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1 <tool id="secimtools_principal_component_analysis" name="Principal Component Analysis (PCA)" version="@WRAPPER_VERSION@">
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2 <description>for visual summaries of the components.</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 principal_component_analysis.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 --load_out $loadings
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13 --score_out $scores
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14 --summary_out $summary
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15 --figure $figures
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16
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17 #if $group
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18 --group $group
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19 #end if
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20 ]]></command>
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21 <inputs>
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22 <param name="input" type="data" format="tabular" label="Wide Dataset" help="Input your tab-separated wide format dataset. If file is not tab separated see TIP below."/>
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23 <param name="design" type="data" format="tabular" label="Design File" help="Input your design file (tab-separated). Note you need a 'sampleID' column. If not tab separated see TIP below."/>
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24 <param name="uniqID" type="text" size="30" value="" label="Unique Feature ID" help="Name of the column in your wide dataset that has unique identifiers."/>
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25 <param name="group" type="text" size="30" label="Group/Treatment [Optional]" help="Name of the column in your design file that contains group classifications."/>
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26 </inputs>
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27 <outputs>
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28 <data format="tabular" name="loadings" label="${tool.name} on ${on_string}: loadings"/>
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29 <data format="tabular" name="scores" label="${tool.name} on ${on_string}: scores"/>
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30 <data format="tabular" name="summary" label="${tool.name} on ${on_string}: summary"/>
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31 <data format="pdf" name="figures" label="${tool.name} on ${on_string}: scatter plots"/>
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32 </outputs>
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33 <tests>
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34 <test>
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35 <param name="input" value="ST000006_data.tsv"/>
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36 <param name="design" value="ST000006_design.tsv"/>
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37 <param name="uniqID" value="Retention_Index" />
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38 <param name="group" value="White_wine_type_and_source" />
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39 <output name="loadings" file="ST000006_principal_component_analysis_load_out.tsv" />
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40 <output name="scores" file="ST000006_principal_component_analysis_score_out.tsv" />
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41 <output name="summary" file="ST000006_principal_component_analysis_summary_out.tsv" />
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42 <output name="figures" file="ST000006_principal_component_analysis_figure.pdf" compare="sim_size" delta="10000" />
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43 </test>
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44 </tests>
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45 <help><![CDATA[
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46
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47 @TIP_AND_WARNING@
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48
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49 **Tool Description**
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50
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51 The tool performs principal component analysis (PCA) of the data.
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52 Visual summaries are provided in the from of 2D and 3D scatter plots for the first three principal components.
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53 Samples in the scatter plots are colored based on the group classification.
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54
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55 --------------------------------------------------------------------------------
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56
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57 **Note**
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58
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59 - This tool currently treats all variables as continuous numeric variables. Running the tool on categorical variables might result in incorrect results.
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60 - Rows containing non-numeric (or missing) data in any of the chosen columns will be skipped from the analysis.
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61
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62 --------------------------------------------------------------------------------
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63
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64 **Input**
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65
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66 - Two input datasets are required.
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67
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68 @WIDE@
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69
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70 **NOTE:** The sample IDs must match the sample IDs in the Design File (below).
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71 Extra columns will automatically be ignored.
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72
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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 @GROUP_OPTIONAL@
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79
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80 --------------------------------------------------------------------------------
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81
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82 **Output**
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83
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84 Four different outputs are produced by the Principal Component Analysis tool:
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85
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86 (1) a TSV file containing eigenvectors/variable loadings
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87 (2) a TSV file containing scores of input data on principal components
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88 (3) a TSV file with the summary for each component
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89 (4) and a PDF file of scatter plots of the first three principal components
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90
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91 There are a total of four scatterplots: three pairwise plots for the first three components and a single 3D plot of the first three components.
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92
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93 ]]></help>
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94 <expand macro="citations"/>
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95 </tool>
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