<tool id="secimtools_add_pval_flags" name="Add Binary (0/1) P-value Flags" version="@WRAPPER_VERSION@">
  <description>using custom thresholds</description>
  <macros>
      <import>macros.xml</import>
  </macros>
  <expand macro="requirements" />
  <command detect_errors="exit_code"><![CDATA[
    add_pval_flags.py
    -de=$DE_Dataset
    -id=$uniqID
    -p=$pvalue
    -t=$thresholds
    -o=$output
    -fl=$flags
  ]]></command>
  <inputs>
    <param name="DE_Dataset" type="data" format="tabular" label="Select Dataset containing p-values from your history"/>
    <param name="uniqID" type="text" size="30" value="" label="Unique FeatureID" help="Name of the column in your Dataset that contains unique FeatureIDs."/>
    <param name="pvalue" type="text" size="30" value="" label="P-Value" help="Name of the column containing your p-values."/>
    <param name="thresholds" type="text" size="30" value="0.1,0.05,0.01" label="P-Value Thresholds" help="Enter the threshold(s). P-values less than the given threshold(s) will be flagged with a 1. Separate more than 1 threshold value with a comma; no spaces allowed. Default values are 0.1,0.05,0.01"/>
  </inputs>
  <outputs>
    <data format="tabular" name="output" label="${tool.name} on ${on_string}: Output File"/>
    <data format="tabular" name="flags" label="${tool.name} on ${on_string}: Flags File"/>
  </outputs>
  <tests>
    <test>
      <param name="DE_Dataset" value="limma_voom_gene.tsv"/>
      <param name="uniqID" value="UniqueID"/>
      <param name="pvalue" value="P.Value"/>
      <param name="thresholds" value="0.1,0.05,0.01"/>
      <output name="output" file="add_flags_gene_output.tsv"/>
      <output name="flags" file="add_flags_gene_flags.tsv"/>
    </test>
    <test>
      <param name="DE_Dataset" value="limma_voom_metabolite.tsv"/>
      <param name="uniqID" value="UniqueID"/>
      <param name="pvalue" value="P.Value"/>
      <param name="thresholds" value="0.1,0.05,0.01"/>
      <output name="output" file="add_flags_metabolite_output.tsv"/>
      <output name="flags" file="add_flags_metabolite_flags.tsv"/>
    </test>
  </tests>
  <help><![CDATA[
**Tool Description**

 This tool generates 0/1 indicator variables to identify (‘flag’) p-values less than a user-specified threshold or less than the 
 default values of 0.1, 0.05 and 0.01.  The flag_threshold variable is equal to 1 if the p-value is less than or equal to the 
 threshold and equal to 0 if greater than the threshold.  You can flag nominal p-values or p-values after correction for multiple testing.

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

**INPUT**

**Example of a dataset containing p-values**

  +-----------+---------+---------+-----+
  | FeatureID | P-value | FDR     | ... |
  +===========+=========+=========+=====+
  | one       | 2.02e-6 | 1.83e-6 | ... |
  +-----------+---------+---------+-----+
  | two       | 2.87e-5 | 0.0063  | ... |
  +-----------+---------+---------+-----+
  | three     | 0.001   | 0.19    | ... |
  +-----------+---------+---------+-----+
  | four      | 0.22    | 0.99    | ... |
  +-----------+---------+---------+-----+
  | ...       | ...     | ...     | ... |
  +-----------+---------+---------+-----+

**P-value**

  Name of the column in your Dataset that contains p-values.

**P-value Threshold**

  P-values less than the threshold will be flagged with a 1. P-values greater than the threshold will be flagged with a 0.

  **NOTE:** More than one threshold value is allowed. Separate flags will be generated for each threshold value.

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

**OUTPUT**

  Two output files are generated from the Add Binary (0/1) P-value Flags tool:

**Output File.** A TSV file containing the same columns as the Input Dataset plus an additional column(s) containing 0/1 binary indicators for whether the P-value was less that the user-specified threshold.  The indicator columns are named by appending the user-specified threshold to "Flag" prefix (e.g. Flag_user-specified threshold, Flag_0.10).

  +-----------+---------+---------+--------+-------------+-----+
  | FeatureID | p-value | FDR     | Flag_0.2 | Flag_0.05 | ... |
  +===========+=========+=========+==========+===========+=====+
  | one       | 2.02e-6 | 1.83e-6 | 1        | 1         | ... |
  +-----------+---------+---------+----------+-----------+-----+
  | two       | 2.87e-5 | 0.0063  | 1        | 1         | ... |
  +-----------+---------+---------+----------+-----------+-----+
  | three     | 0.001   | 0.19    | 1        | 0         | ... |
  +-----------+---------+---------+----------+-----------+-----+
  | four      | 0.22    | 0.99    | 0        | 0         | ... |
  +-----------+---------+---------+----------+-----------+-----+
  | ...       | ...     | ...     | ...      | ...       | ... |
  +-----------+---------+---------+----------+-----------+-----+

**Flag Table.** A TSV file containing only the FeatureID column from the input dataset and the tool-generated binary indicator flags.

  +-----------+----------+-----------+-----+
  | FeatureID | Flag_0.2 | Flag_0.05 | ... |
  +===========+==========+===========+=====+
  | one       | 1        | 1         | ... |
  +-----------+----------+-----------+-----+
  | two       | 1        | 1         | ... |
  +-----------+----------+-----------+-----+
  | three     | 1        | 0         | ... |
  +-----------+----------+-----------+-----+
  | four      | 0        | 0         | ... |
  +-----------+----------+-----------+-----+
  | ...       | ...      | ...       | ... |
  +-----------+----------+-----------+-----+

]]></help>
  <citations>
    <citation type="bibtex">@ARTICLE{Kirpich17secimtools,
    author = {Alexander S. Kirpich, Miguel Ibarra, Oleksandr Moskalenko, Justin M. Fear, Joseph Gerken, Xinlei Mi, Ali Ashrafi, Alison M. Morse, Lauren M. McIntyre},
    title = {SECIMTools: A suite of Metabolomics Data Analysis Tools},
    journal = {BMC Bioinformatics},
    year = {2018}
    }</citation>
    <citation type="bibtex">@article{Mor2021GaitGM,
    title={GAIT-GM integrative cross-omics analyses reveal cholinergic defects in a C. elegans model of Parkinson's disease},
    author={Mor, DE and Huertas, F and Morse, AM and Kaletsky, R and Murphy, CT and Kalia, V and Miller, GW and Moskalenko, O and Conesa, A and McIntyre, LM},
    journal={BMC Genomics},
    year={submitted},
    }</citation>
  </citations>
</tool>