Mercurial > repos > iuc > dimet_metabologram

annotate dimet_metabologram.xml @ 3:b6e621c83f94 draft

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planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/DIMet commit 0e7c185547d51ec3fc8ff2d87ac7e2b6d9ae0b0f
author iuc
date Mon, 04 Mar 2024 19:57:02 +0000
parents 7ff4f61789de
children c7ff01bd331b
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a7d3e880e74c planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/DIMet commit abca848510cb4ac8d09d95634147626ea578cdf0
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1 <tool id="dimet_@EXECUTABLE@" name="dimet @EXECUTABLE@" version="@TOOL_VERSION@+galaxy@VERSION_SUFFIX@" profile="20.05">
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2 <description>
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3 Integration of transcriptomics and (tracer) metabolomics differential data (by DIMet)
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4 </description>
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5 <macros>
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6 <token name="@EXECUTABLE@">metabologram</token>
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7 <import>macros.xml</import>
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8 </macros>
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9 <expand macro="requirements"/>
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10 <command detect_errors="exit_code"><![CDATA[
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11 @INIT_CONFIG@
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12 @INIT_METABOLOGRAM@
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13 @INIT_TRANSCRIPTS@
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14 @INIT_GROUPS@
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15 @INIT_COMPARISONS_METABOLOGRAM@
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16 @INIT_PATHWAYS@
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17 @INIT_STAT_TEST@
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18 HYDRA_FULL_ERROR=1 python -m dimet
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19 -cp '$__new_file_path__/config'
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20 '++figure_path=figures'
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21 '++table_path=tables'
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22 '++hydra.run.dir=metabologram'
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23 '++analysis={
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24 dataset:{
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25 _target_: dimet.data.DataIntegrationConfig,
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26 name: "I am a synthetic data example"
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27 },
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28 method:{
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29 _target_: dimet.method.MetabologramIntegrationConfig,
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30 label: "metabologram",
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31 name: "Perform data integration via metabologram visualization",
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32 abs_values_scale_color_bar: {transcripts: null, metabolites:null},
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33 colors_divergent_palette: ['royalblue', 'white', 'red'],
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34 edge_color: ['black','black'],
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35 line_width:['1','1.2'],
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36 display_label_and_value: true,
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37 font_size: ${output_options.font_size},
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38 fig_width: ${output_options.fig_width},
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39 figure_format:${output_options.figure_format},
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40 color_nan_elements:'gray',
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41 fig_height: ${output_options.fig_height}
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42 },
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43 columns_metabolites: {ID: metabolite, values: log2FC},
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44 columns_transcripts: {ID: ${deg_one_id}, values: ${deg_one_values}},
2
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45 compartment: ${compartments},
0
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46 label: metabologram
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47 }'
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48 '++analysis.method.qualityDistanceOverSpan='${qualityDistanceOverSpan}''
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49 '++analysis.dataset.label='
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50 '+analysis.timepoints=${timepoints}'
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51 '++analysis.method.statistical_test=${statistical_test}'
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52 '++analysis.method.grouping=${groups}'
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53 '++analysis.method.correction_method=${correction_method}'
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54 '++analysis.method.impute_values=${impute_values}'
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55 '++analysis.statistical_test=${statistical_test}'
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56 '++analysis.method.disfit_tail_option="auto"'
0
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57 '++analysis.dataset.subfolder='
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58 '++analysis.dataset.conditions=${$conditions}'
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59 '++analysis.dataset.pathways=${pathways}'
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60 '++analysis.dataset.transcripts=${transcripts}'
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61 '++analysis.comparisons=${comparisons}'
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62 #if $metadata_path:
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63 '++analysis.dataset.metadata=metadata'
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64 #end if
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65 #if str( $data_input.data_input_selector ) == "abundance":
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66 #if $data_input.abundance_file:
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67 '++analysis.dataset.abundances=abundance'
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68 #end if
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69 #else:
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70 #if $data_input.me_or_frac_contrib_file:
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71 '++analysis.dataset.mean_enrichment=me_or_frac_contrib'
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72 #end if
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73 #end if
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74 @REMOVE_CONFIG@
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75 ]]></command>
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76 <inputs>
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77 <expand macro="input_parameters_metabologram"/>
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78 <expand macro="deg_list"/>
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79 <expand macro="compartments_metabologram"/>
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80 <param name="correction_method" type="select" value="bonferroni" display="radio" label="Select multiple test correction to apply" help="Please enter at max 1 method">
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81 <option value="bonferroni">bonferroni</option>
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82 <option value="holm-sidak">holm-sidak</option>
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83 <option value="holm">holm</option>
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84 <option value="simes-hochberg">simes-hochberg</option>
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85 <option value="hommel">hommel</option>
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86 <option value="fdr_bh">fdr_bh</option>
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87 <option value="fdr_by">fdr_by</option>
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88 <option value="fdr_tsbh">fdr_tsbh</option>
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89 <option value="fdr_tsbky">fdr_tsbky</option>
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90 </param>
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91 <param name="qualityDistanceOverSpan" type="float" min="-1.0" max="-0.1" value="-0.3" label="quality Distance Over Span" help="Default value is -0.3."/>
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92 <section name="output_options" title="Output options">
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93 <param name="figure_format" type="select" value="pdf" display="radio" label="Select output figure format" help="Please enter at max 1 format">
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94 <option value="pdf">Pdf</option>
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95 <option value="svg">Svg</option>
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96 </param>
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97 <param name="fig_width" type="integer" min="5" max="20" value="7" label="width of figures"
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98 help="Default value is 7."/>
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99 <param name="fig_height" type="integer" min="5" max="20" value="7" label="heigt of each figure"
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100 help="Default value is 7."/>
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101 <param name="font_size" type="integer" min="1" max="20" value="12" label=" figure font size"
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102 help="Default value is 12."/>
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103 </section>
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104 </inputs>
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105
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106
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107 <outputs>
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108 <collection name="report" type="list">
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109 <discover_datasets pattern="__designation_and_ext__" directory="figures"/>
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110 </collection>
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111 </outputs>
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112 <tests>
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113 <test>
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114
0
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115 <param name="path_kegg_metabolites" ftype="tabular" value="pathways_kegg_metabolites.csv"/>
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116 <param name="path_kegg_transcripts" ftype="tabular" value="pathways_kegg_transcripts.csv"/>
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117 <param name="abundance_file" ftype="tabular" value="rawAbundances.csv"/>
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118 <param name="metadata_path" ftype="tabular" value="example2_metadata.csv"/>
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119 <param name="metabolites_list" value="Fumaric_acid,Glycine,L-Proline"/>
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120 <param name="statistical_test_type" value="parametric"/>
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121 <param name="stat_test" value="Tt"/>
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122 <repeat name="deg_list">
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123 <param name="input" ftype="tabular" value="DEG_comparison_1.csv"/>
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124 <param name="idcol" ftype="integer" value="2"/>
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125 <param name="valuecol" ftype="integer" value="3"/>
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126 <param name="timepoint" value='T0'/>
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127 <repeat name="factor_list">
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128 <param name="condition" value="Control"/>
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129 </repeat>
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130 <repeat name="factor_list">
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131 <param name="condition" value="L-Cycloserine"/>
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132 </repeat>
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133 </repeat>
0
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134 <section name="output_options">
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135 <param name="figure_format" value="svg"/>
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136 <param name="figure_width" value="7"/>
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137 <param name="figure_height" value="7"/>
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138 <param name="font_size" value="12"/>
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139 </section>
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140 <output_collection name="report" type="list" count="3">
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141 <element file="AMINOACIDS-Control-T0-L-Cycloserine-T0--DEG_comparison1-abundances-cell.svg" name="AMINOACIDS-Control-T0-L-Cycloserine-T0--DEG_comparison1-abundances-cell" ftype="svg" compare="sim_size" delta="100"/>
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142 <element file="CENTRAL_CARBON_METABOLISM-Control-T0-L-Cycloserine-T0--DEG_comparison1-abundances-cell.svg" name="CENTRAL_CARBON_METABOLISM-Control-T0-L-Cycloserine-T0--DEG_comparison1-abundances-cell" ftype="svg" compare="sim_size" delta="100"/>
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143 <element file="legend-abundances-cell.svg" name="legend-abundances-cell" ftype="svg" compare="sim_size" delta="100"/>
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144 </output_collection>
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145 </test>
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146 </tests>
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147 <help><![CDATA[
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148 This module is part of DIMet: Differential analysis of Isotope-labeled targeted Metabolomics data (https://pypi.org/project/DIMet/).
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149
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150 DIMet Metabologram integrates tracer metabolomics and transcriptomics, in a pathway based fashion.
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151 More precisely, the differential information (Fold Changes (log2 transformed, or not)) of both types of omics
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152 must be given as input, plus the files defining the pathways. You can use the minimal data examples from https://zenodo.org/records/10579891 that contain a minimal example data for running our DIMet Metabologram tool.
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153
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154 The figures in .pdf format are of publication quality, and as they are vectorial images you can open them and customize aesthetics with a professional image software such as Inkscape, Adobe Illustrator, Sketch, CorelDRAW, etc.
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155
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156
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157 **Input data files**
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158
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159 This tool requires the following tab-delimited .csv files:
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160
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161 1. **The metabolomics data**:
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162
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163 1.1 The measures' (or quantifications') files, that can be of two types:
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164
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165 - The total **abundances** (of the metabolites) file,
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166 OR,
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167
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168 - The mean **enrichment** or labelled fractional contributions
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169
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170 1.2 The metadata, a unique file with the description of the samples in your measures' files. This is compulsory, see section **Metadata File Information**.
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171
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172
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173 2. **The transcriptomics data**:
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174
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175 2.1 The table with the results of the differential expression analysis (performed with an external tool)
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176
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177
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178 3. **The pathways files**:
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179
2
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180
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181 3.1 A file with the pathways and respective gene symbols, which must match with those present in the transcriptomics data. The names of the columns must be the pathways' names, see the minimal data example downloaded from zenodo as explained above. Example:
0
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182
2
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183 ====================== ================== ==================
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184 **PENTHOSE PHOSPHATE** **FATTY ACIDS** ...
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185 ====================== ================== ==================
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186 RPE SCD ...
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187 PGM1 FADS1 ...
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188 PKF BAAT ...
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189 DERA ACOT2 ...
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190 ... ... ...
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191 ====================== ================== ==================
0
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192
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193
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194
2
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195 3.2 A file with the pathways and respective metabolites ID, that must match with those in your metabolomics data. The names of the columns must be the pathways' names, see the minimal data example downloaded from zenodo as explained above. Example:
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196
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197 ====================== ================== ==================
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198 **PENTHOSE PHOSPHATE** **FATTY ACIDS** ...
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199 ====================== ================== ==================
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200 Xyl_P Acetyl_CoA ...
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201 Glc_6P Palmitate ...
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202 Rib_6P Stearate ...
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203 ... ... ...
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204 ====================== ================== ==================
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205
0
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206 **Measures' files**
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207
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208 The measure's files must be organized as matrices:
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209 - The first column must contain Metabolite IDs that are unique (not repeated) within the file.
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210
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211 - The rest of the columns correspond to the samples
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212
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213 - The rows correspond to the metabolites
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214
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215 - The values must be tab separated, with the first row containing the sample/column labels.
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216
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217 See the following examples of measures files:
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218
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219
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220 Example - Metabolites **abundances**:
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221
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222 =============== ================== ================== ================== ================== ================== ==================
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223 ID **MCF001089_TD01** **MCF001089_TD02** **MCF001089_TD03** **MCF001089_TD04** **MCF001089_TD05** **MCF001089_TD06**
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224 =============== ================== ================== ================== ================== ================== ==================
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225 2_3-PG 8698823.9926 10718737.7217 10724373.9 8536484.5 22060650 28898956
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226 2-OHGLu 36924336 424336 92060650 45165 84951950 965165051
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227 Glc6P 2310 2142 2683 1683 012532068 1252172
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228 Gly3P 399298 991656565 525195 6365231 89451625 4952651963
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229 IsoCit 0 0 0 84915613 856236 954651610
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230 =============== ================== ================== ================== ================== ================== ==================
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231
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232 Example - mean **enrichment** or labeled fractional contributions:
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233
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234 =============== ================== ================== ================== ================== ================== ==================
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235 ID **MCF001089_TD01** **MCF001089_TD02** **MCF001089_TD03** **MCF001089_TD04** **MCF001089_TD05** **MCF001089_TD06**
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236 =============== ================== ================== ================== ================== ================== ==================
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237 2_3-PG 0.9711 0.968 0.9909 0.991 0.40 0.9
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238 2-OHGLu 0.01719 0.0246 0.554 0.555 0.73 0.68
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239 Glc6P 0.06 0.66 2683 0.06 2068 2172
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240 Gly3P 0.06 0.06 0.06 1 5 3
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241 IsoCit 0.06 1 0.49 0.36 6 10
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242 =============== ================== ================== ================== ================== ================== ==================
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243
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244
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245
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246
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247
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248 **Metadata File Information**
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249
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250 Provide a tab-separated file that has the names of the samples in the first column and one header row.
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251 Column names must be exactly in this order:
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252
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253 name_to_plot
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254 condition
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255 timepoint
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256 timenum
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257 compartment
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258 original_name
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259
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260
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261 Example **Metadata File**:
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262
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263
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264 ==================== =============== ============= ============ ================ =================
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265 **name_to_plot** **condition** **timepoint** **timenum** **compartment** **original_name**
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266 -------------------- --------------- ------------- ------------ ---------------- -----------------
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267 Control_cell_T0-1 Control T0 0 cell MCF001089_TD01
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268 Control_cell_T0-2 Control T0 0 cell MCF001089_TD02
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269 Control_cell_T0-3 Control T0 0 cell MCF001089_TD03
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270 Tumoral_cell_T0-1 Tumoral T0 0 cell MCF001089_TD04
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271 Tumoral_cell_T0-2 Tumoral T0 0 cell MCF001089_TD05
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272 Tumoral_cell_T0-3 Tumoral T0 0 cell MCF001089_TD06
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273 Tumoral_cell_T24-1 Tumoral T24 24 cell MCF001089_TD07
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274 Tumoral_cell_T24-2 Tumoral T24 24 cell MCF001089_TD08
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275 Tumoral_cell_T24-3 Tumoral T24 24 cell MCF001090_TD01
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276 Control_med_T24-1 Control T24 24 med MCF001090_TD02
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277 Control_med_T24-2 Control T24 24 med MCF001090_TD03
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278 Tumoral_med_T24-1 Tumoral T24 24 med MCF001090_TD04
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279 Tumoral_med_T24-2 Tumoral T24 24 med MCF001090_TD05
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280 Control_med_T0-1 Control T0 0 med MCF001090_TD06
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281 Tumoral_med_T0-1 Tumoral T0 0 med MCF001090_TD07
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282 Tumoral_med_T0-2 Tumoral T0 0 med MCF001090_TD08
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283 ==================== =============== ============= ============ ================ =================
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284
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285
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286 The column **original_name** must have the names of the samples as given in your data.
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287
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288 The column **name_to_plot** must have the names as you want them to be (or set identical to original_name if you prefer). To set names that
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289 are meaningful is a better choice, as we will take them to display the results.
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290
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291 The column **timenum** must contain only the numeric part of the timepoint, for example 2,0, 10, 100 (this means, without letters ("T", "t", "s", "h" etc)
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292 nor any other symbol). Make sure these time numbers are in the same units (but do not write the units here!).
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293
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294 The column **compartment** is an abbreviation, coined by you, for the compartments. This will be used for the results' files names: the longer the
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295 compartments names are, the longer the output files' names! Please pick short and clear abbreviations to fill this column.
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296
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297
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298 **Running the analysis**
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299
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300 You can precise how you want your analysis to be executed, with the parameters:
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301
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302
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303 a. Parameters proper to the metabolomics analysis (that runs automatically before the integration):
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304
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305 - **comparisons** : the pairs of [condition, timepoint] groups to compare
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306
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307 - **datatypes** : the measures type that you want to run, that must be only one (see above in **Input data files** section)
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308
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309 - **statistical_test** : choose the specific statistical test to be applied.
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310
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311 Kruskal-Wallis, Mann-Whitney, Wilcoxon’s signed rank test, Wilcoxon’s rank sum test
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312 t-test, and permutation test are currently offered (we use the trusted functions from scipy library https://docs.scipy.org/doc/scipy/reference/stats.html).
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313
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314 For the permutation test, we have established as test statistic, the absolute difference of geometric means of the two compared groups.
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315
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316 - **qualityDistanceOverSpan**: a normalized distance between the intervals of values of the compared groups, that is the cutoff for
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317
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318 considering a minimal acceptable "separation", and therefore, to be suitable for statistical testing. A 'distance/span' == 1 is a perfect separation,
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319 whereas if 'distance/span' < 0 there is no separation.
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320 To use with caution in case of important dispersion of your intra-group values. Default is -0.3 (not stringent)
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321
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322 - **correction_method** : one of the methods for multiple testing correction available in statsmodels library (bonferroni, fdr_bh, sidak, among others, see https://www.statsmodels.org/dev/generated/statsmodels.stats.multitest.multipletests.html).
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323
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324 - **compartment** : one of the compartments present in your data.
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325
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326 b. Parameters proper to the integration (that runs automatically after the metabolites analysis):
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327
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328
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329 - **transcripts** : the file(s) with the results of the differential expression analysis. They must be as many as the number of comparisons (metabolomics analysis) and keep a coherent order with them.
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330
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331 - **pathways** : files for the pathways, as explained in **Input data files** section
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332
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333
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334 There exist hints on use that will guide you, next to the parameters.
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335
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336 The output consists of one figure by metabologram, and a color key bar legend valid for all metabolograms produced
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337
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338 **Available data for testing**
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339
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340 You can test our tool with the data from our manuscript https://zenodo.org/record/10579862 (the pertinent
0
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341 files for you are located in the subfolders inside the data folder).
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342 You can also use the minimal data examples from https://zenodo.org/record/10579891
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343
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344 ]]>
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345 </help>
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346 <expand macro="citations" />
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347 </tool>