comparison dimet_bivariate_analysis.xml @ 0:eeb106c7bd04 draft

planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/DIMet commit 3dba8748fbc8cc8e89ffc08e5febe0a0527a96a5

0:eeb106c7bd04

<tool id="dimet_@EXECUTABLE@" name="dimet @TOOL_LABEL@" version="@TOOL_VERSION@+galaxy@VERSION_SUFFIX@" profile="20.05">
    <description>
        Computation of the correlation of MDV profiles, or the metabolite time course profiles (by DIMet)
    </description>
    <macros>
        <token name="@TOOL_LABEL@">bivariate analysis</token>
        <token name="@EXECUTABLE@">bivariate_analysis</token>
        <import>macros.xml</import>
    </macros>
    <expand macro="requirements"/>
    <command detect_errors="exit_code"><![CDATA[
    @INIT_CONFIG@
    @INIT_BI_ANALYSIS@
    @INIT_BIVAR_COMPARISONS@
    HYDRA_FULL_ERROR=1 python -m dimet
        '++hydra.run.dir=.'
        '++figure_path=figures'
        '++table_path=tables'
        '++analysis={
            dataset:{
                _target_: dimet.data.DatasetConfig,
                name: "I am a synthetic data example"
             },
             method:{
                _target_: dimet.method.BivariateAnalysisConfig,
                label: "bivariate analysis",
                name: "Computation of the correlation of MDV profiles, or the metabolite time course profiles"
              },
              label: bivariate-analysis-example2
         }'
         '++analysis.conditions=${conditions}'
         '++analysis.dataset.label='
         '++analysis.method.correction_method=${correction_method}'
         '++analysis.method.impute_values=${impute_values}'
         '++analysis.method.conditions_metabolite_time_profiles=${conditions_metabolite_time_profiles}'
         '++analysis.method.timepoints_MDV_comparison=${timepoints_MDV_comparison}'
         '++analysis.method.conditions_MDV_comparison=${conditions_MDV_comparison}'
         '++analysis.method.output_include_gmean_arr_columns=true'
         '++analysis.dataset.subfolder='
         '++analysis.dataset.conditions=${conditions}'
         #if $metadata_path:
             '++analysis.dataset.metadata=metadata'
         #end if
         #if str( $data_input.data_input_selector ) == "abundance":
            #if $data_input.abundance_file:
                '++analysis.dataset.abundances=abundance'
            #end if
        #elif str( $data_input.data_input_selector ) == "mean_enrichment":
            #if $data_input.me_or_frac_contrib_file:
                '++analysis.dataset.mean_enrichment=me_or_frac_contrib'
            #end if
        #elif str( $data_input.data_input_selector ) == "isotop_prop":
            #if $data_input.isotop_prop_file:
                '++analysis.dataset.isotopologue_proportions=isotop_prop'
            #end if
        #else
            #if $data_input.isotop_abs_file:
                '++analysis.dataset.isotopologues=isotop_abs'
            #end if
        #end if
    @REMOVE_CONFIG@
    ]]></command>
    <inputs>
        <expand macro="input_parameters_bivar_analysis"/>
        <expand macro="plot_factor_list"/>
        <expand macro="correction_method"/>
    </inputs>

    <outputs>
        <collection name="report" type="list">
            <discover_datasets pattern="__designation__" directory="tables" format="tabular"/>
        </collection>
    </outputs>
    <tests>
        <test>
            <param name="data_input_selector" value="isotop_prop" />
            <param name="isotop_prop_file" ftype="tabular" value="CorrectedIsotopologues_5.csv"/>
            <param name="metadata_path" ftype="tabular" value="example5_metadata.csv"/>
            <param name="correction_method" value="fdr_bh"/>
            <repeat name="plot_factor_list">
                <param name="condition" value="Control"/>
            </repeat>
            <repeat name="plot_factor_list">
                <param name="condition" value="L-Cycloserine"/>
            </repeat>
             <output_collection name="report" type="list" count="8">
                 <element file="isotop_prop--cell--MDV-Control-L-Cycloserine--T0-pearson.tsv"  name="isotop_prop--cell--MDV-Control-L-Cycloserine--T0-pearson.tsv" ftype="tabular"/>
                 <element file="isotop_prop--cell--MDV-Control-L-Cycloserine--T2h-pearson.tsv"  name="isotop_prop--cell--MDV-Control-L-Cycloserine--T2h-pearson.tsv" ftype="tabular"/>
                 <element file="isotop_prop--cell--MDV-T2h-T0--Control-pearson.tsv"  name="isotop_prop--cell--MDV-T2h-T0--Control-pearson.tsv" ftype="tabular"/>
                 <element file="isotop_prop--cell--MDV-T2h-T0--L-Cycloserine-pearson.tsv"  name="isotop_prop--cell--MDV-T2h-T0--L-Cycloserine-pearson.tsv" ftype="tabular"/>
                 <element file="isotop_prop--med--MDV-Control-L-Cycloserine--T0-pearson.tsv"  name="isotop_prop--med--MDV-Control-L-Cycloserine--T0-pearson.tsv" ftype="tabular"/>
                 <element file="isotop_prop--med--MDV-Control-L-Cycloserine--T2h-pearson.tsv"  name="isotop_prop--med--MDV-Control-L-Cycloserine--T2h-pearson.tsv" ftype="tabular"/>
                 <element file="isotop_prop--med--MDV-T2h-T0--Control-pearson.tsv"  name="isotop_prop--med--MDV-T2h-T0--Control-pearson.tsv" ftype="tabular"/>
                 <element file="isotop_prop--med--MDV-T2h-T0--L-Cycloserine-pearson.tsv"  name="isotop_prop--med--MDV-T2h-T0--L-Cycloserine-pearson.tsv" ftype="tabular"/>
            </output_collection>
        </test>
    </tests>
    <help><![CDATA[

This module is part of DIMet: Computation of the correlation of entire MDV profiles, or the metabolite time course profiles (https://pypi.org/project/DIMet/).

DIMet bi-variate analysis performs the comparison of entire MDV profiles, with the user provided isotopologue proportions data. Moreover, when total abundances and/or mean enrichment are provided, the comparison of the metabolite time-course profiles is also computed. Specifically, three types of bi-variate comparisons are performed automatically:


-  MDV profile comparison between two conditions

-  MDV profile comparison between two consecutive time-points

-  Metabolite (total abundances and/or mean enrichment) time course profiles comparison between two conditions


For all these three types of bi-variate comparison, the statistical test that is applied is the Pearson's correlation test.


To note, MDV (Mass Distribution Vector) are obtained automatically by the tool, using the isotopologue proportions.



    **Input data files**

This tool requires (at max.) 4 tab-delimited .csv files as inputs. There are two types of files:

- The measures' (or quantifications') files, that can be of 3 types.

- The metadata, a unique file with the description of the samples in your measures' files. This is compulsory.

For running DIMet @EXECUTABLE@ you need **at least one file** of measures:

- The **isotopologue proportions** file

- The total **abundances** (of the metabolites) file

- The mean **enrichment** or labelled fractional contributions


and one metadata file, WHICH IS COMPULSORY, see section **Metadata File Information**.


**Measures files**

The measures files must be organized as matrices:

- The first column must contain Metabolite IDs that are unique (not repeated) within the file.

- The rest of the columns correspond to the samples

- The rows correspond to the metabolites

- The values must be tab separated, with the first row containing the sample/column labels.

See the following examples of measures files:


Example - Metabolites **abundances**:

    =============== ================== ================== ================== ================== ================== ==================
    ID              **MCF001089_TD01** **MCF001089_TD02** **MCF001089_TD03** **MCF001089_TD04** **MCF001089_TD05** **MCF001089_TD06**
    =============== ================== ================== ================== ================== ================== ==================
    2_3-PG          8698823.9926       10718737.7217      10724373.9         8536484.5          22060650           28898956
    2-OHGLu         36924336           424336             92060650           45165              84951950           965165051
    Glc6P           2310               2142               2683               1683               012532068          1252172
    Gly3P           399298             991656565          525195             6365231            89451625           4952651963
    IsoCit          0                  0                  0                  84915613           856236             954651610
    =============== ================== ================== ================== ================== ================== ==================

Example - mean **enrichment** or labeled fractional contributions:

    =============== ================== ================== ================== ================== ================== ==================
    ID              **MCF001089_TD01** **MCF001089_TD02** **MCF001089_TD03** **MCF001089_TD04** **MCF001089_TD05** **MCF001089_TD06**
    =============== ================== ================== ================== ================== ================== ==================
    2_3-PG          0.9711             0.968              0.9909             0.991              0.40               0.9
    2-OHGLu         0.01719            0.0246             0.554              0.555              0.73               0.68
    Glc6P           0.06               0.66               2683               0.06               2068               2172
    Gly3P           0.06               0.06               0.06               1                  5                  3
    IsoCit          0.06               1                  0.49               0.36               6                  10
    =============== ================== ================== ================== ================== ================== ==================



Example - **Isotopologue proportions**:

    =============== ================== ================== ================== ================== ================== ==================
    ID              **MCF001089_TD01** **MCF001089_TD02** **MCF001089_TD03** **MCF001089_TD04** **MCF001089_TD05** **MCF001089_TD06**
    =============== ================== ================== ================== ================== ================== ==================
    2_3-PG_m+0      0.023701408        0.026667837        0.003395407        0.05955            0.034383527        0.12
    2_3-PG_m+1      0.0                0.0                0.0                0.0                0.4                0.12
    2_3-PG_m+2      0.015379329        0.01506            0.017029723        0.35483229         0.54131313         0.743
    2_3-PG_m+3      0.960919263        0.958268099        0.97957487         0.581310816        0.017029723        0.017
    2-OHGLu_m+0     0.972778716        0.960016157        0.238843937        0.234383527        0.9998888          0.015064063
    2-OHGLu_m+1     0.0                0.0                0.0                0.0                0.0001112          0.960919263
    =============== ================== ================== ================== ================== ================== ==================



**Metadata File Information**

Provide a tab-separated file that has the names of the samples in the first column and one header row.
Column names must be exactly in this order:

   name_to_plot
   condition
   timepoint
   timenum
   compartment
   original_name


Example **Metadata File**:


    ==================== =============== ============= ============ ================ =================
    **name_to_plot**     **condition**   **timepoint** **timenum**  **compartment**   **original_name**
    -------------------- --------------- ------------- ------------ ---------------- -----------------
    Control_cell_T0-1    Control         T0            0            cell             MCF001089_TD01
    Control_cell_T0-2    Control         T0            0            cell             MCF001089_TD02
    Control_cell_T0-3    Control         T0            0            cell             MCF001089_TD03
    Tumoral_cell_T0-1    Tumoral         T0            0            cell             MCF001089_TD04
    Tumoral_cell_T0-2    Tumoral         T0            0            cell             MCF001089_TD05
    Tumoral_cell_T0-3    Tumoral         T0            0            cell             MCF001089_TD06
    Tumoral_cell_T24-1   Tumoral         T24           24           cell             MCF001089_TD07
    Tumoral_cell_T24-2   Tumoral         T24           24           cell             MCF001089_TD08
    Tumoral_cell_T24-3   Tumoral         T24           24           cell             MCF001090_TD01
    Control_med_T24-1    Control         T24           24           med              MCF001090_TD02
    Control_med_T24-2    Control         T24           24           med              MCF001090_TD03
    Tumoral_med_T24-1    Tumoral         T24           24           med              MCF001090_TD04
    Tumoral_med_T24-2    Tumoral         T24           24           med              MCF001090_TD05
    Control_med_T0-1     Control         T0            0            med              MCF001090_TD06
    Tumoral_med_T0-1     Tumoral         T0            0            med              MCF001090_TD07
    Tumoral_med_T0-2     Tumoral         T0            0            med              MCF001090_TD08
    ==================== =============== ============= ============ ================ =================


The column **original_name** must have the names of the samples as given in your data.

The column **name_to_plot** must have the names as you want them to be (or set identical to original_name if you prefer). To set names that
are meaningful is a better choice, as we will take them to display the results.

The column **timenum** must contain only the numeric part of the timepoint, for example 2,0, 10, 100 (this means, without letters ("T", "t", "s", "h" etc)
nor any other symbol). Make sure these time numbers are in the same units (but do not write the units here!).

The column **compartment** is an abbreviation, coined by you, for the compartments. This will be used for the results' files names: the longer the
compartments names are, the longer the output files' names! Please pick short and clear abbreviations to fill this column.


**Running the analysis**

You can precise how you want your analysis to be executed, with the parameters:

- **datatypes** : the measures type(s) that you want to run.

- **conditions**: the two conditions to be compared in the bi-variate analysis. If 3 or more conditions are set, the tool automatically performs all the 1-to-1 condition comparisons. If you only have one condition in your data, select the condition, and see the Note at the end of this section.

- **correction_method** : one of the methods for multiple testing correction available in statsmodels library (bonferroni, fdr_bh, sidak, among others, see https://www.statsmodels.org/dev/generated/statsmodels.stats.multitest.multipletests.html).


**Note**: the tool automatically performs the comparison of *MDV profile between two consecutive time-points*. All the time-points are processed.


There exist hints on use that will guide you, next to the parameters.

For more information about the implemented statistical tests, please visit:  https://github.com/cbib/DIMet/wiki/2-Statistical-tests

The output files are explained in https://github.com/cbib/DIMet/wiki/3-Output

**Available data for testing**

You can test our tool with the data from our manuscript https://zenodo.org/records/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/records/10579891

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