comparison dimet_metabologram.xml @ 2:7ff4f61789de draft

planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/DIMet commit 30fe10acdf65c6917856a0eae21dc91abd2f609f

1:8d8ae3697a65

    <command detect_errors="exit_code"><![CDATA[
    @INIT_CONFIG@
    @INIT_METABOLOGRAM@
    @INIT_TRANSCRIPTS@
    @INIT_GROUPS@
    @INIT_COMPARISONS@
    @INIT_PATHWAYS@
    @INIT_STAT_TEST@
    HYDRA_FULL_ERROR=1 python -m dimet
    -cp '$__new_file_path__/config'
    '++figure_path=figures'

            color_nan_elements:'gray',
            fig_height: ${output_options.fig_height}
        },
        columns_metabolites: {ID: metabolite, values: log2FC},
        columns_transcripts: {ID: ${deg_one_id}, values: ${deg_one_values}},
        compartment: cell,
        label: metabologram
    }'
    '++analysis.method.qualityDistanceOverSpan='${qualityDistanceOverSpan}''
    '++analysis.dataset.label='
    '+analysis.timepoints=${timepoints}'

    '++analysis.method.grouping=${groups}'
    '++analysis.method.correction_method=${correction_method}'
    '++analysis.method.impute_values=${impute_values}'
    '++analysis.statistical_test=${statistical_test}'
    '++analysis.dataset.subfolder='
    '++analysis.dataset.conditions=${conds}'
    '++analysis.dataset.pathways=${pathways}'
    '++analysis.dataset.transcripts=${transcripts}'
    '++analysis.comparisons=${comparisons}'
    #if $metadata_path:
        '++analysis.dataset.metadata=metadata'

    @REMOVE_CONFIG@
    ]]></command>
    <inputs>
        <expand macro="input_parameters_metabologram"/>
        <expand macro="deg_list"/>
        <expand macro="conditions"/>
        <expand macro="timepoint"/>
        <param name="correction_method" type="select" value="bonferroni" display="radio" label="Select multiple test correction to apply" help="Please enter at max 1 method">
                <option value="bonferroni">bonferroni</option>
                <option value="holm-sidak">holm-sidak</option>
                <option value="holm">holm</option>
                <option value="simes-hochberg">simes-hochberg</option>

            <discover_datasets pattern="__designation_and_ext__" directory="figures"/>
        </collection>
    </outputs>
    <tests>
        <test>
            <param name="input" ftype="tabular" value="DEG_comparison_1.csv"/>
            <param name="idcol" ftype="integer" value="2"/>
            <param name="valuecol" ftype="integer" value="3"/>
            <param name="path_kegg_metabolites" ftype="tabular" value="pathways_kegg_metabolites.csv"/>
            <param name="path_kegg_transcripts" ftype="tabular" value="pathways_kegg_transcripts.csv"/>
            <param name="abundance_file" ftype="tabular" value="rawAbundances.csv"/>
            <param name="metadata_path" ftype="tabular" value="example2_metadata.csv"/>
            <param name="metabolites_list" value="Fumaric_acid,Glycine,L-Proline"/>
            <param name="stat_test" value="Tt"/>
            <param name="conditions" value='Control,L-Cycloserine'/>
            <param name="timepoint" value='T0'/>
            <section name="output_options">
                <param name="figure_format" value="svg"/>
                <param name="figure_width" value="7"/>
                <param name="figure_height" value="7"/>
                <param name="font_size" value="12"/>

    <help><![CDATA[
This module is part of DIMet: Differential analysis of Isotope-labeled targeted Metabolomics data (https://pypi.org/project/DIMet/).

DIMet Metabologram integrates tracer metabolomics and transcriptomics, in a pathway based fashion.
More precisely, the differential information (Fold Changes (log2 transformed, or not)) of both types of omics
must be given as input, plus the files defining the pathways. You can use the minimal data examples from https://zenodo.org/records/8380706  that contain a minimal example data for running our DIMet Metabologram tool.

The figures in .pdf format are of publication quality, and as they are vectorial images you can open them and customize aesthetics with a professional image software such as Inkscape, Adobe Illustrator, Sketch,  CorelDRAW, etc.


**Input data files**

  2.1 The table with the results of the differential expression analysis (performed with an external tool)


3. **The pathways files**:

  3.1 A file with the pathways and respective gene symbols, which must match with those present in the transcriptomics data. The names of the columns must be the pathways' names, see the minimal data example downloaded from zenodo as explained above.

  3.2 A file with the pathways and respective metabolites ID, that must match with those in your metabolomics data. The names of the columns must be the pathways' names, see the minimal data example downloaded from zenodo as explained above.



**Measures' files**

The measure's files must be organized as matrices:
- The first column must contain Metabolite IDs that are unique (not repeated) within the file.

The output consists of one figure by metabologram,  and a color key bar legend valid for all metabolograms produced

**Available data for testing**

You can test our tool with the data from our manuscript https://zenodo.org/record/8378887 (the pertinent
files for you are located in the subfolders inside the data folder).
You can also use the minimal data examples from https://zenodo.org/record/8380706

 ]]>
    </help>
    <expand macro="citations" />
</tool> 

2:7ff4f61789de

    <command detect_errors="exit_code"><![CDATA[
    @INIT_CONFIG@
    @INIT_METABOLOGRAM@
    @INIT_TRANSCRIPTS@
    @INIT_GROUPS@
    @INIT_COMPARISONS_METABOLOGRAM@
    @INIT_PATHWAYS@
    @INIT_STAT_TEST@
    HYDRA_FULL_ERROR=1 python -m dimet
    -cp '$__new_file_path__/config'
    '++figure_path=figures'

            color_nan_elements:'gray',
            fig_height: ${output_options.fig_height}
        },
        columns_metabolites: {ID: metabolite, values: log2FC},
        columns_transcripts: {ID: ${deg_one_id}, values: ${deg_one_values}},
        compartment: ${compartments},
        label: metabologram
    }'
    '++analysis.method.qualityDistanceOverSpan='${qualityDistanceOverSpan}''
    '++analysis.dataset.label='
    '+analysis.timepoints=${timepoints}'

    '++analysis.method.grouping=${groups}'
    '++analysis.method.correction_method=${correction_method}'
    '++analysis.method.impute_values=${impute_values}'
    '++analysis.statistical_test=${statistical_test}'
    '++analysis.dataset.subfolder='
    '++analysis.dataset.conditions=${$conditions}'
    '++analysis.dataset.pathways=${pathways}'
    '++analysis.dataset.transcripts=${transcripts}'
    '++analysis.comparisons=${comparisons}'
    #if $metadata_path:
        '++analysis.dataset.metadata=metadata'

    @REMOVE_CONFIG@
    ]]></command>
    <inputs>
        <expand macro="input_parameters_metabologram"/>
        <expand macro="deg_list"/>
        <expand macro="compartments_metabologram"/>
        <param name="correction_method" type="select" value="bonferroni" display="radio" label="Select multiple test correction to apply" help="Please enter at max 1 method">
                <option value="bonferroni">bonferroni</option>
                <option value="holm-sidak">holm-sidak</option>
                <option value="holm">holm</option>
                <option value="simes-hochberg">simes-hochberg</option>

            <discover_datasets pattern="__designation_and_ext__" directory="figures"/>
        </collection>
    </outputs>
    <tests>
        <test>

            <param name="path_kegg_metabolites" ftype="tabular" value="pathways_kegg_metabolites.csv"/>
            <param name="path_kegg_transcripts" ftype="tabular" value="pathways_kegg_transcripts.csv"/>
            <param name="abundance_file" ftype="tabular" value="rawAbundances.csv"/>
            <param name="metadata_path" ftype="tabular" value="example2_metadata.csv"/>
            <param name="metabolites_list" value="Fumaric_acid,Glycine,L-Proline"/>
            <param name="statistical_test_type" value="parametric"/>
            <param name="stat_test" value="Tt"/>
            <repeat name="deg_list">
                <param name="input" ftype="tabular" value="DEG_comparison_1.csv"/>
                <param name="idcol" ftype="integer" value="2"/>
                <param name="valuecol" ftype="integer" value="3"/>
                <param name="timepoint" value='T0'/>
                <repeat name="factor_list">
                    <param name="condition" value="Control"/>
                </repeat>
                <repeat name="factor_list">
                    <param name="condition" value="L-Cycloserine"/>
                </repeat>
            </repeat>
            <section name="output_options">
                <param name="figure_format" value="svg"/>
                <param name="figure_width" value="7"/>
                <param name="figure_height" value="7"/>
                <param name="font_size" value="12"/>

    <help><![CDATA[
This module is part of DIMet: Differential analysis of Isotope-labeled targeted Metabolomics data (https://pypi.org/project/DIMet/).

DIMet Metabologram integrates tracer metabolomics and transcriptomics, in a pathway based fashion.
More precisely, the differential information (Fold Changes (log2 transformed, or not)) of both types of omics
must be given as input, plus the files defining the pathways. You can use the minimal data examples from https://zenodo.org/records/10579891  that contain a minimal example data for running our DIMet Metabologram tool.

The figures in .pdf format are of publication quality, and as they are vectorial images you can open them and customize aesthetics with a professional image software such as Inkscape, Adobe Illustrator, Sketch,  CorelDRAW, etc.


**Input data files**

  2.1 The table with the results of the differential expression analysis (performed with an external tool)


3. **The pathways files**:


  3.1 A file with the pathways and respective gene symbols, which must match with those present in the transcriptomics data. The names of the columns must be the pathways' names, see the minimal data example downloaded from zenodo as explained above. Example:

    ====================== ================== ==================
    **PENTHOSE PHOSPHATE** **FATTY ACIDS**    ...
    ====================== ================== ==================
    RPE                    SCD                ...
    PGM1                   FADS1              ...
    PKF                    BAAT               ...
    DERA                   ACOT2              ...
    ...                    ...                ...
    ====================== ================== ==================



  3.2 A file with the pathways and respective metabolites ID, that must match with those in your metabolomics data. The names of the columns must be the pathways' names, see the minimal data example downloaded from zenodo as explained above. Example:

    ====================== ================== ==================
    **PENTHOSE PHOSPHATE** **FATTY ACIDS**    ...
    ====================== ================== ==================
    Xyl_P                  Acetyl_CoA         ...
    Glc_6P                 Palmitate          ...
    Rib_6P                 Stearate           ...
    ...                    ...                ...
    ====================== ================== ==================

**Measures' files**

The measure's files must be organized as matrices:
- The first column must contain Metabolite IDs that are unique (not repeated) within the file.

The output consists of one figure by metabologram,  and a color key bar legend valid for all metabolograms produced

**Available data for testing**

You can test our tool with the data from our manuscript https://zenodo.org/record/10579862 (the pertinent
files for you are located in the subfolders inside the data folder).
You can also use the minimal data examples from https://zenodo.org/record/10579891

 ]]>
    </help>
    <expand macro="citations" />
</tool>