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maaslin3.xml
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<tool id="maaslin3" name="MaAsLin3" version="0.1.0" python_template_version="3.5">
<requirements>
</requirements>
<command detect_errors="exit_code"><![CDATA[
rm -rf /tmp/tmp.maaslin3_output_$__user_id__ &&
rm -f /tmp/tmp.maaslin3_{$__user_id__}_taxonomy.tsv &&
rm -f /tmp/tmp.maaslin3_{$__user_id__}_metadata.tsv &&
mkdir /tmp/tmp.maaslin3_output_$__user_id__ &&
rm -f /tmp/tmp.maaslin3_{$__user_id__}_Results.zip &&
cp $taxonomy /tmp/tmp.maaslin3_{$__user_id__}_taxonomy.tsv &&
cp $metadata /tmp/tmp.maaslin3_{$__user_id__}_metadata.tsv &&
Rscript '/export/galaxy-central/tools/maaslin3/R/maaslin3.R'
/tmp/tmp.maaslin3_{$__user_id__}_taxonomy.tsv
/tmp/tmp.maaslin3_{$__user_id__}_metadata.tsv
/tmp/tmp.maaslin3_output_$__user_id__
#if $str($gchoice_formula.glocal_choice_formula) == "1":
--formula=$gchoice_formula.formula1
#end if
#if $str($gchoice_additional_analysis_types.global_additional_analysis_types) == "1":
#if $str($gchoice_additional_analysis_types.fixed_effects) != "None":
--fixed_effects $gchoice_additional_analysis_types.fixed_effects
#end if
#if $str($gchoice_additional_analysis_types.random_effects) != "None":
--random_effects $gchoice_additional_analysis_types.random_effects
#end if
#if $str($gchoice_additional_analysis_types.group_effects) != "None":
--group_effects $gchoice_additional_analysis_types.group_effects
#end if
#end if
--reference=$reference1
#if $str($gchoice_absolute_abundance.global_absolute_abundance) == "1":
#if $str($gchoice_absolute_abundance.unscaled_abundance) != "None":
--unscaled_abundance $gchoice_absolute_abundance.unscaled_abundance
#end if
#end if
#if $str($gchoice_additional_abundance_options.global_additional_abundance) == "1":
#if float($gchoice_additional_abundance_options.min_abundance) > 0:
--min_abundance $gchoice_additional_abundance_options.min_abundance
#end if
#if float($gchoice_additional_abundance_options.min_prevalence) > 0:
--min_prevalence $gchoice_additional_abundance_options.min_prevalence
#end if
#if float($gchoice_additional_abundance_options.zero_threshold) > 0:
--zero-threshold $gchoice_additional_abundance_options.zero_threshold
#end if
#if float($gchoice_additional_abundance_options.min_variance) > 0:
--min_variance $gchoice_additional_abundance_options.min_variance
#end if
#if float($gchoice_additional_abundance_options.max_significance) != 0.1:
--max_significance $gchoice_additional_abundance_options.max_significance
#end if
#if str($gchoice_additional_abundance_options.transform) != "LOG":
--transform $gchoice_additional_abundance_options.transform
#end if
#if str($gchoice_additional_abundance_options.normalization) != "TSS":
--normalization $gchoice_additional_abundance_options.normalization
#end if
#if str($gchoice_additional_abundance_options.correction) != "BH":
--correction $gchoice_additional_abundance_options.correction
#end if
#if str($gchoice_additional_abundance_options.standardize) != "TRUE":
--standardize $gchoice_additional_abundance_options.standardize
#end if
#if str($gchoice_additional_abundance_options.median_comparison_abundance) != "TRUE":
--median_comparison_abundance $gchoice_additional_abundance_options.median_comparison_abundance
#end if
#if str($gchoice_additional_abundance_options.median_comparison_prevalence) != "FALSE":
--median_comparison_prevalence $gchoice_additional_abundance_options.median_comparison_prevalence
#end if
#if float($gchoice_additional_abundance_options.median_comparison_abundance_threshold) > 0:
--median_comparison_abundance_threshold $gchoice_additional_abundance_options.median_comparison_abundance_threshold
#end if
#if float($gchoice_additional_abundance_options.median_comparison_prevalence_threshold) > 0:
--median_comparison_prevalence_threshold $gchoice_additional_abundance_options.median_comparison_prevalence_threshold
#end if
#if str($gchoice_additional_abundance_options.subtract_median) != "FALSE":
--subtract_median $gchoice_additional_abundance_options.subtract_median
#end if
#if str($gchoice_additional_abundance_options.warn_prevalence) != "TRUE":
--warn_prevalence $gchoice_additional_abundance_options.warn_prevalence
#end if
#if str($gchoice_additional_abundance_options.augment) != "TRUE":
--augment $gchoice_additional_abundance_options.augment
#end if
#if str($gchoice_additional_abundance_options.evaluate_only) != "NULL":
--evaluate_only $gchoice_additional_abundance_options.evaluate_only
#end if
#if int($gchoice_additional_abundance_options.summary_plot_first_n) != 25:
--summary_plot_first_n $gchoice_additional_abundance_options.summary_plot_first_n
#end if
#if str($gchoice_additional_abundance_options.coef_plot_vars) != "NA":
--coef_plot_vars $gchoice_additional_abundance_options.coef_plot_vars
#end if
#if str($gchoice_additional_abundance_options.heatmap_vars) != "NA":
--heatmap_vars $gchoice_additional_abundance_options.heatmap_vars
#end if
#if str($gchoice_additional_abundance_options.summary_plot_balanced) != "FALSE":
--summary_plot_balanced $gchoice_additional_abundance_options.summary_plot_balanced
#end if
#end if
&&
cp /tmp/tmp.maaslin3_output_$__user_id__/maaslin3.log $output0 &&
cp /tmp/tmp.maaslin3_output_$__user_id__/all_results.tsv $output1 &&
cp /tmp/tmp.maaslin3_output_$__user_id__/significant_results.tsv $output2 &&
cp /tmp/tmp.maaslin3_output_$__user_id__/figures/summary_plot.pdf $output3 &&
zip -r /tmp/tmp.maaslin3_{$__user_id__}_Results.zip /tmp/tmp.maaslin3_output_$__user_id__ &&
cp /tmp/tmp.maaslin3_{$__user_id__}_Results.zip $output4 &&
rm -rf /tmp/tmp.maaslin3_output_$__user_id__ &&
rm -f /tmp/tmp.maaslin3_{$__user_id__}_taxonomy.tsv &&
rm -f /tmp/tmp.maaslin3_{$__user_id__}_metadata.tsv &&
rm -f /tmp/tmp.maaslin3_{$__user_id__}_Results.zip
]]></command>
<inputs>
<param type="data" name="taxonomy" label="Input Taxonomy: Use DataType 'maaslin3_taxonomy_input' when you upload this file" format="maaslin3_taxonomy_input" />
<param type="data" name="metadata" label="Input Metadata : Use DataType 'maaslin3_metadata_input' when you upload" format="maaslin3_metadata_input" />
<conditional name="gchoice_formula">
<param name="glocal_choice_formula" type="select" label="Use model from formula. For Sample enter the following formula: '~ diagnosis + dysbiosis_score + antibiotics + age + reads'" multiple="False" help="Specify a model by typing a formula">
<option value="1" selected='True'>Yes</option>
<option value="0">No</option>
</param>
<when value="0">
</when>
<when value="1">
<param name="formula1" type="text" label="formula: For sample use: '~ diagnosis + dysbiosis_state + antibiotics + age + reads'" value="'~ diagnosis + dysbiosis_state + antibiotics + age + reads'">
<sanitizer >
<valid initial="string.printable">
</valid>
</sanitizer>
</param>
</when>
</conditional>
<conditional name="gchoice_additional_analysis_types">
<param name="global_additional_analysis_types" type="select" label="Advanced Model Formula Parameters" multiple="False" help="Select advanced choices ">
<option value="0" selected='True'>No</option>
<option value="1">Yes</option>
</param>
<when value="0">
</when>
<when value="1">
<param name="fixed_effects" label="fixed_effects: The fixed effects for the model, comma-delimited for multiple effects [ Default: none ]" value="None" type="text" format="text"/>
<param name="random_effects" label="random_effects: The random effects for the model, comma-delimited for multiple effects [ Default: none ]" value="None" type="text" format="text"/>
<param name="group_effects" label="group_effects: The group effects for the model, comma-delimited for multiple effects[ Default: none ] " value="None" type="text" format="text"/>
</when>
</conditional>
<param name="reference1" type="text" label="reference: For sample use: 'diagnosis,nonIBD;dysbiosis_state,none;antibiotics,No'" value="'diagnosis,nonIBD;dysbiosis_state,none;antibiotics,No'">
<sanitizer >
<valid initial="string.printable">
</valid>
</sanitizer>
</param>
<conditional name="gchoice_absolute_abundance">
<param name="global_absolute_abundance" type="select" label="Use absolute abundance data" multiple="False" help="Include spike-in or total abundance data">
<option value="0" selected='True'>No</option>
<option value="1">Yes</option>
</param>
<when value="0">
</when>
<when value="1">
<param type="data" name="unscaled_abundance" label="Input absolute abundance : Use DataType 'maaslin3_unscaled_abundance' when you upload" value="None" format="maaslin3_unscaled_abundance" />
</when>
</conditional>
<conditional name="gchoice_additional_abundance_options">
<param name="global_additional_abundance" type="select" label="Advanced Analysis Options" multiple="False" help="Select Advanced Analysis Options">
<option value="0" selected='True'>No</option>
<option value="1">Yes</option>
</param>
<when value="0">
</when>
<when value="1">
<param name="min_abundance" type="float" value="0" label=" min_abundance: The minimum abundance for each feature "/>
<param name="min_prevalence" type="float" value="0" label=" min_prevalence: The minimum proportion of samples for which a feature is detected at minimum abundance "/>
<param name="zero_threshold" type="float" value="0" label=" zero_threshold: The minimum abundance to be considered non-zero "/>
<param name="min_variance" type="float" value="0" label=" min_variance: Keep features with variances greater than value "/>
<param name="max_significance" type="float" value="0.1" label=" max_significance: The q-value threshold for significance "/>
<param name="normalization" type="select" value="TSS" format="text" >
<label>The normalization method to apply </label>
<option value="TSS">TSS</option>
<option value="CLR">CLR</option>
<option value="CSS">CSS</option>
<option value="NONE">NONE</option>
<option value="TMM">TMM</option>
</param>
<param name="transform" type="select" value="LOG" format="text" >
<label>The transform method to apply </label>
<option value="LOG">LOG</option>
<option value="PLOG">LOGIT</option>
<option value="NONE">NONE</option>
</param>
<param name="correction" type="select" value="BH" format="text" >
<label>correction: The correction to obtain FDR-corrected q-values from raw p-values. </label>
<option value="BH">BH</option>
<option value="holm">holm</option>
<option value="hochberg">hochberg</option>
<option value="hommel">hommel</option>
<option value="bonferroni">bonferroni</option>
<option value="BY">BY</option>
</param>
<param name="standardize" type="select" value="TRUE" format="text" >
<label>standardize: Apply z-score so continuous metadata are on the same scale </label>
<option value="TRUE">TRUE</option>
<option value="FALSE">FALSE</option>
</param>
<param name="median_comparison_abundance" type="select" value="TRUE" format="text" >
<label>median_comparison_abundance: Test abundance coefficients against the median association </label>
<option value="TRUE">TRUE</option>
<option value="FALSE">FALSE</option>
</param>
<param name="median_comparison_prevalence" type="select" value="FALSE" format="text" >
<label>median_comparison_prevalence: Test prevalence coefficients against the median association </label>
<option value="FALSE">FALSE</option>
<option value="TRUE">TRUE</option>
</param>
<param name="median_comparison_abundance_threshold" type="float" value="0" label=" median_comparison_abundance_threshold: median_comparison_abundance_threshold "/>
<param name="median_comparison_prevalence_threshold" type="float" value="0" label=" Radius within which the median adjustment gives a p-value of 1 "/>
<param name="subtract_median" type="select" value="FALSE" format="text" >
<label>subtract_median: ubtract_median: Subtract the median from coefficients when doing median comparisons </label>
<option value="FALSE">FALSE</option>
<option value="TRUE">TRUE</option>
</param>
<param name="warn_prevalence" type="select" value="TRUE" format="text" >
<label>warn_prevalence: warn_prevalence: Check and warn if prevalence associations are likely due to compositionality </label>
<option value="TRUE">TRUE</option>
<option value="FALSE">FALSE</option>
</param>
<param name="augment" type="select" value="TRUE" format="text" >
<label>augment: augment: Add weighted extra 0s and 1s to avoid linear separability </label>
<option value="TRUE">TRUE</option>
<option value="FALSE">FALSE</option>
</param>
<param name="evaluate_only" type="text" value="NULL" label="evaluate_only: Whether to evaluate just the abundance or prevalence models"/>
<param name="summary_plot_first_n" type="integer" size="4" value="25" label="summary_plot_first_n: In summary plot, plot top N features with significant associations "/>
<param name="coef_plot_vars" type="text" value="NA" label="coef_plot_vars: 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]"/>
<param name="heatmap_vars" type="text" value="NA" label="heatmap_vars: 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]"/>
<param name="summary_plot_balanced" type="select" value="FALSE" format="text" >
<label>summary_plot_balanced: If coef_plot_vars is selected this will select balanced top features </label>
<option value="FALSE">FALSE</option>
<option value="TRUE">TRUE</option>
</param>
</when>
</conditional>
</inputs>
<outputs>
<data name="output0" format="txt" label="maaslin3 log" />
<data name="output1" format="tsv" label="all results" />
<data name="output2" format="tsv" label="significant results" />
<data name="output3" format="pdf" label="Summary Plot" />
<data name="output4" format="zip" label="MaAsLin Output zip results - please download" />
</outputs>
<help><![CDATA[
MaAsLin 3 is the next generation of MaAsLin (Microbiome Multivariable Associations with Linear Models).
* This comprehensive R package efficiently determines multivariable associations between clinical metadata and microbial meta-omics features.
* Relative to MaAsLin 2, MaAsLin 3 introduces the ability to quantify and test for both abundance and prevalence associations while accounting for compositionality.
* By incorporating generalized linear models, MaAsLin 3 accomodates most modern epidemiological study designs including cross-sectional and longitudinal studies.
.. image:: http://github.com/biobakery/galaxy_maaslin3/blob/main/figures/summary_plot1.png?raw=true
:height: 500
:width: 800
**Inputs**
==========
MaAslin requires two inputs which are described here: Inputs_Description_
*Taxonomy file:*
================
Possible features include taxonomy or genes.
These can be relative abundances or counts.
Sample for this file can be found in MaAslin3_Sample_Taxonomy_ .
Please upload with datatype of *"maaslin3_taxonomy_input"*
*Metadata file:*
================
Formatted with variables as columns and samples as rows. Possible metadata include gender or age
Sample for this file can be found in MaAslin3_Sample_Metadata_ .
Please upload with datatype of *"maaslin3_metadata_input"*
*Absolute abundance file:*
==========================
A single column with the column name the same as one of the features when using a spike-in or 'total' when using total abundance scaling.
Row names should be the same as for the taxonomy and metadata files.
Please upload with datatype of *"maaslin3_unscaled_abundance"*
Please see detailed documentation on how to use this file in doc_unscaled_abundance_
Sample for this file can be found in MaAslin3_Sample_Specific_Unscaled_Abundance_ .
**Outputs**
===========
* MaAsLin files explained in detail in MaAsLin3_Outputs_Description_
.. _MaAsLin3_Outputs_Description: http://github.com/biobakery/maaslin3?tab=readme-ov-file#output-files
.. _the_manuscript: http://doi.org/10.1101/2024.12.13.628459
.. _MaAslin3_Sample_Taxonomy: http://github.com/biobakery/galaxy_maaslin3/blob/main/sample_data/HMP2_taxonomy.tsv
.. _MaAslin3_Sample_Metadata: http://github.com/biobakery/galaxy_maaslin3/blob/main/sample_data/HMP2_metadata.tsv
.. _MaAslin3_Sample_Specific_Unscaled_Abundance: http://github.com/biobakery/galaxy_maaslin3/blob/main/sample_data/maaslin3_unscaled_abundance.tsv
.. _Inputs_Description: http://github.com/biobakery/maaslin3?tab=readme-ov-file#input-data
.. _User_Manual: http://github.com/biobakery/Maaslin3
.. _Tutorial: http://github.com/biobakery/biobakery/wiki/maaslin3
.. _Forum: http://forum.biobakery.org/c/Downstream-analysis-and-statistics/MaAsLin3
.. _doc_unscaled_abundance: http://github.com/biobakery/biobakery/wiki/MaAsLin3/#spike-in
**User Manual, Tutorial and Forum**
===================================
You can find the following resources:
- User_Manual_
- Tutorial_
- Forum_
If you use the MaAsLin 3 software, please cite the_manuscript_ :
William A. Nickols, Thomas Kuntz, Jiaxian Shen, Sagun Maharjan, Himel Mallick, Eric A. Franzosa, Kelsey N. Thompson, Jacob T. Nearing, Curtis Huttenhower.
MaAsLin 3: Refining and extending generalized multivariable linear models for meta-omic association discovery. bioRxiv 2024.12.13.628459; doi: https://doi.org/10.1101/2024.12.13.628459
]]></help>
</tool>