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1 <tool id="secimtools_modulated_modularity_clustering" name="Modulated Modularity Clustering (MMC)" version="@WRAPPER_VERSION@">
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2 <description>with visual summaries.</description>
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3 <macros>
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4 <import>macros.xml</import>
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5 </macros>
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6 <expand macro="requirements" />
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7 <command detect_errors="exit_code"><![CDATA[
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8 modulated_modularity_clustering.py
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9 --input $input
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10 --design $design
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11 --ID $uniqID
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12 --out $output
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13 --figure $figure
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14 --sigmaLow $sigmaLow
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15 --sigmaHigh $sigmaHigh
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16 --sigmaNum $sigmaNum
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17 --correlation $corr
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18 ]]></command>
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19 <inputs>
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20 <param name="input" type="data" format="tabular" label="Wide Dataset" help="Input your tab-separated wide format dataset. If not tab separated see TIP below." />
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21 <param name="design" type="data" format="tabular" label="Design Dataset" help="Input your design file (tab-separated). Note you need a 'sampleID' column. If not tab separated see TIP below."/>
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22 <param name="uniqID" type="text" size="30" label="Unique Feature ID" help="Name of the column in your wide dataset that has unique identifiers.." />
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23 <param name="sigmaLow" type="float" size="6" value="0.05" label="Lower sigma bound" help="Default: 0.05." />
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24 <param name="sigmaHigh" type="float" size="6" value="0.50" label="Upper sigma bound" help="Default: 0.50." />
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25 <param name="sigmaNum" type="float" size="6" value="451" label="Number of Sigma values" help="Number of values of sigma to search. Default: 451." />
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26 <param name="corr" type="select" value="pearson" label="Correlation method" help="Select correlation method for preliminary correlation before clustering. Default: Pearson." >
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27 <option value="pearson" selected="true">Pearson</option>
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28 <option value="kendall" selected="true">Kendall</option>
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29 <option value="spearman" selected="true">Spearman</option>
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30 </param>
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31 </inputs>
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32 <outputs>
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33 <data format="tabular" name="output" label="${tool.name} on ${on_string}: Values"/>
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34 <data format="pdf" name="figure" label="${tool.name} on ${on_string}: Heatmaps"/>
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35 </outputs>
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36 <tests>
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37 <test>
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38 <param name="input" value="ST000006_data.tsv"/>
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39 <param name="design" value="ST000006_design.tsv"/>
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40 <param name="uniqID" value="Retention_Index" />
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41 <param name="corr" value="pearson" />
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42 <output name="output" file="ST000006_modulated_modularity_clustering_out.tsv" compare="sim_size" delta="10000"/>
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43 <output name="figure" file="ST000006_modulated_modularity_clustering_figure.pdf" compare="sim_size" delta="10000" />
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44 </test>
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45 </tests>
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46 <help><![CDATA[
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47
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48 @TIP_AND_WARNING@
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49
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50 **Tool Description**
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51
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52 Modulated Modularity Clustering method (MMC) was designed to detect latent structure in data using weighted graphs.
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53 The method searches for optimal community structure and detects the magnitude of pairwise relationships.
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54 The optimal number of clusters and the optimal cluster size are selected by the method during the analysis.
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55
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56 The initial boundaries (lower and upper) for sigma as well as the number of points in the search grid (number of sigma values) are specified initially by the user.
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57 The boundaries are extended automatically by the algorithm if the values are close to the boundary. The correlation type (Pearson, Kendall or Spearman) can be specified.
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58
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59 More details about the method can be found in:
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60
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61 Stone, E. A., and Ayroles, J. F. (2009). Modulated modularity clustering as an exploratory tool for functional genomic inference. PLoS Genet, 5(5), e1000479.
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62
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63
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64 --------------------------------------------------------------------------------
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65
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66 **Input**
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67
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68 - Two input datasets are required.
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69
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70 @WIDE@
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71
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72 **NOTE:** The sample IDs must match the sample IDs in the Design File (below). Extra columns will automatically be ignored.
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73
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74 @METADATA@
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75
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76 @UNIQID@
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77
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78 **Lower sigma value**
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79
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80 - Default: 0.05.
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81
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82 **Upper sigma value**
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83
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84 - Default: 0.50.
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85
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86 **Sigma values**
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87
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88 - Number of values of sigma to search. Default: 451. Higher numbers increase the precision but decrease the performance time.
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89
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90 **Correlation method**
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91
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92 - Correlation method for preliminary correlation before clustering. Default = Pearson.
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93
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94 --------------------------------------------------------------------------------
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95
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96 **Output**
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97
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98 The tool produces four files: a single TSV file and three PDF files:
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99
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100 (1) a TSV file containing the algorithm summaries and
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101 (2) three PDF files containing (i) unsorted, (ii) sorted, and (iii) sorted and smoothed dependency heatmaps produced by the MMC algorithm respectively.
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102
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103
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104 ]]></help>
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105 <expand macro="citations"/>
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106 </tool>
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