse.tau.bool == 'false'
            --penalty $analyse.tau.value
            #else
            --penalty $analyse.tau.bool
            #end if
            --ncomp $analyse.ncomp
            --scheme $analyse.scheme
            #if $analyse.method.family == '1'
            --type pca
            #else
            --type $analyse.method.type
            #end if
            #if $analyse.connection
            --connection $analyse.connection
            #end if
            #if $analyse.supervised.learning_mode == 'supervised'
            --response $analyse.supervised.block_response
            #end if
            #if $graphic.response
            --group $graphic.response
            #end if
            --compx $graphic.compx
            --compy $graphic.compy
            --nmark $graphic.nmark
            $graphic.text

        <param name="output_selector" type="select" multiple="true" label="Outputs">
            <option value="individuals" selected="true">Individual plot</option>
            <option value="corcircle" selected = "true">Corcircle plot</option>
            <option value="top_variables">Top variables plot</option>
            <option value="ave">Averages plot</option>
            <option value="design">Design plot</option>
            <option value="individual_table" selected="true">Individual table</option>
            <option value="variable_table" selected="true">Variable table</option>
            <option value="rdata">RData file</option>
        </param>

            <param name="blocks" value="agriculture.tsv" ftype = "tsv"/>
            <output name="individual_table">
                <assert_contents>
                    <has_n_columns n="4"/>
                    <has_line_matching
                            expression='"agriculture.axis1"\s"agriculture.axis2"\s"superblock.axis1"\s"superblock.axis2"'/>
                    <has_line_matching
                            expression='^.+(\s\-?\d+.\d+){4}$'/>
                </assert_contents>
            </output>
        </test>

                <has_text text="sgcca"/>
            </assert_command>
        </test>

        <test expect_num_outputs="8" expect_exit_code="0">
            <expand macro="output_tests" path="2blocks" compx="3" compy="1"/>
            <param name="blocks" value="agriculture.tsv,politic.tsv"/>
            <section name="analyse">
                <param name="scale" value="false"/>
                <conditional name="tau">
                    <param name="bool" value="false"/>

**Author:**
Etienne CAMENEN


**Contact:**
[e-mail]


**R package:**
The RGCCA package is available from the CRAN repository (https://cran.r-project.org/web/packages/RGCCA).

---------------------------------------------------

==================================
R/SGCCA

A user-friendly multi-blocks analysis (Regularized Generalized Canonical Correlation Analysis, RGCCA) as described in [1] and [2] with all default settings predefined. The software produces figures to explore the analysis' results: individuals and variables projected on two components of the multi-block analysis, list of top variables and explained variance in the model.

**Working example**

    | From Russett data (RGCCA package): https://github.com/rgcca-factory/RGCCA/tree/master/inst/extdata
    | Use *agriculture.tsv* as a block. Add *industry.tsv* and *politic.tsv* as new blocks. *connection.tsv* could be used as a design matrix and *political_system.tsv* as a response variable respectively in analysis and graphic settings.

**Documentation**

- RGCCA: https://cran.r-project.org/web/packages/RGCCA/vignettes/vignette_RGCCA.pdf
- accepted input / output formats: https://github.com/rgcca-factory/RGCCA#input-files
<!-- - tutorial: https://github.com/BrainAndSpineInstitute/rgcca_galaxy/blob/release/0.2/README.md-->

</help>

    <citations>
        <citation type="doi">10.1007/s11336-017-9573-x</citation>

1:4e73ea176c34

<tool id="rgcca" name="RGCCA" version="@TOOL_VERSION@+galaxy@VERSION_SUFFIX@">

    <description>performs multiblock data analysis of several sets of variables (blocks) observed on the same group of individuals.</description>

    <macros>
        <import>macro.xml</import>

        <edam_operation>operation_0337</edam_operation>
    </edam_operations>

    <requirements>
        <requirement type="package" version="@TOOL_VERSION@">rgccacmd</requirement>
        <requirement type="package" version="4.1">r-base</requirement>
    </requirements>

    <command detect_errors="exit_code"><![CDATA[
        #set data_paths = ",".join([str(_.file_name) for _ in $blocks])
        #set data_names = ",".join([str(_.element_identifier).replace(',', '_') for _ in $blocks])
        #set out_files = str($output_selector).split(",")
        Rscript '$__tool_directory__/launcher.R'
            --datasets '${data_paths}'
            --names '${data_names}'
            #if 'individuals' in $out_files
                --o1 '$individual_plot'
            #end if
            #if 'corcircle' in $out_files
                --o2 '$corcircle'
            #end if
            #if 'top_variables' in $out_files
                --o3 '$top_variables'
            #end if
            #if 'ave' in $out_files
                --o4 '$ave'
            #end if
            #if 'design' in $out_files
                --o5 '$design'
            #end if
            #if 'individual_table' in $out_files
                --o6 '$individual_table'
            #end if
            #if 'variable_table' in $out_files
                --o7 '$variable_table'
            #end if
            #if 'rdata' in $out_files
                --o8 '$rdata'
            #end if
            $parse.header
            --separator $parse.separator
            $analyse.superblock
            $analyse.scale
            #if $analyse.tau.bool == 'false'
                --penalty $analyse.tau.value
            #else
                --penalty $analyse.tau.bool
            #end if
            --ncomp $analyse.ncomp
            --scheme $analyse.scheme
            #if $analyse.method.family == '1'
                --type pca
            #else
                --type $analyse.method.type
            #end if
            #if $analyse.connection
                --connection $analyse.connection
            #end if
            #if $analyse.supervised.learning_mode == 'supervised'
                --response $analyse.supervised.block_response
            #end if
            #if $graphic.response
                --group $graphic.response
            #end if
            --compx $graphic.compx
            --compy $graphic.compy
            --nmark $graphic.nmark
            $graphic.text

        <param name="output_selector" type="select" multiple="true" label="Outputs">
            <option value="individuals" selected="true">Individual plot</option>
            <option value="corcircle" selected = "true">Corcircle plot</option>
            <option value="top_variables">Top variables plot</option>
            <option value="ave">Explained variance plot</option>
            <option value="design">Design plot</option>
            <option value="individual_table" selected="true">Individual table</option>
            <option value="variable_table" selected="true">Variable table</option>
            <option value="rdata">RData file</option>
        </param>

            <param name="blocks" value="agriculture.tsv" ftype = "tsv"/>
            <output name="individual_table">
                <assert_contents>
                    <has_n_columns n="4"/>
                    <has_line_matching
                            expression='"agriculture.component1"\s"agriculture.component2"\s"superblock.component1"\s"superblock.component2"'/>
                    <has_line_matching
                            expression='^.+(\s\-?\d+.\d+){4}$'/>
                </assert_contents>
            </output>
        </test>

                <has_text text="sgcca"/>
            </assert_command>
        </test>

        <test expect_num_outputs="8" expect_exit_code="0">
            <expand macro="output_tests" path="2blocks"/>
            <param name="blocks" value="agriculture.tsv,politic.tsv"/>
            <section name="analyse">
                <param name="scale" value="false"/>
                <conditional name="tau">
                    <param name="bool" value="false"/>

**Author:**
Etienne CAMENEN


**Contact:**
[e-mail]


**R package:**
    | The RGCCA package is available from the CRAN repository (v2.1.2; https://cran.r-project.org/web/packages/RGCCA).
    | This tool is based on a version available on github (v3.0; https://github.com/rgcca-factory/RGCCA).

---------------------------------------------------

==================================
R/SGCCA

A user-friendly multi-blocks analysis (Regularized Generalized Canonical Correlation Analysis, RGCCA) as described in [1] and [2] with all default settings predefined. The software produces figures to explore the analysis' results: individuals and variables projected on two components of the multi-block analysis, list of top variables and explained variance in the model.

**Working example**

    | From Russett data (RGCCA package): https://github.com/BrainAndSpineInstitute/rgcca_ui/tree/master/inst/extdata
    | Use *agriculture.tsv* as a block. Add *industry.tsv* and *politic.tsv* for multiblock analysis. *connection.tsv* could be used as a design matrix and *political_system.tsv* as a response variable respectively in analysis and graphic settings.

**Documentation**

- RGCCA: https://cran.r-project.org/web/packages/RGCCA/vignettes/vignette_RGCCA.pdf
- accepted input / https://github.com/BrainAndSpineInstitute/rgcca_ui#input-files
- tutorial: https://github.com/BrainAndSpineInstitute/rgcca_galaxy#readme

</help>

    <citations>
        <citation type="doi">10.1007/s11336-017-9573-x</citation>