<tool id="maaslin3" name="MaAsLin3" version="@TOOL_VERSION@+galaxy@VERSION_SUFFIX@" profile="@PROFILE@">
    <description>Microbiome Multivariable Association with Linear Models</description>
    <macros>
        <import>macros.xml</import>
    </macros>
    <expand macro="requirements"/>
    <command detect_errors="exit_code"><![CDATA[
            mkdir 'output' &&

            #if $fixed_effects:
                #set idx = []
                #for $i in $fixed_effects:
                    #silent idx.append(f'${i}')
                #end for
                #set idx_for_awk = ','.join(idx)

                fixed_effects=`awk -v OFS=',' -F"\t" 'NR == 1 { print $idx_for_awk }' "$input_metadata"` &&
                echo 'Assigned fixed effects as:' \$fixed_effects &&
            #end if

            #if $random_effects:
            #set idx = []
            #for $i in $random_effects:
                #silent idx.append(f'${i}')
            #end for
            #set idx_for_awk = ','.join(idx)

            random_effects=`awk -v OFS=',' -F"\t" 'NR == 1 { print $idx_for_awk }' "$input_metadata"` &&
            echo 'Assigned random effects as:' \$random_effects &&
            #end if

            #if $group_effects:
                #set idx = []
                #for $i in $group_effects:
                    #silent idx.append(f'${i}')
                #end for
                #set idx_for_awk = ','.join(idx)

                group_effects=`awk -v OFS=',' -F"\t" 'NR == 1 { print $idx_for_awk }' "$input_metadata"` &&
                echo 'Assigned group effects as:' \$group_effects &&
            #end if

            #if $ordered_effects:
                #set idx = []
                #for $i in $ordered_effects:
                    #silent idx.append(f'${i}')
                #end for
                #set idx_for_awk = ','.join(idx)

                ordered_effects=`awk -v OFS=',' -F"\t" 'NR == 1 { print $idx_for_awk }' "$input_metadata"` &&
                echo 'Assigned fixed effects as:' \$ordered_effects &&
            #end if

            #if $strata_effects:
                strata_effects=`awk -v OFS=',' -F"\t" 'NR == 1 { print $strata_effects }' "$input_metadata"` &&
                echo 'Assigned strata effects as:' \$strata_effects &&
            #end if

            #if $covariate.feature_specific_covariate and $covariate.feature_specific_covariate_name:
                #set idx = []
                #for $i in $covariate.feature_specific_covariate_name:
                    #silent idx.append(f'${i}')
                #end for
                #set idx_for_awk = ','.join(idx)

                covariate.feature_specific_covariate_name=`awk -v OFS=',' -F"\t" 'NR == 1 { print $idx_for_awk }' "$covariate.feature_specific_covariate"` &&
                echo 'Assigned covariate feature specific covariate name as:' \$covariate.feature_specific_covariate_name &&
            #end if

            #if len($ref) != 0:
                #set ref_dict = []
                #for $i, $r in enumerate($ref):
                    #silent $ref_dict.append('%s' %(str($r.reference)))
                #end for
                #set final_ref = ';'.join(ref_dict)
            #end if

            ln -s '$input_data' 'input_data.tsv' &&
            ln -s '$input_metadata' 'input_metadata.tsv' &&

            #if $options.unscaled_abundance:
                ln -s '$options.unscaled_abundance' 'unscaled_abundance.tsv' &&
            #end if

            #if $covariate.feature_specific_covariate:
                ln -s '$covariate.feature_specific_covariate' 'feature_specific_covariate.tsv' &&
            #end if

            maaslin3.R
            'input_data.tsv'
            'input_metadata.tsv'
            'output'
            #if $formula:
                --formula  '~ $formula'
            #end if
            #if $fixed_effects:
                --fixed_effects \$fixed_effects
            #end if
            #if len($ref) != 0:
                --reference '$final_ref'
            #end if
            #if $random_effects:
                --random_effects \$random_effects
            #end if
            #if $group_effects:
                --group_effects \$group_effects
            #end if
            #if $ordered_effects:
                --ordered_effects \$ordered_effects
            #end if
            #if $strata_effects:
                --strata_effects  \$strata_effects
            #end if
            #if $options.min_abundance:
                --min_abundance ${options.min_abundance}
            #end if
            #if $options.min_prevalence:
                --min_prevalence ${options.min_prevalence}
            #end if
            #if $options.zero_threshold:
                --zero-threshold ${options.zero_threshold}
            #end if
            #if $options.min_variance:
                --min_variance ${options.min_variance}
            #end if
            #if $options.max_significance:
                --max_significance $options.max_significance
            #end if
            --normalization '$options.normalization'
            --transform '$options.transform'
            --correction '$options.correction'
            ${options.standardize}
            #if $options.unscaled_abundance:
                --unscaled_abundance 'unscaled_abundance.tsv'
            #end if
            ${options.median_comparison_abundance}
            ${options.median_comparison_prevalence}
            #if $options.median_comparison_abundance_threshold:
                --median_comparison_abundance_threshold ${options.median_comparison_abundance_threshold}
            #end if
            #if $options.median_comparison_prevalence_threshold:
                --median_comparison_prevalence_threshold ${options.median_comparison_prevalence_threshold}
            #end if
            ${options.subtract_median}
            ${options.warn_prevalence}
            ${options.small_random_effects}
            ${options.augment}
            #if $options.evaluate_only != 'NULL':
                --evaluate_only '$options.evaluate_only'
            #end if
            #if $covariate.feature_specific_covariate:
                --feature_specific_covariate 'feature_specific_covariate.tsv'
            #end if
            #if $covariate.feature_specific_covariate and $covariate.feature_specific_covariate_name:
                --feature_specific_covariate_name \$covariate.feature_specific_covariate_name
            #end if
            #if $covariate.feature_specific_covariate and $covariate.feature_specific_covariate_record:
                --feature_specific_covariate_record '$covariate.feature_specific_covariate_record'
            #end if
            ${output.plot_summary_plot}
            #if $output.summary_plot_first_n:
                --summary_plot_first_n ${output.summary_plot_first_n}
            #end if
            #if $output.coef_plot_vars:
                --coef_plot_vars '$output.coef_plot_vars'
            #end if
            #if $output.heatmap_vars:
                --heatmap_vars '$output.heatmap_vars'
            #end if
            ${output.plot_associations}
            #if $output.max_pngs:
                --max_pngs ${output.max_pngs}
            #end if
            ${output.save_models}
            ${output.save_plots_rds}
            ${output.summary_plot_balanced}
            --cores "\${GALAXY_SLOTS:-1}"
    ]]></command>
    <inputs>
        <param name="input_data" type="data" format="tabular" label="Data file"/>
        <param name="input_metadata" type="data" format="tabular" label="Metadata file"/>
        <param argument="--formula" type="text" value="" label="Input formula (use header of columns for this)" help="Enter the formula which should the model use. NOTE: separate the name with and plus and have space between it like: 'header1 + header2 + ...' as an example. When no input is used all columns will be used"/>
        <param argument="--fixed_effects" type="data_column" data_ref="input_metadata" use_header_names="true" multiple="true" optional="true" label="Set fix effects" help="Select the fix effects for the model. DEFAULT: ALL"/>
        <repeat name="ref" title="Reference pairs">
            <param argument="--reference" type="text" label="Reference" help="The factor to use as a reference for a variable with more than two levels provided as a string of 'variable,reference'. DEFAULT: NA"/>
        </repeat>
        <param argument="--random_effects" type="data_column" data_ref="input_metadata" use_header_names="true" multiple="true" optional="true" label="Set random effects" help="Select the random effects for the model. DEFAULT; NONE"/>
        <param argument="--group_effects" type="data_column" data_ref="input_metadata" use_header_names="true" multiple="true" optional="true" label="Set group effects" help="Select group effects for the model. DEFAULT: NONE"/>
        <param argument="--ordered_effects" type="data_column" data_ref="input_metadata" use_header_names="true" multiple="true" optional="true" label="Set ordered effects" help="Select the ordered effects for the model. DEFAULT: NONE"/>
        <param argument="--strata_effects" type="data_column" data_ref="input_metadata" use_header_names="true" optional="true" label="Set strata effects" help="Select only one strata effect for the model. DEFAULT: NONE"/>
        <section name="options" title="Additional options" expanded="true">
            <param argument="--min_abundance" type="integer" value="0" label="Minimum abundance" help="Minimum abundance for each feature befor normalization and transformation"/>
            <param argument="--min_prevalence" type="integer" value="0" label="Mimimum prevalence" help="The minimum proportion of samples for which a feature is detected at minimum abundance"/>
            <param argument="--zero-threshold" type="integer" value="0" label="Zero threshold" help="The minimum abundance to be considered non-zero"/>
            <param argument="--min_variance" type="integer" value="0" label="Minimum variance" help="Keep features with variances greater than value"/>
            <param argument="--max_significance" type="float" value="0.1" label="Maximum significance" help="The q-value threshold for significance"/>
            <param argument="--normalization" type="select" label="Normalization method">
                <option value="TSS" selected="true">TSS</option>
                <option value="CLR">CLR</option>
                <option value="NONE">NONE</option>
            </param>
            <param argument="--transform" type="select" label="Transform to apply">
                <option value="LOG" selected="true">LOG</option>
                <option value="PLOG">PLOG</option>
                <option value="NONE">NONE</option>
            </param>
            <param argument="--correction" type="select" label="correction method for computing the q-value">
                <option value="BH" selected="true">BH</option>
                <option value="BY">BY</option>
                <option value="bonferroni">bonferroni</option>
                <option value="holm">holm</option>
                <option value="hochberg">hochberg</option>
                <option value="hommel">hommel</option>
                <option value="fdr">fdr</option>
                <option value="none">none</option>
            </param>
            <param argument="--standardize" type="boolean" truevalue="--standardize TRUE" falsevalue="" checked="true" label="Standardize" help="Scale continuous metadata witth the z-score(converting the interpretation of its scale for a standardized scale)"/>
            <param argument="--unscaled_abundance" type="data" format="tabular" optional="true" label="unscaled abundance reference table" help="The table to use as an unscaled abundance reference. Example in the help section. NOTE: the single column name must be the same as one of the features or 'total'!"/>
            <param argument="--median_comparison_abundance" type="boolean" truevalue="--median_comparison_abundance TRUE" falsevalue="" checked="true" label="Median comparison abundance" help="Test abundance coefficients against the median association"/>
            <param argument="--median_comparison_prevalence" type="boolean" truevalue="--median_comparison_prevalence TRUE" falsevalue="--median_comparison_prevalence FALSE" checked="false" label="Median comparison prevalence" help="Test prevalence coefficients against the median association"/>
            <param argument="--median_comparison_abundance_threshold" type="integer" value="0" label="Median comparison abundance threshold" help="Radius within which the median adjustment gives a p-value of 1"/>
            <param argument="--median_comparison_prevalence_threshold" type="integer" value="0" label="Median comparison prevalence threshold" help="Radius within which the median adjustment gives a p-value of 1"/>
            <param argument="--subtract_median" type="boolean" truevalue="--subtract_median TRUE" falsevalue="--subtract_median FALSE" checked="false" label="Subtract median" help="Subtract the median from coefficients when doing median comparisons"/>
            <param argument="--warn_prevalence" type="boolean" truevalue="--warn_prevalence TRUE" falsevalue="" checked="true" label="Warn prevalence" help="Check and warn if prevalence associations are likely due to compositionality"/>
            <param argument="--small_random_effects" type="boolean" truevalue="--small_random_effects TRUE" falsevalue="--small_random_effects FALSE" checked="false" label="Small random effects" help=" Replace prevalence random effects with fixed effects because groups are small"/>
            <param argument="--augment" type="boolean" truevalue="--augment TRUE" falsevalue="--augment FALSE" checked="true" label="Augment" help="Add weighted extra 0s and 1s to avoid linear separability"/>
            <param argument="--evaluate_only" type="select" label="Evaluate only" help="Whether to evaluate just the abundnace or prevalence models">
                <option value="NULL" selected="true">NULL</option>
                <option value="none">None</option>
                <option value="abundance">abundance</option>
                <option value="prevalence">prevalence</option>
            </param>
        </section>
        <section name="covariate" title="Feature specific covariate">
            <param argument="--feature_specific_covariate" type="data" format="tabular" optional="true" label="feature-specific covariates table" help="The table to use for feature-specific covariates. Row and column names should match the data input. EXAMPLE IN THE HELP SECTION DOWN BELOW!"/>
            <param argument="--feature_specific_covariate_name" type="data_column" data_ref="feature_specific_covariate" use_header_names="true" multiple="true" optional="true" label="Set feature-specific covariate" help="Select the feature-specific covariate"/>
            <param argument="--feature_specific_covariate_record" type="text" optional="true" label="Set feature_specific_covariate data" help="Select which data (rows) should included for the feature-specific covariate"/>
        </section>
        <section name="output" title="Output options">
            <param argument="--plot_summary_plot" type="boolean" truevalue="--plot_summary_plot TRUE" falsevalue="--plot_summary_plot FALSE" checked="true" label="Plot summary plot"/>
            <param argument="--summary_plot_first_n" type="integer" value="25" label="Summary plot first n" help="In summary plot, plot top N features with significant associations"/>
            <param argument="--coef_plot_vars" type="text" label="Coefficient plot variables" help="The variables to use in the coefficient plot section of the summary plot provided as a comma-separated string. Continuous variables should match the metadata column name, and categorical variables should be of the form: [metadata] [level]. DEFAULT: NA"/>
            <param argument="--heatmap_vars" type="text" label="Heatmap variables" help="The variables to use in the heatmap section of the summary plot provided as a comma-separated string. Continuous variables should match the metadata column name, and categorical variables should be of the form: [metadata] [level]. DEFAULT: NA"/>
            <param argument="--plot_associations" type="boolean" truevalue="--plot_associations TRUE" falsevalue="--plot_associations FALSE" checked="true" label="Plot associations"/>
            <param argument="--max_pngs" type="integer" value="30" label=" maximum number of association plots"/>
            <param argument="--save_models" type="boolean" truevalue="--save_models TRUE" falsevalue="--save_models FALSE" checked="false" label="Save model" help="Return the full model outputs and save to an RData file"/>
            <param argument="--save_plots_rds" type="boolean" truevalue="--save_plots_rds TRUE" falsevalue="--save_plots_rds FALSE" checked="false" label="Save plots as rds" help="Save the plots to RDS files"/>
            <param argument="--summary_plot_balanced" type="boolean" truevalue="--summary_plot_balanced TRUE" falsevalue="--summary_plot_balanced FALSE" checked="false" label="Summary plot balanced" help="If coef_plot_vars is selected this will select balanced top features"/>
        </section>
    </inputs>
    <outputs>
        <data name="data_norm" format="tabular" from_work_dir="output/features/data_norm.tsv" label="${tool.name} on ${on_string}: DATA NORMALIZED"/>
        <data name="data_trans" format="tabular" from_work_dir="output/features/data_transformed.tsv" label="${tool.name} on ${on_string}: DATA TRANSFORMED"/>
        <data name="data_filter" format="tabular" from_work_dir="output/features/filtered_data.tsv" label="${tool.name} on ${on_string}: FILTERED DATA"/>
        <collection name="rdata" type="list" label="${tool.name} on ${on_string}: R DATA">
            <discover_datasets pattern="(?P&lt;designation&gt;.+)" directory="output/fits" format="rdata"/>
        </collection>
        <collection name="plots" type="list:list" label="${tool.name} on ${on_string}: PLOTS">
            <discover_datasets pattern="(?P&lt;designation&gt;.+)\.png" directory="output/figures/association_plots" recurse="true" format="png"/>
            <filter>output['plot_associations'] is True</filter>
        </collection>
        <data name="summary_png" format="png" from_work_dir="output/figures/summary_plot.png" label="${tool.name} on ${on_string}: SUMMARY PLOT (PNG)">
            <filter>output['summary_plot_balanced'] is False</filter>
        </data>
        <data name="summary_pdf" format="pdf" from_work_dir="output/figures/summary_plot.pdf" label="${tool.name} on ${on_string}: SUMMARY PLOT (PDF)">
            <filter>output['summary_plot_balanced'] is False</filter>
        </data>
        <collection name="plots_r" type="list" label="${tool.name} on ${on_string}: PLOTS (R DATA)">
            <discover_datasets pattern="(?P&lt;designation&gt;.+)\.RDS" directory="output/figures" recurse="true" format="rdata"/>
            <filter>output['plot_associations'] is True and output['save_plots_rds'] is True</filter>
        </collection>
        <data name="result_all" format="tabular" from_work_dir="output/all_results.tsv" label="${tool.name} on ${on_string}: ALL RESULT"/>
        <data name="result_sig" format="tabular" from_work_dir="output/significant_results.tsv" label="${tool.name} on ${on_string}: SIGNIFICANT RESULT"/>
    </outputs>
    <tests>
        <test expect_num_outputs="9">
            <param name="input_data" value="HMP2_taxonomy.tsv" ftype="tabular"/>
            <param name="input_metadata" value="HMP2_metadata.tsv" ftype="tabular"/>
            <param name="formula" value="reads + diagnosis + dysbiosis_state + antibiotics + age"/>
            <repeat name="ref">
                <param name="reference" value="diagnosis,CD"/>
            </repeat>
            <repeat name="ref">
                <param name="reference" value="dysbiosis_state,dysbiosis_UC"/>
            </repeat>
            <output name="data_norm" ftype="tabular">
                <assert_contents>
                    <has_n_lines n="818" delta="10"/>
                </assert_contents>
            </output>
            <output name="data_trans" file="data_transformed.tsv" ftype="tabular"/>
            <output name="data_filter" ftype="tabular">
                <assert_contents>
                    <has_n_lines n="818" delta="10"/>
                </assert_contents>
            </output>
            <output_collection name="rdata" type="list">
                <element name="fitted_linear.rds" ftype="rdata">
                    <assert_contents>
                        <has_size value="327196" delta="5000"/>
                    </assert_contents>
                </element>
            </output_collection>
            <output name="summary_png" ftype="png">
                <assert_contents>
                    <has_image_height height="3690" delta="20"/>
                    <has_image_width width="4375" delta="20"/>
                </assert_contents>
            </output>
            <output name="summary_pdf" ftype="pdf">
                <assert_contents>
                    <has_size size="18000" delta="2000"/>
                </assert_contents>
            </output>
            <output name="result_all" ftype="tabular">
                <assert_contents>
                    <has_n_lines n="2036" delta="30"/>
                </assert_contents>
            </output>
            <output name="result_sig" ftype="tabular">
                <assert_contents>
                    <has_n_lines n="834" delta="15"/>
                </assert_contents>
            </output>
        </test>
        <test expect_num_outputs="7">
            <param name="input_data" value="HMP2_taxonomy.tsv" ftype="tabular"/>
            <param name="input_metadata" value="HMP2_metadata.tsv" ftype="tabular"/>
            <param name="formula" value="reads + diagnosis + dysbiosis_state + antibiotics + age"/>
            <repeat name="ref">
                <param name="reference" value="diagnosis,CD"/>
            </repeat>
            <repeat name="ref">
                <param name="reference" value="dysbiosis_state,dysbiosis_UC"/>
            </repeat>
            <section name="output">
                <param name="summary_plot_balanced" value="True"/>
            </section>
            <output name="data_norm" ftype="tabular">
                <assert_contents>
                    <has_n_lines n="818" delta="10"/>
                </assert_contents>
            </output>
            <output name="data_trans" file="data_transformed.tsv" ftype="tabular"/>
            <output name="data_filter" ftype="tabular">
                <assert_contents>
                    <has_n_lines n="818" delta="10"/>
                </assert_contents>
            </output>
            <output_collection name="rdata" type="list">
                <element name="fitted_linear.rds" ftype="rdata">
                    <assert_contents>
                        <has_size value="327196" delta="5000"/>
                    </assert_contents>
                </element>
            </output_collection>
            <output name="result_all" ftype="tabular">
                <assert_contents>
                    <has_n_lines n="2036" delta="30"/>
                </assert_contents>
            </output>
            <output name="result_sig" ftype="tabular">
                <assert_contents>
                    <has_n_lines n="834" delta="15"/>
                </assert_contents>
            </output>
        </test>
        <test expect_num_outputs="10">
            <param name="input_data" value="HMP2_taxonomy.tsv" ftype="tabular"/>
            <param name="input_metadata" value="HMP2_metadata.tsv" ftype="tabular"/>
            <param name="formula" value="reads + diagnosis + dysbiosis_state + antibiotics + age"/>
            <repeat name="ref">
                <param name="reference" value="diagnosis,CD"/>
            </repeat>
            <repeat name="ref">
                <param name="reference" value="dysbiosis_state,dysbiosis_UC"/>
            </repeat>
            <section name="output">
                <param name="save_plots_rds" value="True"/>
            </section>
            <output name="data_norm" ftype="tabular">
                <assert_contents>
                    <has_n_lines n="818" delta="10"/>
                </assert_contents>
            </output>
            <output name="data_trans" file="data_transformed.tsv" ftype="tabular"/>
            <output name="data_filter" ftype="tabular">
                <assert_contents>
                    <has_n_lines n="818" delta="10"/>
                </assert_contents>
            </output>
            <output_collection name="rdata" type="list">
                <element name="fitted_linear.rds" ftype="rdata">
                    <assert_contents>
                        <has_size value="327196" delta="5000"/>
                    </assert_contents>
                </element>
            </output_collection>
            <output name="summary_png" ftype="png">
                <assert_contents>
                    <has_image_height height="3690" delta="20"/>
                    <has_image_width width="4375" delta="20"/>
                </assert_contents>
            </output>
            <output name="summary_pdf" ftype="pdf">
                <assert_contents>
                    <has_size size="18000" delta="2000"/>
                </assert_contents>
            </output>
            <output name="result_all" ftype="tabular">
                <assert_contents>
                    <has_n_lines n="2036" delta="30"/>
                </assert_contents>
            </output>
            <output name="result_sig" ftype="tabular">
                <assert_contents>
                    <has_n_lines n="834" delta="15"/>
                </assert_contents>
            </output>
            <output_collection name="plots_r" type="list" count="4">
                <element name="summary_plot_gg" ftype="rdata">
                    <assert_contents>
                        <has_size size="720000" delta="100000"/>
                    </assert_contents>
                </element>
            </output_collection>
        </test>
        <test expect_num_outputs="8">
            <param name="input_data" value="HMP2_taxonomy.tsv" ftype="tabular"/>
            <param name="input_metadata" value="HMP2_metadata.tsv" ftype="tabular"/>
            <param name="formula" value="reads + diagnosis + dysbiosis_state + antibiotics + age"/>
            <repeat name="ref">
                <param name="reference" value="diagnosis,CD"/>
            </repeat>
            <repeat name="ref">
                <param name="reference" value="dysbiosis_state,dysbiosis_UC"/>
            </repeat>
            <section name="output">
                <param name="save_models" value="True"/>
                <param name="plot_associations" value="False"/>
            </section>
            <output name="data_norm" ftype="tabular">
                <assert_contents>
                    <has_n_lines n="818" delta="10"/>
                </assert_contents>
            </output>
            <output name="data_trans" file="data_transformed.tsv" ftype="tabular"/>
            <output name="data_filter" ftype="tabular">
                <assert_contents>
                    <has_n_lines n="818" delta="10"/>
                </assert_contents>
            </output>
            <output_collection name="rdata" type="list">
                <element name="fitted_linear.rds" ftype="rdata">
                    <assert_contents>
                        <has_size value="327196" delta="5000"/>
                    </assert_contents>
                </element>
            </output_collection>
            <output name="summary_png" ftype="png">
                <assert_contents>
                    <has_image_height height="3690" delta="20"/>
                    <has_image_width width="4375" delta="20"/>
                </assert_contents>
            </output>
            <output name="summary_pdf" ftype="pdf">
                <assert_contents>
                    <has_size size="18000" delta="2000"/>
                </assert_contents>
            </output>
            <output name="result_all" ftype="tabular">
                <assert_contents>
                    <has_n_lines n="2036" delta="30"/>
                </assert_contents>
            </output>
            <output name="result_sig" ftype="tabular">
                <assert_contents>
                    <has_n_lines n="834" delta="15"/>
                </assert_contents>
            </output>
        </test>
    </tests>
    <help>
        <![CDATA[

            **IMPORTANT**

            random_effects, group_effects, ordered_effects, and strata_effects must be NULL when formula is not NULL!!

            If warn_prevalence (Warn prevalence) is set to True please selecet evaluate_only (Evaluate only) to NULL.
            If you want to use evaluate_only (Evaluate only) please do not set warn_prevalence (Warn prevalence) to True!
            Both argument can not be set at the same time!


            **Input**

            - A data table
            - A metadata table
            - unscaled abundance reference table (optional)

                Example::
                    SampleID	total
                    S1	2.3e8
                    S2	1.8e8
                    S3	3.1e8
                    S4	2.6e8
                
                For a none specific case and here is a example for a specific case with *SpikeIn1*::

                    SampleID	SpikeIn1
                    S1	1000
                    S2	2000
                    S3	1500
                    S4	1800
            
            - A feature-specific covariates table (optional)

                Example::
                    Feature	GC_content	Batch_flag
                    GeneA	0.42	1
                    GeneB	0.65	0
                    PathwayX	NA	1
                    PathwayY	0.58	1

            **Output**

            - multiple plots (optional)
            - Plots in R format (optional)
            - certain R format data files (some optional)
            - Mulitple data tables

        ]]>
    </help>
    <citations>
        <citation type="doi">10.1101/2024.12.13.628459</citation>
    </citations>
</tool>