<?xml version="1.0" encoding="utf-8"?>
<tool id="sc3_calc_consens" name="SC3 Calculate Consensus" version="@TOOL_VERSION@+galaxy0">
  <description>from multiple runs of k-means clustering</description>
  <macros>
    <import>sc3-macros.xml</import>
  </macros>
  <expand macro="requirements" />
  <command detect_errors="exit_code"><![CDATA[
sc3-sc3-calc-consens.R -i '${sc3_kmeans}' -t '${clusters}' -o '${sc3_consensus}'
  ]]></command>

  <inputs>
    <param name="sc3_kmeans" type="data" format="rdata" label="Serialised sc3 SingleCellExperiment object with stored kmeans clustering"/>
  </inputs>

  <outputs>
    <data name="clusters" format="csv" label="${tool.name} on ${on_string}: text table storing consensus clustering"/>
    <data name="sc3_consensus" format="rdata" label="${tool.name} on ${on_string}: serialised sc3 SingleCellExperiment with consensus matrix."/>
  </outputs>

  <tests>
    <test>
      <param name="sc3_kmeans" value="sc3_kmeans.rds"/>
      <output name="clusters" value="consensus_clusters.txt"/>
      <output name="sc3_consensus" file="sc3_consensus.rds"/>
    </test>
  </tests>

  <help><![CDATA[
==============================================
This function calculates consensus clustering.
==============================================

Calculate consensus matrices based on the clustering solutions contained in the
kmeans item of the sc3 slot of the metadata(object). It then creates and
populates the consensus item of the sc3 slot with consensus matrices, their
hierarchical clusterings in hclust objects, and Silhouette indeces of the
clusters. It also removes the previously calculated kmeans clusterings from the
sc3 slot, as they are not needed for further analysis.

@HELP@

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