comparison NetworkAttributes.xml @ 0:c8a8a1e90a9d draft

Uploaded

0:c8a8a1e90a9d

<tool id="network_attributes" name="Calculate Network Attributes">
  <description></description>
  <command interpreter="python">Calculate_attributes_wrapper.py $edges $centrality $modularity $node_attr \$INSTALL_RUN_PATH/</command>
  <requirements>
    <requirement type="set_environment">INSTALL_RUN_PATH</requirement>
  </requirements>
  <inputs>
    <param format="sif" name="edges" type="data" label="Network File"/>
    <param type="select" name="centrality" label="Select Centrality Metric"/>
		<option value="eigenvector">eigenvector centrality</option>
		<option value="betweenness">betweenness centrality</option>
		<option value="closeness">closeness centrality</option>
		<option value="PageRank">Page Rank</option>
    <param type="select" name="modularity" label="Select Community Detection Algorithm">
      <option value="fast.greedy">Fast Greedy (recommended)</option>
      <option value="optimal">Optimal (see note)</option>
      <option value="edge.betweenness">Edge Betweenness</option>
	  <option value="walk.trap">Walk Trap</option>
	  <option value="spin.glass">Spin Glass</option>
	  <option value="leading eigenvector">Leading Eigenvector</option>
	  <option value="label.propagation">Label Propagation</option>
	  <option value="multilevel">Multilevel</option>
    </param>
  </inputs>
  <outputs>
    <data format="txt" name="Node_attributes" label="Node Attributes"/>
  </outputs>
  <stdio>
    <regex match="Error|error"
     source="stdout"
           level="fatal"
           description="Unknown error"/>
    <regex match="Error|error"
     source="stderr"
           level="fatal"
           description="Unknown error"/>
  </stdio>

  <tests>
    <test>
      <param name="input" value="fa_gc_content_input.fa"/>
      <output name="out_file1" file="fa_gc_content_output.txt"/>
    </test>
  </tests>
  <help>
SAINT Output to Protein Interaction File
----------------------------------------

This tool reads in a network sif file and calculates the specified centrality
metric and perform community detection based on the specified algorithm.

**1) Network File **
    This file must be a two column sif file (tab delimited)
	e.g. EGFR\_HUMAN	GRB2\_HUMAN

**2) Centrality**
    Specify centrality metric measuring influence or bottlenecks in the network

**3) Modularity**
    Please specify community detection algorithm.
	**NOTE:** Optimal community detection computational time scales exponentially
	with the number of nodes. Use with caution if number of nodes > 30.
  </help>
</tool>