<tool id="coVennTree" name="CoVennTree (Comparative weighted Venn Tree) - Rooted Tree" version="1.6.0">
    <description>Comparative rooted tree analysis for files in dsv format</description>
    <requirements>
        <requirement type="package" version="1.6">coVennTree</requirement>
        <requirement type="package" version="5.18.1">perl</requirement>
    </requirements>
    <command interpreter="perl">
        coVennTree.pl
        $infile
        $color_mode
        $trans_func
        $leafs_allInformation
        $outfile_network
        $outfile_attribute
    </command>
    
    <inputs>
        <param name="infile" type="data" format="tabular" label="Path File" help="Tabular file containing the paths and values"/>
        
      
        <param name="color_mode" multiple="false" type="select" label="Select color mode for Venn diagrams">
                <option value="0">(1) Set1: blue       Set2: red       Set3: yellow</option>
                <option value="1">(2) Set1: red        Set2: green     Set3: blue</option>
                <option value="2">(3) Set1: green      Set2: magenta   Set3: blue</option>
                <option value="3">(4) Set1: green      Set2: purple    Set3: red</option>
                <option value="4">(5) Set1: dark gray  Set2: mid-grey  Set3: light gray</option>
        </param>
        
             
        <param name="trans_func" multiple="false" type="select" label="Select transformation function">
                <option value="0">(1) datasets max: 3,000 data points in sum</option>
                <option value="1">(2) datasets max: 30,000 data points in sum</option>
                <option value="2">(3) datasets max: 300,000 data points in sum</option>
                <option value="3">(4) datasets max: 3,000,000 data points in sum</option>
                <option value="4">(5) datasets max: 30,000,000 data points in sum</option>
                <option value="5">(6) datasets max: 300,000,000 data points in sum</option>
                <option value="6">(7) datasets max: 3,000,000,000 data points in sum</option>
        </param>
       
        
        <param name="leafs_allInformation" multiple="false" type="select" label="Select tree analyzes function">
                <option value="1">(1) leaf + inner nodes informations</option>
                <option value="0">(2) only leaf information</option>
        </param>
        
    </inputs>
    
    <outputs>
        <data format="tabular" name="outfile_network" label="Network" />
        <data format="tabular" name="outfile_attribute" label="Attributes" />
    </outputs>
    
    <tests>
        <test>
        </test>
    </tests>
    
    <help>
.. class:: infomark

CoVennTree compares up to three rooted trees at the same time.

CoVennTree (Comparative weighted Venn Tree) is a software to analyze and compare up to three datasets. Unlike other
methods, CoVennTree correlates data on the leaf level and transfers this information to the root node. CoVennTree works with numbers to compute weighted
Venn diagrams for each node in the graph (rooted tree). Therefore any kind of input data can be processed as long as the data structure will be taken into account.



**Input**

*Input example*


.. image:: $PATH_TO_IMAGES/example1.png 
  :height: 430 
  :width: 600


*dsv-format: The following table represents the graph.*


===========  ======  ======  ======
#Datasets    set1    set2    set3
===========  ======  ======  ======
"root;"      0       0       0
"root;A;"    10000   0       0
"root;A;C;"  600000  300000  500000
"root;A;D;"  0       100000  200000
"root;A;E;"  800000  0       100000
"root;B;"    10000   20000   50000
===========  ======  ======  ======


-------


**Results**

A specific color is assigned to each dataset in five optional color schemes (see parameter "Select color mode for weighted Venn diagrams").
In this example set1 corresponds to color blue, set2 to red and set3 to yellow.
In order to cover a wide numerical range a non linear transformation function is used.


*Data format \*.sif*

[parent_node]   [connected_with]    [child_node]


*Data format \*.venn*

[id]    [google_url]    [id_vds]    [Venn_abs_values]


*Output example "leaf information and not assigned information"*

By selecting "leaf information + not assigned information" artificial nodes can be inserted.
Artificial nodes will be inserted if inner nodes have values larger than zero.

.. image:: $PATH_TO_IMAGES/venn-graph-off.png 
  :height: 358 
  :width: 425
  
  
-------


*Output example "only leaf information"*

By selecting "only leaf information" only leaf nodes are considered for the computation of weighted Venn diagrams.

.. image:: $PATH_TO_IMAGES/venn-graph-on.png 
  :height: 358
  :width: 400



    </help>
    <citations>
        <citation type="doi">
        
        </citation>>
    </citations>
</tool>