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comparison dimet_pca_analysis.xml @ 0:04d213632103 draft

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planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/DIMet commit abca848510cb4ac8d09d95634147626ea578cdf0
author iuc
date Tue, 10 Oct 2023 11:56:02 +0000
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1 <tool id="dimet_@EXECUTABLE@" name="dimet @TOOL_LABEL@" version="@TOOL_VERSION@+galaxy@VERSION_SUFFIX@" profile="20.05">
2 <description>
3 Principal Component Analysis for tracer metabolomics data, producing tables (by DIMet)
4 </description>
5 <macros>
6 <token name="@TOOL_LABEL@">pca analysis</token>
7 <token name="@EXECUTABLE@">pca_analysis</token>
8 <import>macros.xml</import>
9 </macros>
10 <expand macro="requirements"/>
11 <command detect_errors="exit_code"><![CDATA[
12 @INIT_CONFIG@
13 @INIT_PCA@
14 @INIT_IMPUTE_VALUES@
15 @INIT_CONDITIONS@
16 HYDRA_FULL_ERROR=1 python -m dimet
17 -cp '$__new_file_path__/config'
18 '++hydra.run.dir=pca-analysis-tables'
19 '++figure_path=figures'
20 '++table_path=tables'
21 '++analysis={
22 dataset:{
23 _target_:dimet.data.DatasetConfig,
24 name: "Galaxy DIMet run"
25 },
26 method:{
27 _target_: dimet.method.PcaAnalysisConfig,
28 label: pca-analysis-tables,
29 name: "Generate Principal Component Analysis tables",
30 pca_split_further:['timepoint'],
31 draw_ellipses: null,
32 run_iris_demo: false,
33 impute_values:${impute_values}
34 },
35 label: pca-table
36 }'
37 '++analysis.dataset.subfolder='
38 '++analysis.dataset.label='
39 '++analysis.dataset.conditions=${conds}'
40 #if $metadata_path:
41 '++analysis.dataset.metadata=metadata'
42 #end if
43 #if $abundance_file:
44 '++analysis.dataset.abundances=abundance'
45 #end if
46 #if $me_or_frac_contrib_file:
47 '++analysis.dataset.mean_enrichment=me_or_frac_contrib'
48 #end if
49 @REMOVE_CONFIG@
50 ]]></command>
51 <inputs>
52 <expand macro="input_parameters_pca"/>
53 <expand macro="conditions"/>
54 </inputs>
55
56 <outputs>
57 <collection name="report" type="list">
58 <discover_datasets pattern="__designation__" directory="tables" format="tabular"/>
59 </collection>
60 </outputs>
61 <tests>
62 <test>
63 <param name="abundance_file" ftype="tabular" value="rawAbundances.csv" />
64 <param name="metadata_path" ftype="tabular" value="example2_metadata.csv"/>
65 <param name="conditions" value='Control,L-Cycloserine'/>
66 <output_collection name="report" type="list" count="12">
67 <element file="abundances--cell_pc.csv" name="abundances--cell_pc.csv" ftype="tabular"/>
68 <element file="abundances--cell_var.csv" name="abundances--cell_var.csv" ftype="tabular"/>
69 <element file="abundances--med_pc.csv" name="abundances--med_pc.csv" ftype="tabular"/>
70 <element file="abundances--med_var.csv" name="abundances--med_var.csv" ftype="tabular"/>
71 <element file="abundances--T0--cell_pc.csv" name="abundances--T0--cell_pc.csv" ftype="tabular"/>
72 <element file="abundances--T0--cell_var.csv" name="abundances--T0--cell_var.csv" ftype="tabular"/>
73 <element file="abundances--T0--med_pc.csv" name="abundances--T0--med_pc.csv" ftype="tabular"/>
74 <element file="abundances--T0--med_var.csv" name="abundances--T0--med_var.csv" ftype="tabular"/>
75 <element file="abundances--T2h--cell_pc.csv" name="abundances--T2h--cell_pc.csv" ftype="tabular"/>
76 <element file="abundances--T2h--cell_var.csv" name="abundances--T2h--cell_var.csv" ftype="tabular"/>
77 <element file="abundances--T2h--med_pc.csv" name="abundances--T2h--med_pc.csv" ftype="tabular"/>
78 <element file="abundances--T2h--med_var.csv" name="abundances--T2h--med_var.csv" ftype="tabular"/>
79 </output_collection>
80 </test>
81 </tests>
82 <help><![CDATA[
83 This module is part of DIMet: Differential analysis of Isotope-labeled targeted Metabolomics data (https://pypi.org/project/DIMet/).
84
85 This tool performs the Principal Components Analysis (PCA) on your data,
86 generating the tab-delimited .csv files with the results of the PCA, it is, all the principal components or "dimensions" eigenvalues, and the percentage of explained variances across all the principal components detected in your data.
87
88 For automatic plotting of a PCA analysis use our tool **DIMet pca plot**
89
90 **Input data files**
91
92 This tool requires (at max.) 3 tab-delimited .csv files as inputs. There are two types of files:
93
94 - The measures' (or quantifications') files, that can be of 4 types.
95
96 - The metadata, a unique file with the description of the samples in your measures' files. This is compulsory.
97
98 For running DIMet @EXECUTABLE@ you need **at least one file** of measures:
99
100 - The total **abundances** (of the metabolites) file
101
102 - The mean **enrichment** or labelled fractional contributions
103
104
105 and one metadata file, WHICH IS COMPULSORY, see section **Metadata File Information**.
106
107
108 **Measures' files**
109
110 The measure's files must be organized as matrices:
111
112 - The first column must contain Metabolite IDs that are unique (not repeated) within the file.
113
114 - The rest of the columns correspond to the samples
115
116 - The rows correspond to the metabolites
117
118 - The values must be tab separated, with the first row containing the sample/column labels.
119
120 See the following examples of measures files:
121
122
123 Example - Metabolites **abundances**:
124
125 =============== ================== ================== ================== ================== ================== ==================
126 ID **MCF001089_TD01** **MCF001089_TD02** **MCF001089_TD03** **MCF001089_TD04** **MCF001089_TD05** **MCF001089_TD06**
127 =============== ================== ================== ================== ================== ================== ==================
128 2_3-PG 8698823.9926 10718737.7217 10724373.9 8536484.5 22060650 28898956
129 2-OHGLu 36924336 424336 92060650 45165 84951950 965165051
130 Glc6P 2310 2142 2683 1683 012532068 1252172
131 Gly3P 399298 991656565 525195 6365231 89451625 4952651963
132 IsoCit 0 0 0 84915613 856236 954651610
133 =============== ================== ================== ================== ================== ================== ==================
134
135 Example - mean **enrichment** or labeled fractional contributions:
136
137 =============== ================== ================== ================== ================== ================== ==================
138 ID **MCF001089_TD01** **MCF001089_TD02** **MCF001089_TD03** **MCF001089_TD04** **MCF001089_TD05** **MCF001089_TD06**
139 =============== ================== ================== ================== ================== ================== ==================
140 2_3-PG 0.9711 0.968 0.9909 0.991 0.40 0.9
141 2-OHGLu 0.01719 0.0246 0.554 0.555 0.73 0.68
142 Glc6P 0.06 0.66 2683 0.06 2068 2172
143 Gly3P 0.06 0.06 0.06 1 5 3
144 IsoCit 0.06 1 0.49 0.36 6 10
145 =============== ================== ================== ================== ================== ================== ==================
146
147
148 **Metadata File Information**
149
150 Provide a tab-separated file that has the names of the samples in the first column and one header row.
151 Column names must be exactly in this order:
152
153 name_to_plot
154 condition
155 timepoint
156 timenum
157 compartment
158 original_name
159
160
161 Example **Metadata File**:
162
163
164 ==================== =============== ============= ============ ================ =================
165 **name_to_plot** **condition** **timepoint** **timenum** **compartment** **original_name**
166 -------------------- --------------- ------------- ------------ ---------------- -----------------
167 Control_cell_T0-1 Control T0 0 cell MCF001089_TD01
168 Control_cell_T0-2 Control T0 0 cell MCF001089_TD02
169 Control_cell_T0-3 Control T0 0 cell MCF001089_TD03
170 Tumoral_cell_T0-1 Tumoral T0 0 cell MCF001089_TD04
171 Tumoral_cell_T0-2 Tumoral T0 0 cell MCF001089_TD05
172 Tumoral_cell_T0-3 Tumoral T0 0 cell MCF001089_TD06
173 Tumoral_cell_T24-1 Tumoral T24 24 cell MCF001089_TD07
174 Tumoral_cell_T24-2 Tumoral T24 24 cell MCF001089_TD08
175 Tumoral_cell_T24-3 Tumoral T24 24 cell MCF001090_TD01
176 Control_med_T24-1 Control T24 24 med MCF001090_TD02
177 Control_med_T24-2 Control T24 24 med MCF001090_TD03
178 Tumoral_med_T24-1 Tumoral T24 24 med MCF001090_TD04
179 Tumoral_med_T24-2 Tumoral T24 24 med MCF001090_TD05
180 Control_med_T0-1 Control T0 0 med MCF001090_TD06
181 Tumoral_med_T0-1 Tumoral T0 0 med MCF001090_TD07
182 Tumoral_med_T0-2 Tumoral T0 0 med MCF001090_TD08
183 ==================== =============== ============= ============ ================ =================
184
185
186 The column **original_name** must have the names of the samples as given in your data.
187
188 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
189 are meaningful is a better choice, as we will take them to display the results.
190
191 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)
192 nor any other symbol). Make sure these time numbers are in the same units (but do not write the units here!).
193
194 The column **compartment** is an abbreviation, coined by you, for the compartments. This will be used for the results' files names: the longer the
195 compartments names are, the longer the output files' names! Please pick short and clear abbreviations to fill this column.
196
197
198 **Running the analysis**
199
200 You can precise how you want your analysis to be executed, there exist hints on use that will guide you, next to the parameters.
201
202 Our tool automatically analyzes the integrality of your data (one global PCA analysis), and also splits your data by timepoint to generate PCA results by timepoint (which is convenient to explore the "grouping" of conditions), but if you only have one condition you can discard them.
203
204 The output consists of two .csv files for each performed PCA analysis (one file with the Principal Components (PC), one file with the variances).
205
206 **Available data for testing**
207
208 You can test our tool with the data from our manuscript https://zenodo.org/record/8378887 (the pertinent
209 files for you are located in the subfolders inside the data folder).
210 You can also use the minimal data examples from https://zenodo.org/record/8380706
211
212 ]]>
213 </help>
214 <expand macro="citations" />
215 </tool>