Mercurial > repos > iuc > dimet_bivariate_analysis

annotate dimet_bivariate_analysis.xml @ 0:89e637c99bed draft

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planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/DIMet commit 30fe10acdf65c6917856a0eae21dc91abd2f609f
author iuc
date Thu, 15 Feb 2024 12:53:48 +0000
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89e637c99bed planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/DIMet commit 30fe10acdf65c6917856a0eae21dc91abd2f609f
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1 <tool id="dimet_@EXECUTABLE@" name="dimet @TOOL_LABEL@" version="@TOOL_VERSION@+galaxy@VERSION_SUFFIX@" profile="20.05">
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2 <description>
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3 Computation of the correlation of MDV profiles, or the metabolite time course profiles (by DIMet)
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4 </description>
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5 <macros>
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6 <token name="@TOOL_LABEL@">bivariate analysis</token>
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7 <token name="@EXECUTABLE@">bivariate_analysis</token>
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8 <import>macros.xml</import>
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9 </macros>
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10 <expand macro="requirements"/>
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11 <command detect_errors="exit_code"><![CDATA[
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12 @INIT_CONFIG@
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13 @INIT_BI_ANALYSIS@
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14 @INIT_BIVAR_COMPARISONS@
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15 HYDRA_FULL_ERROR=1 python -m dimet
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16 -cp '$__new_file_path__/config'
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17 '++hydra.run.dir=bivariate_analysis'
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18 '++figure_path=figures'
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19 '++table_path=tables'
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20 '++analysis={
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21 dataset:{
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22 _target_: dimet.data.DatasetConfig,
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23 name: "I am a synthetic data example"
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24 },
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25 method:{
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26 _target_: dimet.method.BivariateAnalysisConfig,
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27 label: "bivariate analysis",
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28 name: "Computation of the correlation of MDV profiles, or the metabolite time course profiles"
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29 },
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30 label: bivariate-analysis-example2
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31 }'
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32 '++analysis.conditions=${conditions}'
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33 '++analysis.dataset.label='
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34 '++analysis.method.correction_method=${correction_method}'
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35 '++analysis.method.impute_values=${impute_values}'
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36 '++analysis.method.conditions_metabolite_time_profiles=${conditions_metabolite_time_profiles}'
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37 '++analysis.method.timepoints_MDV_comparison=${timepoints_MDV_comparison}'
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38 '++analysis.method.conditions_MDV_comparison=${conditions_MDV_comparison}'
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39 '++analysis.method.output_include_gmean_arr_columns=true'
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40 '++analysis.dataset.subfolder='
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41 '++analysis.dataset.conditions=${conditions}'
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42 #if $metadata_path:
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43 '++analysis.dataset.metadata=metadata'
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44 #end if
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45 #if str( $data_input.data_input_selector ) == "abundance":
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46 #if $data_input.abundance_file:
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47 '++analysis.dataset.abundances=abundance'
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48 #end if
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49 #elif str( $data_input.data_input_selector ) == "mean_enrichment":
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50 #if $data_input.me_or_frac_contrib_file:
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51 '++analysis.dataset.mean_enrichment=me_or_frac_contrib'
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52 #end if
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53 #elif str( $data_input.data_input_selector ) == "isotop_prop":
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54 #if $data_input.isotop_prop_file:
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55 '++analysis.dataset.isotopologue_proportions=isotop_prop'
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56 #end if
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57 #else
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58 #if $data_input.isotop_abs_file:
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59 '++analysis.dataset.isotopologues=isotop_abs'
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60 #end if
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61 #end if
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62 @REMOVE_CONFIG@
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63 ]]></command>
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64 <inputs>
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65 <expand macro="input_parameters_bivar_analysis"/>
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66 <expand macro="plot_factor_list"/>
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67 <expand macro="correction_method"/>
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68 </inputs>
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69
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70 <outputs>
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71 <collection name="report" type="list">
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72 <discover_datasets pattern="__designation__" directory="tables" format="tabular"/>
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73 </collection>
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74 </outputs>
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75 <tests>
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76 <test>
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77 <param name="data_input_selector" value="isotop_prop" />
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78 <param name="isotop_prop_file" ftype="tabular" value="CorrectedIsotopologues_5.csv"/>
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79 <param name="metadata_path" ftype="tabular" value="example5_metadata.csv"/>
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80 <param name="correction_method" value="fdr_bh"/>
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81 <repeat name="plot_factor_list">
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82 <param name="condition" value="Control"/>
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83 </repeat>
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84 <repeat name="plot_factor_list">
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85 <param name="condition" value="L-Cycloserine"/>
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86 </repeat>
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87 <output_collection name="report" type="list" count="8">
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88 <element file="isotop_prop--cell--MDV-Control-L-Cycloserine--T0-pearson.tsv" name="isotop_prop--cell--MDV-Control-L-Cycloserine--T0-pearson.tsv" ftype="tabular"/>
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89 <element file="isotop_prop--cell--MDV-Control-L-Cycloserine--T2h-pearson.tsv" name="isotop_prop--cell--MDV-Control-L-Cycloserine--T2h-pearson.tsv" ftype="tabular"/>
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90 <element file="isotop_prop--cell--MDV-T2h-T0--Control-pearson.tsv" name="isotop_prop--cell--MDV-T2h-T0--Control-pearson.tsv" ftype="tabular"/>
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91 <element file="isotop_prop--cell--MDV-T2h-T0--L-Cycloserine-pearson.tsv" name="isotop_prop--cell--MDV-T2h-T0--L-Cycloserine-pearson.tsv" ftype="tabular"/>
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92 <element file="isotop_prop--med--MDV-Control-L-Cycloserine--T0-pearson.tsv" name="isotop_prop--med--MDV-Control-L-Cycloserine--T0-pearson.tsv" ftype="tabular"/>
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93 <element file="isotop_prop--med--MDV-Control-L-Cycloserine--T2h-pearson.tsv" name="isotop_prop--med--MDV-Control-L-Cycloserine--T2h-pearson.tsv" ftype="tabular"/>
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94 <element file="isotop_prop--med--MDV-T2h-T0--Control-pearson.tsv" name="isotop_prop--med--MDV-T2h-T0--Control-pearson.tsv" ftype="tabular"/>
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95 <element file="isotop_prop--med--MDV-T2h-T0--L-Cycloserine-pearson.tsv" name="isotop_prop--med--MDV-T2h-T0--L-Cycloserine-pearson.tsv" ftype="tabular"/>
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96 </output_collection>
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97 </test>
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98 </tests>
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99 <help><![CDATA[
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100
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101 This module is part of DIMet: Computation of the correlation of entire MDV profiles, or the metabolite time course profiles (https://pypi.org/project/DIMet/).
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102
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103 DIMet bi-variate analysis performs the comparison of entire MDV profiles, with the user provided isotopologue proportions data. Moreover, when total abundances and/or mean enrichment are provided, the comparison of the metabolite time-course profiles is also computed. Specifically, three types of bi-variate comparisons are performed automatically:
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104
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105
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106 - MDV profile comparison between two conditions
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107
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108 - MDV profile comparison between two consecutive time-points
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109
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110 - Metabolite (total abundances and/or mean enrichment) time course profiles comparison between two conditions
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111
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112
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113 For all these three types of bi-variate comparison, the statistical test that is applied is the Pearson's correlation test.
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114
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115
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116 To note, MDV (Mass Distribution Vector) are obtained automatically by the tool, using the isotopologue proportions.
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117
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118
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119
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120 **Input data files**
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121
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122 This tool requires (at max.) 4 tab-delimited .csv files as inputs. There are two types of files:
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123
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124 - The measures' (or quantifications') files, that can be of 3 types.
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125
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126 - The metadata, a unique file with the description of the samples in your measures' files. This is compulsory.
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127
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128 For running DIMet @EXECUTABLE@ you need **at least one file** of measures:
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129
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130 - The **isotopologue proportions** file
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131
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132 - The total **abundances** (of the metabolites) file
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133
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134 - The mean **enrichment** or labelled fractional contributions
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135
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136
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137 and one metadata file, WHICH IS COMPULSORY, see section **Metadata File Information**.
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138
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139
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140 **Measures files**
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141
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142 The measures files must be organized as matrices:
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143
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144 - The first column must contain Metabolite IDs that are unique (not repeated) within the file.
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145
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146 - The rest of the columns correspond to the samples
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147
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148 - The rows correspond to the metabolites
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149
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150 - The values must be tab separated, with the first row containing the sample/column labels.
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151
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152 See the following examples of measures files:
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153
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154
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155 Example - Metabolites **abundances**:
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156
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157 =============== ================== ================== ================== ================== ================== ==================
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158 ID **MCF001089_TD01** **MCF001089_TD02** **MCF001089_TD03** **MCF001089_TD04** **MCF001089_TD05** **MCF001089_TD06**
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159 =============== ================== ================== ================== ================== ================== ==================
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160 2_3-PG 8698823.9926 10718737.7217 10724373.9 8536484.5 22060650 28898956
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161 2-OHGLu 36924336 424336 92060650 45165 84951950 965165051
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162 Glc6P 2310 2142 2683 1683 012532068 1252172
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163 Gly3P 399298 991656565 525195 6365231 89451625 4952651963
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164 IsoCit 0 0 0 84915613 856236 954651610
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165 =============== ================== ================== ================== ================== ================== ==================
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166
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167 Example - mean **enrichment** or labeled fractional contributions:
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168
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169 =============== ================== ================== ================== ================== ================== ==================
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170 ID **MCF001089_TD01** **MCF001089_TD02** **MCF001089_TD03** **MCF001089_TD04** **MCF001089_TD05** **MCF001089_TD06**
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171 =============== ================== ================== ================== ================== ================== ==================
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172 2_3-PG 0.9711 0.968 0.9909 0.991 0.40 0.9
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173 2-OHGLu 0.01719 0.0246 0.554 0.555 0.73 0.68
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174 Glc6P 0.06 0.66 2683 0.06 2068 2172
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175 Gly3P 0.06 0.06 0.06 1 5 3
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176 IsoCit 0.06 1 0.49 0.36 6 10
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177 =============== ================== ================== ================== ================== ================== ==================
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178
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179
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180
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181 Example - **Isotopologue proportions**:
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182
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183 =============== ================== ================== ================== ================== ================== ==================
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184 ID **MCF001089_TD01** **MCF001089_TD02** **MCF001089_TD03** **MCF001089_TD04** **MCF001089_TD05** **MCF001089_TD06**
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185 =============== ================== ================== ================== ================== ================== ==================
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186 2_3-PG_m+0 0.023701408 0.026667837 0.003395407 0.05955 0.034383527 0.12
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187 2_3-PG_m+1 0.0 0.0 0.0 0.0 0.4 0.12
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188 2_3-PG_m+2 0.015379329 0.01506 0.017029723 0.35483229 0.54131313 0.743
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189 2_3-PG_m+3 0.960919263 0.958268099 0.97957487 0.581310816 0.017029723 0.017
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190 2-OHGLu_m+0 0.972778716 0.960016157 0.238843937 0.234383527 0.9998888 0.015064063
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191 2-OHGLu_m+1 0.0 0.0 0.0 0.0 0.0001112 0.960919263
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192 =============== ================== ================== ================== ================== ================== ==================
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193
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194
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195
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196 **Metadata File Information**
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197
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198 Provide a tab-separated file that has the names of the samples in the first column and one header row.
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199 Column names must be exactly in this order:
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200
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201 name_to_plot
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202 condition
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203 timepoint
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204 timenum
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205 compartment
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206 original_name
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207
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208
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209 Example **Metadata File**:
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210
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211
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212 ==================== =============== ============= ============ ================ =================
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213 **name_to_plot** **condition** **timepoint** **timenum** **compartment** **original_name**
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214 -------------------- --------------- ------------- ------------ ---------------- -----------------
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215 Control_cell_T0-1 Control T0 0 cell MCF001089_TD01
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216 Control_cell_T0-2 Control T0 0 cell MCF001089_TD02
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217 Control_cell_T0-3 Control T0 0 cell MCF001089_TD03
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218 Tumoral_cell_T0-1 Tumoral T0 0 cell MCF001089_TD04
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219 Tumoral_cell_T0-2 Tumoral T0 0 cell MCF001089_TD05
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220 Tumoral_cell_T0-3 Tumoral T0 0 cell MCF001089_TD06
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221 Tumoral_cell_T24-1 Tumoral T24 24 cell MCF001089_TD07
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222 Tumoral_cell_T24-2 Tumoral T24 24 cell MCF001089_TD08
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223 Tumoral_cell_T24-3 Tumoral T24 24 cell MCF001090_TD01
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224 Control_med_T24-1 Control T24 24 med MCF001090_TD02
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225 Control_med_T24-2 Control T24 24 med MCF001090_TD03
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226 Tumoral_med_T24-1 Tumoral T24 24 med MCF001090_TD04
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227 Tumoral_med_T24-2 Tumoral T24 24 med MCF001090_TD05
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228 Control_med_T0-1 Control T0 0 med MCF001090_TD06
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229 Tumoral_med_T0-1 Tumoral T0 0 med MCF001090_TD07
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230 Tumoral_med_T0-2 Tumoral T0 0 med MCF001090_TD08
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231 ==================== =============== ============= ============ ================ =================
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232
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233
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234 The column **original_name** must have the names of the samples as given in your data.
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235
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236 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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237 are meaningful is a better choice, as we will take them to display the results.
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238
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239 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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240 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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241
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242 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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243 compartments names are, the longer the output files' names! Please pick short and clear abbreviations to fill this column.
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244
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245
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246 **Running the analysis**
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247
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248 You can precise how you want your analysis to be executed, with the parameters:
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249
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250 - **datatypes** : the measures type(s) that you want to run.
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251
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252 - **conditions**: the two conditions to be compared in the bi-variate analysis. If 3 or more conditions are set, the tool automatically performs all the 1-to-1 condition comparisons. If you only have one condition in your data, select the condition, and see the Note at the end of this section.
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253
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254 - **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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255
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256
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257 **Note**: the tool automatically performs the comparison of *MDV profile between two consecutive time-points*. All the time-points are processed.
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258
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259
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260 There exist hints on use that will guide you, next to the parameters.
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261
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262 For more information about the implemented statistical tests, please visit: https://github.com/cbib/DIMet/wiki/2-Statistical-tests
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263
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264 The output files are explained in https://github.com/cbib/DIMet/wiki/3-Output
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265
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266 **Available data for testing**
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267
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268 You can test our tool with the data from our manuscript https://zenodo.org/records/10579862 (the pertinent
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269 files for you are located in the subfolders inside the data folder).
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270 You can also use the minimal data examples from https://zenodo.org/records/10579891
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271
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272 ]]>
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273 </help>
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274 <expand macro="citations" />
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275 </tool>