diff cluster.tools/consensus.clustering.xml @ 0:0decf3fd54bc draft

Uploaded

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/cluster.tools/consensus.clustering.xml	Thu Feb 28 01:45:39 2013 -0500
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+<tool id="consensus_clustering" name="Consensus Clustering" force_history_refresh="True">
+    <command interpreter="python">consensus.clustering.py
+-d $dataset 
+-n ${direction} 
+-a ${method.algorithm} 
+#if $method.algorithm == 'hc' # -m ${method.hc_distance_metric}
+-i ${method.innerLinkage} 
+#end if
+#if $method.algorithm == 'pam' # -m ${method.pam_distance_metric}
+#end if
+#if $method.algorithm == 'km' # -m euclidean
+#end if
+-k ${kmax} 
+-r ${reps} 
+-f ${finalLinkage}
+-o ${output} 
+-h $report 
+-p ${report.files_path}
+
+</command>
+    <inputs>
+      <param name="dataset" type="data" format='tabular' label="Data Set" help="Specify the data matrix (tab-delimited) to be clustered"/>
+      <param name="direction" type="select" label="Cluster Samples or Genes?" help="Specify the matrix dimension to cluster (see help below)">
+	<option value="rows">Genes (rows)</option>
+	<option value="cols" selected="true">Samples (columns)</option>
+      </param>
+    	
+      <conditional name='method'>
+	<param name="algorithm" type="select" label="Clustering Algorithm" help="Specify the cluster method to use (see help below)">
+	  <option value="hc">Hierarchical Clustering</option>
+	  <option value="pam" selected='true'>Partioning around Medioids</option>
+	  <option value="km">K-Means Clustering</option>
+	</param>
+	<when value='hc'>
+	  <param name="hc_distance_metric" type="select" label="Distance Metric" help="Specify the distance metric to use (see help below)">
+	    <option value="cosine" selected='true'>Cosine</option>
+	    <option value="abscosine">Absolute Cosine</option>
+	    <option value="pearson">Pearson</option>
+	    <option value="abspearson">Absolute Pearson</option>
+	    <option value="spearman">Spearman</option>
+	    <option value="kendall">Kendall</option>
+	    <option value="euclidean">Euclidean</option>
+	    <option value="maximum">Maximum</option>
+	    <option value="manhattan">Manhattan (AKA city block)</option>
+	    <option value="canberra">Canberra</option>
+	    <option value="binary">Binary</option>
+	  </param>
+
+	  <param name="innerLinkage" type="select" label="Linkage for inner HAC " help="Specify the linkage to use during the 'inner' hierarchcial clustering (see help below)">
+	    <option value="average">Average</option>
+	    <option value="centroid">Centroid</option>
+	    <option value="complete" selected='true'>Complete</option>
+	    <option value="mcquitty">McQuitty</option>
+	    <option value="median">Median</option>
+	    <option value="single">Single</option>
+	    <option value="ward">Ward</option>
+	  </param>
+    	</when>
+ 	<when value='pam'>
+	  <param name="pam_distance_metric" type="select" label="Distance Metric" help="Specify the distance metric to use (see help below)">
+	    <option value="cosine" selected='true'>Cosine</option>
+	    <option value="abscosine">Absolute Cosine</option>
+	    <option value="pearson">Pearson</option>
+	    <option value="abspearson">Absolute Pearson</option>
+	    <option value="spearman">Spearman</option>
+	    <option value="kendall">Kendall</option>
+	    <option value="euclidean">Euclidean</option>
+	    <option value="maximum">Maximum</option>
+	    <option value="manhattan">Manhattan (AKA city block)</option>
+	    <option value="canberra">Canberra</option>
+	    <option value="binary">Binary</option>
+	  </param>
+    	</when>
+      </conditional>
+      <param name="finalLinkage" type="select" label="Final Linkage" help="Specify the linkage to use when clustering the consensus matrix (see help below)">
+	<option value="average">Average</option>
+	<option value="centroid">Centroid</option>
+	<option value="complete" selected='true'>Complete</option>
+	<option value="mcquitty">McQuitty</option>
+	<option value="median">Median</option>
+	<option value="single">Single</option>
+	<option value="ward">Ward</option>
+      </param>
+      
+      
+      <param name="kmax" type="integer" label="K Max" value="10" help="Maximum number of K to analyze" />
+      <param name="reps" type="integer" label="Repetitions" value="500" help="Number of Sample Permutations to Repeat"/>
+    	
+    </inputs>
+    <outputs>
+      <data format="html" name="report" label="Consensus Clustering Report (HTML)"/>
+      <data format="rdata" name="output" label="Consensus Clustering Data (RData)"/>
+    </outputs>
+<help>
+.. class:: infomark
+     
+**Perform Consensus Clustering (Cluster Samples) on a specified data set**
+
+----
+
+**Parameters**
+
+- **Data Set** - Specify the data matrix to be clustered.  Data must be formated as follows:
+
+         * Tab-delimited
+         * Use row/column headers
+
+- **Cluster Samples or Genes** - Specify the dimension of the matrix to cluster:
+
+         * Rows (Genes)
+         * Columns (Samples)
+
+- **Clustering Algorithm** Specify the choice of algorithm to use.  Choice of:
+
+         * Hierarchical Clustering
+         * K-Means
+
+- **Distance Metric** Specify the choice of distance metric to use.  Choice of:
+
+	 * Cosine (AKA uncentered pearson)
+	 * Absolute Cosine (AKA uncentered pearson, absolute value)
+         * Pearson (pearson correlation)
+	 * Absolute Pearson (pearson correlation, absolute value)
+         * Spearman (spearman correlation)
+	 * Kendall (Kendall's Tau)
+         * Euclidean (euclidean distance)
+	 * Maximum
+	 * Manhattan (AKA city block)
+	 * Canberra
+	 * Binary
+
+- **Final Linkage** Specify the choice linkage to use when clustering Consensus Matrix.  Choice of:
+
+         * Average (see documentation for R's hclust function for explanation of choices)
+         * Single
+         * Complete
+         * Median
+         * Centroid
+         * McQuity
+         * Ward
+
+- **Inner Linkage** Specify the choice linkage to use when using HAC as clustering method.  Same choices as 'Final Linkage'
+
+- **K Max** Specify the number to use for the largest K considered
+
+- **Repititions**  Specify the number of 'bootstrap' repitions to perform to generate the consensus matrix
+
+</help>
+</tool>