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1 <tool id="consensus_clustering" name="Consensus Clustering" force_history_refresh="True">
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2 <command interpreter="python">consensus.clustering.py
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3 -d $dataset
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4 -n ${direction}
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5 -a ${method.algorithm}
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6 #if $method.algorithm == 'hc' # -m ${method.hc_distance_metric}
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7 -i ${method.innerLinkage}
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8 #end if
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9 #if $method.algorithm == 'pam' # -m ${method.pam_distance_metric}
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10 #end if
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11 #if $method.algorithm == 'km' # -m euclidean
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12 #end if
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13 -k ${kmax}
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14 -r ${reps}
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15 -f ${finalLinkage}
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16 -o ${output}
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17 -h $report
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18 -p ${report.files_path}
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19
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20 </command>
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21 <inputs>
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22 <param name="dataset" type="data" format='tabular' label="Data Set" help="Specify the data matrix (tab-delimited) to be clustered"/>
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23 <param name="direction" type="select" label="Cluster Samples or Genes?" help="Specify the matrix dimension to cluster (see help below)">
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24 <option value="rows">Genes (rows)</option>
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25 <option value="cols" selected="true">Samples (columns)</option>
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26 </param>
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27
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28 <conditional name='method'>
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29 <param name="algorithm" type="select" label="Clustering Algorithm" help="Specify the cluster method to use (see help below)">
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30 <option value="hc">Hierarchical Clustering</option>
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31 <option value="pam" selected='true'>Partioning around Medioids</option>
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32 <option value="km">K-Means Clustering</option>
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33 </param>
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34 <when value='hc'>
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35 <param name="hc_distance_metric" type="select" label="Distance Metric" help="Specify the distance metric to use (see help below)">
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36 <option value="cosine" selected='true'>Cosine</option>
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37 <option value="abscosine">Absolute Cosine</option>
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38 <option value="pearson">Pearson</option>
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39 <option value="abspearson">Absolute Pearson</option>
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40 <option value="spearman">Spearman</option>
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41 <option value="kendall">Kendall</option>
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42 <option value="euclidean">Euclidean</option>
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43 <option value="maximum">Maximum</option>
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44 <option value="manhattan">Manhattan (AKA city block)</option>
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45 <option value="canberra">Canberra</option>
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46 <option value="binary">Binary</option>
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47 </param>
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48
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49 <param name="innerLinkage" type="select" label="Linkage for inner HAC " help="Specify the linkage to use during the 'inner' hierarchcial clustering (see help below)">
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50 <option value="average">Average</option>
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51 <option value="centroid">Centroid</option>
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52 <option value="complete" selected='true'>Complete</option>
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53 <option value="mcquitty">McQuitty</option>
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54 <option value="median">Median</option>
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55 <option value="single">Single</option>
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56 <option value="ward">Ward</option>
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57 </param>
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58 </when>
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59 <when value='pam'>
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60 <param name="pam_distance_metric" type="select" label="Distance Metric" help="Specify the distance metric to use (see help below)">
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61 <option value="cosine" selected='true'>Cosine</option>
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62 <option value="abscosine">Absolute Cosine</option>
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63 <option value="pearson">Pearson</option>
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64 <option value="abspearson">Absolute Pearson</option>
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65 <option value="spearman">Spearman</option>
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66 <option value="kendall">Kendall</option>
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67 <option value="euclidean">Euclidean</option>
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68 <option value="maximum">Maximum</option>
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69 <option value="manhattan">Manhattan (AKA city block)</option>
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70 <option value="canberra">Canberra</option>
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71 <option value="binary">Binary</option>
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72 </param>
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73 </when>
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74 </conditional>
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75 <param name="finalLinkage" type="select" label="Final Linkage" help="Specify the linkage to use when clustering the consensus matrix (see help below)">
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76 <option value="average">Average</option>
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77 <option value="centroid">Centroid</option>
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78 <option value="complete" selected='true'>Complete</option>
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79 <option value="mcquitty">McQuitty</option>
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80 <option value="median">Median</option>
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81 <option value="single">Single</option>
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82 <option value="ward">Ward</option>
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83 </param>
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84
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85
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86 <param name="kmax" type="integer" label="K Max" value="10" help="Maximum number of K to analyze" />
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87 <param name="reps" type="integer" label="Repetitions" value="500" help="Number of Sample Permutations to Repeat"/>
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88
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89 </inputs>
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90 <outputs>
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91 <data format="html" name="report" label="Consensus Clustering Report (HTML)"/>
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92 <data format="rdata" name="output" label="Consensus Clustering Data (RData)"/>
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93 </outputs>
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94 <help>
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95 .. class:: infomark
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96
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97 **Perform Consensus Clustering (Cluster Samples) on a specified data set**
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98
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99 ----
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100
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101 **Parameters**
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102
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103 - **Data Set** - Specify the data matrix to be clustered. Data must be formated as follows:
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104
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105 * Tab-delimited
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106 * Use row/column headers
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107
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108 - **Cluster Samples or Genes** - Specify the dimension of the matrix to cluster:
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109
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110 * Rows (Genes)
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111 * Columns (Samples)
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112
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113 - **Clustering Algorithm** Specify the choice of algorithm to use. Choice of:
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114
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115 * Hierarchical Clustering
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116 * K-Means
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117
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118 - **Distance Metric** Specify the choice of distance metric to use. Choice of:
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119
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120 * Cosine (AKA uncentered pearson)
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121 * Absolute Cosine (AKA uncentered pearson, absolute value)
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122 * Pearson (pearson correlation)
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123 * Absolute Pearson (pearson correlation, absolute value)
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124 * Spearman (spearman correlation)
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125 * Kendall (Kendall's Tau)
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126 * Euclidean (euclidean distance)
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127 * Maximum
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128 * Manhattan (AKA city block)
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129 * Canberra
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130 * Binary
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131
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132 - **Final Linkage** Specify the choice linkage to use when clustering Consensus Matrix. Choice of:
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133
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134 * Average (see documentation for R's hclust function for explanation of choices)
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135 * Single
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136 * Complete
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137 * Median
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138 * Centroid
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139 * McQuity
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140 * Ward
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141
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142 - **Inner Linkage** Specify the choice linkage to use when using HAC as clustering method. Same choices as 'Final Linkage'
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143
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144 - **K Max** Specify the number to use for the largest K considered
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145
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146 - **Repititions** Specify the number of 'bootstrap' repitions to perform to generate the consensus matrix
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147
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148 </help>
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149 </tool>
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