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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 ${linkage}
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8
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8
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9 #if str($numk) != "-1":
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10 -k ${numk}
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11 #end if
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12
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13 #if str($direction) == "rows":
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14 -o ${rdata_output_rows}
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15 #end if
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16
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17 #if str($direction) == "cols":
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18 -o ${rdata_output_cols}
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19 #end if
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20
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21 </command>
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22 <inputs>
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23 <param name="dataset" type="data" format='tabular' label="Data Set" help="Specify the data matrix (tab-delimited) to be clustered"/>
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24 <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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25 <param name="direction" type="select" label="Cluster Samples or Genes?" help="Specify the matrix dimension to cluster (see help below)">
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26 <option value="cols">Columns (Samples)</option>
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27 <option value="rows" selected='true'>Rows (Genes)</option>
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28 </param>
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29
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30 <param name="distance_metric" type="select" label="Distance Metric" help="Specify the distance metric to use (see help below)">
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31 <option value="cosine" selected='true'>Cosine</option>
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32 <option value="abscosine">Absolute Cosine</option>
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33 <option value="pearson">Pearson</option>
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34 <option value="abspearson">Absolute Pearson</option>
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35 <option value="spearman">Spearman</option>
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36 <option value="kendall">Kendall</option>
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37 <option value="euclidean">Euclidean</option>
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38 <option value="maximum">Maximum</option>
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39 <option value="manhattan">Manhattan (AKA city block)</option>
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40 <option value="canberra">Canberra</option>
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41 <option value="binary">Binary</option>
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42 </param>
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43
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44 <param name="linkage" type="select" label="Linkage" help="Specify the linkage to use when clustering (see help below)">
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45 <option value="average">Average</option>
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46 <option value="centroid">Centroid</option>
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47 <option value="complete" selected='true'>Complete</option>
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48 <option value="mcquitty">McQuitty</option>
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49 <option value="median">Median</option>
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50 <option value="single">Single</option>
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51 <option value="ward">Ward</option>
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52 </param>
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53
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54 <param name="numk" type="integer" label="Number of Clusters" value="-1" help="Specify the number of clusters to use (-1 to use default. See help below)."/>
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55
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56 </inputs>
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57 <outputs>
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58 <data format="rdata" name="rdata_output_rows" label="Hierarchical Clustering Results; Gene Clusters (RData)">
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59 <filter>(direction)=="rows"</filter>
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60 </data>
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61 <data format="rdata" name="rdata_output_cols" label="Hierarchical Clustering Results; Sample Clusters (RData)">
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62 <filter>(direction)=="cols"</filter>
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63 </data>
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64 </outputs>
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65 <help>
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66 .. class:: infomark
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67
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68 **Perform Hierarchical Clustering (Cluster Samples) on a specified data set**
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69
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70 ----
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71
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72 **Parameters**
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73
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74 - **Data Set** - Specify the data matrix to be clustered. Data must be formated as follows:
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75
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76 * Tab-delimited
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77 * Use row/column headers
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78
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79 - **Cluster Samples or Genes** - Specify the dimension of the matrix to cluster:
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80
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81 * Rows (Genes)
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82 * Columns (Samples)
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83
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84 - **Distance Object** Specify whether or not the data set is a pairwise distance matrix
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85
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86 - **Distance Metric** Specify the distance metric to use. Choice of:
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87
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88 * Cosine (AKA uncentered pearson)
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89 * Absolute Cosine (AKA uncentered pearson, absolute value)
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90 * Pearson (pearson correlation)
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91 * Absolute Pearson (pearson correlation, absolute value)
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92 * Spearman (spearman correlation)
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93 * Kendall (Kendall's Tau)
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94 * Euclidean (euclidean distance)
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95 * Maximum
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96 * Manhattan (AKA city block)
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97 * Canberra
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98 * Binary
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99
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100 - **Linkage** Specify the linkage to use when clustering. Choice of:
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101
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102 * Average (see documentation for R's hclust function for explanation of choices)
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103 * Single
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104 * Complete
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105 * Median
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106 * Centroid
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107 * McQuity
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108 * Ward
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109
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110 - **Number of Clusters** Specify the number of clusters to use. If set to -1, default values will be used, with the default set as follows:
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111 * if samples/columns are being clustered, the **default** is 5.
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112 * if genes/rows are being clustered, the **default** is set to num_rows/30, e.g. if there are 600 row/genes in the matrix, the default will be 20 clusters.
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113
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114 </help>
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115 </tool>
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