comparison pathway_image.xml @ 21:d6b961721037

Miller Lab Devshed version 4c04e35b18f6

20:8a4b8efbc82c

  <command interpreter="python">
    mkpthwpng.py
      "--input=${input}"
      "--output=${output}"
      "--KEGGpath=${pathway}"
      "--posKEGGclmn=${input.metadata.kegg_path}"
      "--KEGGgeneposcolmn=${input.metadata.kegg_gene}"
  </command>

  <inputs>
    <param name="input" type="data" format="gd_sap" label="Table">
      <validator type="metadata" check="kegg_gene,kegg_path" message="Missing KEGG gene code column and/or KEGG pathway code/name column metadata.  Click the pencil icon in the history item to edit/save the metadata attributes" />
    </param>
    <param name="pathway" type="select">
      <options from_file="gd.pathways.txt">
        <column name="value" index="1"/>
        <column name="name" index="2"/>
        <filter type="data_meta" ref="input" key="dbkey" column="0" separator="\t" />
      </options>

  </outputs>

  <tests>
    <test>
      <param name="input" value="test_in/sample.gd_sap" ftype="gd_sap" />
      <param name="pathway" value="cfa05214" />
      <output name="output" file="test_out/pathway_image/pathway_image.png" compare="sim_size" delta = "10000" />
    </test>
  </tests>

  <help>

**What it does**

This tool produces an image of an input KEGG pathway, highlighting the
modules representing genes in an input list.  NOTE:  a given gene can
be assigned to multiple modules, and different genes can be assigned to
the same module.

  </help>
</tool>

21:d6b961721037

  <command interpreter="python">
    mkpthwpng.py
      "--input=${input}"
      "--output=${output}"
      "--KEGGpath=${pathway}"
      "--posKEGGclmn=${kpath}"
      "--KEGGgeneposcolmn=${kgene}"
  </command>

  <inputs>
    <param name="input" type="data" format="tabular" label="Dataset" />
    <param name="kgene" type="data_column" data_ref="input" label="Column with KEGG gene ID"  />
    <param name="kpath" type="data_column" data_ref="input" numerical="false" label="Column with KEGG pathways" />
    <param name="pathway" label="Pathway" type="select">
      <options from_file="gd.pathways.txt">
        <column name="value" index="1"/>
        <column name="name" index="2"/>
        <filter type="data_meta" ref="input" key="dbkey" column="0" separator="\t" />
      </options>

  </outputs>

  <tests>
    <test>
      <param name="input" value="test_in/sample.gd_sap" ftype="gd_sap" />
      <param name="kpath" value="10" />
      <param name="kgene" value="12" />
      <param name="pathway" value="cfa05214" />
      <output name="output" file="test_out/pathway_image/pathway_image.png" compare="sim_size" delta = "10000" />
    </test>
  </tests>

  <help>

**Dataset formats**

The input and output datasets are in tabular_ format.
The input dataset must have columns with KEGG gene ID and pathways.
The output dataset is described below.
(`Dataset missing?`_)

.. _tabular: ./static/formatHelp.html#tab
.. _Dataset missing?: ./static/formatHelp.html

-----

**What it does**

This tool produces an image of a KEGG pathway, highlighting (in red) the
modules representing genes in the input dataset.  Click here_ for help
with reading the pathway map.

NOTE:  a given gene can
be assigned to multiple modules, and different genes can be assigned to
the same module.

.. _here: http://www.genome.jp/kegg/document/help_pathway.html

-----

**Example**

- input::

   476153  probably damaging       cfa00230=Purine metabolism.cfa00500=Starch and sucrose metabolism.cfa00740=Riboflavin metabolism.cfa00760=Nicotinate and nicotinamide metabolism.cfa00770=Pantothenate and CoA biosynthesis.cfa01100=Metabolic pathways
   483960  probably damaging       N
   610160  possibly damaging       N
   403657  benign  cfa04010=MAPK signaling pathway.cfa04012=ErbB signaling pathway.cfa04060=Cytokine-cytokine receptor interaction.cfa04144=Endocytosis.cfa04510=Focal adhesion.cfa04540=Gap junction.cfa04810=Regulation of actin cytoskeleton.cfa05160=Hepatitis C.cfa05200=Pathways in cancer.cfa05212=Pancreatic cancer.cfa05213=Endometrial cancer.cfa05214=Glioma.cfa05215=Prostate cancer.cfa05218=Melanoma.cfa05219=Bladder cancer.cfa05223=Non-small cell lung cancer
   etc.

output showing pathway cfa05214:

.. image:: ${static_path}/images/gd_pathway_image.png

  </help>
</tool>