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1 <tool id="hcluster" name="Hierarchical Clustering (HAC)" force_history_refresh="True">
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2 <command interpreter="python">hclust.py
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3 -d $dataset
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4 ${dist_obj}
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5 -n ${direction}
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6 -m ${distance_metric}
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7 -l ${linkage}
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8 -k ${numk}
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9 -o ${rdata_output}
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10
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11 </command>
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12 <inputs>
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13 <param name="dataset" type="data" format='tabular' label="Data Set" help="Specify the data matrix (tab-delimited) to be clustered"/>
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14 <param name="dist_obj" type="boolean" label="Distance Object (R dist object)?" truevalue="-D" falsevalue="" checked="False" help="Check if the matrix contains the pairwise distances between a set of objects"/>
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15 <param name="direction" type="select" label="Cluster Samples or Genes?" help="Specify the matrix dimension to cluster (see help below)">
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16 <option value="cols">Columns (Samples)</option>
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17 <option value="rows" selected='true'>Rows (Genes)</option>
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18 </param>
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19
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20 <param name="distance_metric" type="select" label="Distance Metric" help="Specify the distance metric to use (see help below)">
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21 <option value="cosine" selected='true'>Cosine</option>
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22 <option value="abscosine">Absolute Cosine</option>
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23 <option value="pearson">Pearson</option>
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24 <option value="abspearson">Absolute Pearson</option>
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25 <option value="spearman">Spearman</option>
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26 <option value="kendall">Kendall</option>
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27 <option value="euclidean">Euclidean</option>
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28 <option value="maximum">Maximum</option>
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29 <option value="manhattan">Manhattan (AKA city block)</option>
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30 <option value="canberra">Canberra</option>
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31 <option value="binary">Binary</option>
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32 </param>
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33
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34 <param name="linkage" type="select" label="Linkage" help="Specify the linkage to use when clustering (see help below)">
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35 <option value="average">Average</option>
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36 <option value="centroid">Centroid</option>
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37 <option value="complete" selected='true'>Complete</option>
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38 <option value="mcquitty">McQuitty</option>
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39 <option value="median">Median</option>
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40 <option value="single">Single</option>
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41 <option value="ward">Ward</option>
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42 </param>
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43
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44 <param name="numk" type="integer" label="Number of Clusters" value="50" help="Specify the number of clusters to use"/>
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45
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46 </inputs>
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47 <outputs>
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48 <data format="rdata" name="rdata_output" label="Hierarchical Clustering Result (RData)"/>
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49 </outputs>
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50 <help>
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51 .. class:: infomark
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52
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53 **Perform Hierarchical Clustering (Cluster Samples) on a specified data set**
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54
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55 ----
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56
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57 **Parameters**
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58
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59 - **Data Set** - Specify the data matrix to be clustered. Data must be formated as follows:
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60
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61 * Tab-delimited
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62 * Use row/column headers
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63
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64 - **Cluster Samples or Genes** - Specify the dimension of the matrix to cluster:
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65
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66 * Rows (Genes)
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67 * Columns (Samples)
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68
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69 - **Distance Object** Specify whether or not the data set is a pairwise distance matrix
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70
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71 - **Distance Metric** Specify the distance metric to use. Choice of:
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72
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73 * Cosine (AKA uncentered pearson)
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74 * Absolute Cosine (AKA uncentered pearson, absolute value)
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75 * Pearson (pearson correlation)
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76 * Absolute Pearson (pearson correlation, absolute value)
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77 * Spearman (spearman correlation)
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78 * Kendall (Kendall's Tau)
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79 * Euclidean (euclidean distance)
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80 * Maximum
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81 * Manhattan (AKA city block)
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82 * Canberra
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83 * Binary
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84
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85 - **Linkage** Specify the linkage to use when clustering. Choice of:
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86
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87 * Average (see documentation for R's hclust function for explanation of choices)
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88 * Single
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89 * Complete
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90 * Median
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91 * Centroid
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92 * McQuity
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93 * Ward
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94
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95 - **Number of Clusters** Specify the number of clusters to use
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96
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97 </help>
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98 </tool>
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