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comparison w4mcorcov.xml @ 2:e03582f26617 draft

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planemo upload for repository https://github.com/HegemanLab/w4mcorcov_galaxy_wrapper/tree/master commit 7682e8e7ae2bfb926d94b414b9a1649389f33582
author eschen42
date Sun, 12 Nov 2017 19:45:36 -0500
parents 0c2ad44b6c9c
children 5aaab36bc523
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1:0c2ad44b6c9c 2:e03582f26617
1 <tool id="w4mcorcov" name="OPLS-DA_Contrasts" version="0.98.3"> 1 <tool id="w4mcorcov" name="OPLS-DA_Contrasts" version="0.98.5">
2 2
3 <description>OPLS-DA Contrasts of Univariate Results</description> 3 <description>OPLS-DA Contrasts of Univariate Results</description>
4 4
5 <requirements> 5 <requirements>
6 <requirement type="package">r-batch</requirement> 6 <requirement type="package">r-batch</requirement>
21 facC "$facC" 21 facC "$facC"
22 pairSigFeatOnly "$pairSigFeatOnly" 22 pairSigFeatOnly "$pairSigFeatOnly"
23 levCSV '$levCSV' 23 levCSV '$levCSV'
24 matchingC '$matchingC' 24 matchingC '$matchingC'
25 labelFeatures '$labelFeatures' 25 labelFeatures '$labelFeatures'
26 labelOrthoFeatures '$labelOrthoFeatures'
26 contrast_detail '$contrast_detail' 27 contrast_detail '$contrast_detail'
27 contrast_corcov '$contrast_corcov' 28 contrast_corcov '$contrast_corcov'
28 contrast_salience '$contrast_salience' 29 contrast_salience '$contrast_salience'
29 ]]></command> 30 ]]></command>
30 31
31 <inputs> 32 <inputs>
32 <param name="dataMatrix_in" label="Data matrix file" type="data" format="tabular" help="Features x samples (tabular data - decimal: '.'; missing: NA; mode: numerical; separator: tab character)" /> 33 <param name="dataMatrix_in" label="Data matrix file" type="data" format="tabular" help="Features x samples (tabular data - decimal: '.'; missing: NA; mode: numerical; separator: tab character)" />
33 <param name="sampleMetadata_in" label="Sample metadata file" type="data" format="tabular" help="Samples x metadata (tabular data - decimal: '.'; missing: NA; mode: character or numerical; separator: tab character)" /> 34 <param name="sampleMetadata_in" label="Sample metadata file" type="data" format="tabular" help="Samples x metadata (tabular data - decimal: '.'; missing: NA; mode: character or numerical; separator: tab character)" />
34 <param name="variableMetadata_in" label="Variable metadata file (ideally from Univariate)" type="data" format="tabular" help="Features x metadata (tabular data - decimal: '.'; missing: NA; mode: character or numerical; separator: tab character)" /> 35 <param name="variableMetadata_in" label="Variable metadata file (ideally from Univariate)" type="data" format="tabular" help="Features x metadata (tabular data - decimal: '.'; missing: NA; mode: character or numerical; separator: tab character)" />
35 <param name="facC" label="Factor of interest" type="text" help="REQUIRED - The name of the column of sampleMetadata corresponding to the qualitative variable used to define the contrasts. Except when the 'Univariate Significance-test' is set to 'none', this also must be a portion of the column names in the variableMetadata file."/> 36 <param name="facC" label="Factor of interest" type="text" help="REQUIRED - The name of the column of sampleMetadata corresponding to the qualitative variable used to define the contrasts. Except when the 'Univariate Significance-test' is set to 'none', this also must be a portion of the column names in the variableMetadata file."/>
36 <param name="tesC" label="Univariate Significance-Test" type="select" help="Either 'none' or the name of the statistical test that was run by the 'Univariate' tool to produce the variableMetadata file; that name must also be a portion of the column names in that file."> 37 <param name="tesC" label="Univariate significance-test" type="select" help="Either 'none' or the name of the statistical test that was run by the 'Univariate' tool to produce the variableMetadata file; that name must also be a portion of the column names in that file.">
37 <option value="none">none - Display all features from variableMetadata (rather than choosing a subset based on significance in univariate testing)</option> 38 <option value="none">none - Display all features from variableMetadata (rather than choosing a subset based on significance in univariate testing)</option>
38 <option value="ttest">ttest - Student's t-test (parametric test, qualitative factor with exactly 2 levels)</option> 39 <option value="ttest">ttest - Student's t-test (parametric test, qualitative factor with exactly 2 levels)</option>
39 <option value="anova">anova - Analysis of variance (parametric test, qualitative factor with more than 2 levels)</option> 40 <option value="anova">anova - Analysis of variance (parametric test, qualitative factor with more than 2 levels)</option>
40 <option value="wilcoxon">wilcoxon - Wilcoxon rank test (nonparametric test, qualitative factor with exactly 2 levels)</option> 41 <option value="wilcoxon">wilcoxon - Wilcoxon rank test (nonparametric test, qualitative factor with exactly 2 levels)</option>
41 <option value="kruskal">kruskal - Kruskal-Wallis rank test (nonparametric test, qualitative factor with more than 2 levels)</option> 42 <option value="kruskal">kruskal - Kruskal-Wallis rank test (nonparametric test, qualitative factor with more than 2 levels)</option>
45 type="boolean" 46 type="boolean"
46 checked="true" 47 checked="true"
47 truevalue="TRUE" 48 truevalue="TRUE"
48 falsevalue="FALSE" 49 falsevalue="FALSE"
49 label="Retain only pairwise-significant features" 50 label="Retain only pairwise-significant features"
50 help="When 'none' is chosen, all features are included in the analysis. Otherwise, when this option is set to 'Yes', analysis will be performed including only features that differ significantly for the pair of levels being contrasted; when set to 'No', any feature that varies significantly across all levels will be included (i.e., exclude any feature that is not significantly different across all levels). See examples below."/> 51 help="When 'none' is chosen as the test, all features are included in the analysis (i. e., this parameter is ignored). Otherwise, when this option is set to 'Yes', analysis will be performed including only features that differ significantly for the pair of levels being contrasted; when set to 'No', any feature that varies significantly across all levels will be included (i.e., exclude any feature that is not significantly different across all levels). See examples below."/>
51 <param name="levCSV" label="Levels of interest" type="text" value = "*" help="Comma-separated level-names (or comma-less regular expressions to match level-names) to consider in analysis; must match at least two levels; levels must be non-numeric; may include wild cards or regular expressions. Note that extra space characters will affect results - 'a,b' is correct, but 'a , b' is not and may fail or give different results."> 52 <param name="levCSV" label="Levels of interest" type="text" value = "*" help="Comma-separated level-names (or comma-less regular expressions to match level-names) to consider in analysis; must match at least two levels; levels must be non-numeric; may include wild cards or regular expressions. Note that extra space characters will affect results - 'a,b' is correct, but 'a , b' is not and may fail or give different results.">
52 <sanitizer> 53 <sanitizer>
53 <valid initial="string.letters"> 54 <valid initial="string.letters">
54 <add preset="string.digits"/> 55 <add preset="string.digits"/>
55 <add value="&#36;" /> <!-- $ dollar, dollar-sign --> 56 <add value="&#36;" /> <!-- $ dollar, dollar-sign -->
72 <add value="&#125;" /> <!-- } r-cube, right-curly-bracket --> 73 <add value="&#125;" /> <!-- } r-cube, right-curly-bracket -->
73 <!-- IMPORTANT - Note that single and double quotes are not part of this list; they have the potential to make the 'command' section insecure or broken. --> 74 <!-- IMPORTANT - Note that single and double quotes are not part of this list; they have the potential to make the 'command' section insecure or broken. -->
74 </valid> 75 </valid>
75 </sanitizer> 76 </sanitizer>
76 </param> 77 </param>
77 <param name="matchingC" label="Level-name matching" type="select" help="How to specify level-names generically (if at all)."> 78 <param name="matchingC" label="Level-name matching" type="select" help="How to specify level-names generically. (See help below for details on using wild cards or regular expressions.)">
78 <option value="none">do no generic matching (default)</option> 79 <option value="none">do no generic matching (default)</option>
79 <option value="wildcard" selected="true">use wild-cards for matching level-names</option> 80 <option value="wildcard" selected="true">use wild-cards for matching level-names</option>
80 <option value="regex">use regular expressions for matching level-names</option> 81 <option value="regex">use regular expressions for matching level-names</option>
81 </param> 82 </param>
82 <param name="labelFeatures" type="text" value="3" label="Number of features having extreme loadings to label on cov-vs.-cor plot" help="Specify the number of features at each of the four loading-extremes that should be labelled (with the name of the feature) on the covariance-vs.-correlation plot; specify 'ALL' to label all features; this choice has no effect on the OPLS-DA loadings plot."/> 83 <param name="labelFeatures" type="text" value="3" label="How many features having extreme loadings should be labelled on cov-vs.-cor plot" help="Specify the number of features at each of the loading-extremes that should be labelled (with the name of the feature) on the covariance-vs.-correlation plot; specify 'ALL' to label all features or '0' to label no features; this choice has no effect on the OPLS-DA loadings plot."/>
84 <param
85 name="labelOrthoFeatures"
86 type="boolean"
87 checked="false"
88 truevalue="TRUE"
89 falsevalue="FALSE"
90 label="Label features having extreme orthogonal loadings"
91 help="When using the preceding parameter to label only features at the loading-extremess in the cor-vs.-cov plot, use 'no' here to label only features having extreme parallel loadings (loadp); this is the default. Choose 'yes' to add labels also to features having extreme orthogonal loadings (both loado and loadp); this may clutter the plot."/>
83 </inputs> 92 </inputs>
84 93
85 <outputs> 94 <outputs>
86 <!-- 95 <!--
87 pdf1: summaries of each contrasts, clearly labeled by level=pair name 96 pdf1: summaries of each contrasts, clearly labelled by level=pair name
88 * first PCA score-plot 97 * first PCA score-plot
89 * then PLS score-plot 98 * then PLS score-plot
90 * then PLS S-PLOT; color in red features with VIP > 1; color in grey any non-pairwise-significant features, if these are included 99 * then PLS S-PLOT; color in red features with VIP > 1; color in grey any non-pairwise-significant features, if these are included
91 --> 100 -->
92 <data name="contrast_detail" label="${tool.name}_${variableMetadata_in.name}_detail" format="pdf" /> 101 <data name="contrast_detail" label="${tool.name}_${variableMetadata_in.name}_detail" format="pdf" />
118 <param name="sampleMetadata_in" value="input_sampleMetadata.tsv"/> 127 <param name="sampleMetadata_in" value="input_sampleMetadata.tsv"/>
119 <param name="variableMetadata_in" value="input_variableMetadata.tsv"/> 128 <param name="variableMetadata_in" value="input_variableMetadata.tsv"/>
120 <param name="tesC" value="kruskal"/> 129 <param name="tesC" value="kruskal"/>
121 <param name="facC" value="k10"/> 130 <param name="facC" value="k10"/>
122 <param name="pairSigFeatOnly" value="FALSE"/> 131 <param name="pairSigFeatOnly" value="FALSE"/>
123 <param name="labelFeatures" value="TRUE"/> 132 <param name="labelFeatures" value="3"/>
133 <param name="labelOrthogonalFeatures" value="FALSE"/>
124 <param name="levCSV" value="k[12],k[3-4]"/> 134 <param name="levCSV" value="k[12],k[3-4]"/>
125 <param name="matchingC" value="regex"/> 135 <param name="matchingC" value="regex"/>
126 <output name="contrast_corcov"> 136 <output name="contrast_corcov">
127 <assert_contents> 137 <assert_contents>
128 <!-- column-labels line --> 138 <!-- column-labels line -->
181 <param name="sampleMetadata_in" value="input_sampleMetadata.tsv"/> 191 <param name="sampleMetadata_in" value="input_sampleMetadata.tsv"/>
182 <param name="variableMetadata_in" value="input_variableMetadata.tsv"/> 192 <param name="variableMetadata_in" value="input_variableMetadata.tsv"/>
183 <param name="tesC" value="kruskal"/> 193 <param name="tesC" value="kruskal"/>
184 <param name="facC" value="k10"/> 194 <param name="facC" value="k10"/>
185 <param name="pairSigFeatOnly" value="TRUE"/> 195 <param name="pairSigFeatOnly" value="TRUE"/>
186 <param name="labelFeatures" value="TRUE"/> 196 <param name="labelFeatures" value="3"/>
197 <param name="labelOrthogonalFeatures" value="TRUE"/>
187 <param name="levCSV" value="k[12],k[3-4]"/> 198 <param name="levCSV" value="k[12],k[3-4]"/>
188 <param name="matchingC" value="regex"/> 199 <param name="matchingC" value="regex"/>
189 <output name="contrast_corcov"> 200 <output name="contrast_corcov">
190 <assert_contents> 201 <assert_contents>
191 <!-- column-labels line --> 202 <!-- column-labels line -->
242 <param name="sampleMetadata_in" value="input_sampleMetadata.tsv"/> 253 <param name="sampleMetadata_in" value="input_sampleMetadata.tsv"/>
243 <param name="variableMetadata_in" value="input_variableMetadata.tsv"/> 254 <param name="variableMetadata_in" value="input_variableMetadata.tsv"/>
244 <param name="tesC" value="none"/> 255 <param name="tesC" value="none"/>
245 <param name="facC" value="k10"/> 256 <param name="facC" value="k10"/>
246 <param name="pairSigFeatOnly" value="TRUE"/> 257 <param name="pairSigFeatOnly" value="TRUE"/>
247 <param name="labelFeatures" value="FALSE"/> 258 <param name="labelFeatures" value="3"/>
259 <param name="labelOrthogonalFeatures" value="FALSE"/>
248 <param name="levCSV" value="k[12],k[3-4]"/> 260 <param name="levCSV" value="k[12],k[3-4]"/>
249 <param name="matchingC" value="regex"/> 261 <param name="matchingC" value="regex"/>
250 <output name="contrast_corcov"> 262 <output name="contrast_corcov">
251 <assert_contents> 263 <assert_contents>
252 <!-- column-labels line --> 264 <!-- column-labels line -->
331 The purpose of the 'PLS-DA Contrasts' tool is to visualize GC-MS or LC-MS features that are possible biomarkers. 343 The purpose of the 'PLS-DA Contrasts' tool is to visualize GC-MS or LC-MS features that are possible biomarkers.
332 344
333 The W4M 'Univariate' tool (Thévenot *et al.*, 2015) adds the results of family-wise corrected pairwise significance-tests as columns of the **variableMetadata** dataset. 345 The W4M 'Univariate' tool (Thévenot *et al.*, 2015) adds the results of family-wise corrected pairwise significance-tests as columns of the **variableMetadata** dataset.
334 For instance, suppose that you ran Kruskal-Wallis testing for a column named 'cluster' in sampleMetadata that has values 'k1' and 'k2' and at least one other value. 346 For instance, suppose that you ran Kruskal-Wallis testing for a column named 'cluster' in sampleMetadata that has values 'k1' and 'k2' and at least one other value.
335 347
336 - A column of variableMetadata would be labeled 'cluster_kruskal_sig' and would have values '1' and '0'; when the samples are grouped by 'cluster', '1' means that there is strong evidence against the hypothesis that there is no difference among the intensities for the feature across all sample-groups. 348 - A column of variableMetadata would be labelled 'cluster_kruskal_sig' and would have values '1' and '0'; when the samples are grouped by 'cluster', '1' means that there is strong evidence against the hypothesis that there is no difference among the intensities for the feature across all sample-groups.
337 - A column of variableMetadata would be labeled 'cluster_kruskal_k1.k2_sig' and would have values '1' and '0', where '1' means that there is significant evidence against the hypothesis that samples from sampleMetadata whose 'cluster' column contains 'k1' or 'k2' have the same intensity for that feature. 349 - A column of variableMetadata would be labelled 'cluster_kruskal_k1.k2_sig' and would have values '1' and '0', where '1' means that there is significant evidence against the hypothesis that samples from sampleMetadata whose 'cluster' column contains 'k1' or 'k2' have the same intensity for that feature.
338 350
339 The 'PLS-DA Contrasts' tool produces graphics and data for OPLS-DA contrasts of feature-intensities between significantly different pairs of factor-levels. For each factor-level, the tool performs a contrast with all other factor-levels combined and then separately with each other factor-level. 351 The 'PLS-DA Contrasts' tool produces graphics and data for OPLS-DA contrasts of feature-intensities between significantly different pairs of factor-levels. For each factor-level, the tool performs a contrast with all other factor-levels combined and then separately with each other factor-level.
340 352
341 **Along the left-to-right axis, the plots show the supervised projection of the variation explained by the predictor** (i.e., the factor specified when invoking the tool); **the top-to-bottom axis displays the variation that is orthogonal to the predictor level** (i.e., independent of it). 353 **Along the left-to-right axis, the plots show the supervised projection of the variation explained by the predictor** (i.e., the factor specified when invoking the tool); **the top-to-bottom axis displays the variation that is orthogonal to the predictor level** (i.e., independent of it).
342 354
343 Although this tool can be used in a purely exploratory manner by supplying the variableMetadata file without the columns added by the W4M 'Univariate' tool, **the preferred workflow is to use univariate testing to exclude features that are not significantly different and use OPLS-DA to visualize the differences identified in univariate testing** (Thévenot *et al.*, 2015); an appropriate exception would be to visualize contrasts of a specific list of metabolites. 355 Although this tool can be used in a purely exploratory manner by supplying the variableMetadata file without the columns added by the W4M 'Univariate' tool, **the preferred workflow is to use univariate testing to exclude features that are not significantly different and use OPLS-DA to visualize the differences identified in univariate testing** (Thévenot *et al.*, 2015); an appropriate exception would be to visualize contrasts of a specific list of metabolites.
344 356
345 It must be stressed that there may be no *single* definitive computational approach to select features that are reliable biomarkers, especially from a small number of samples or experiments. A few possible choices are examining extreme values on S-PLOTs, examining "variable importance in projection VIP for OPLS-DA" (Galindo-Prieto *et al.* 2014), and examining a feature's "selectivity ratio" (Rajalahti *et al.*, 2009). In this spirit, this tool reports the S-PLOT covariance and correlation (Wiklund *op. cit.*) and VIP metrics, and it introduces an informal "salience" metric to flag features that may merit attention without dimensional reduction; future versions may add selectivity ratio. 357 It must be stressed that there may be no *single* definitive computational approach to select features that are reliable biomarkers, especially from a small number of samples or experiments. A few possible choices are:
358
359 - picking features with maximum loadings along the projection parallel to the predictor (loadp),
360 - examining extreme values on S-PLOTs (for which covariance is linearly related to loadp),
361 - examining "variable importance in projection VIP for OPLS-DA" (Galindo-Prieto *et al.* 2014), and
362 - examining a feature's "selectivity ratio" (Rajalahti *et al.*, 2009).
363
364 In this spirit, this tool reports the S-PLOT covariance and correlation (Wiklund *op. cit.*) and VIP metrics, and it introduces an informal "salience" metric to flag features that may merit attention without dimensional reduction; future versions may add selectivity ratio.
346 365
347 For a more systematic approach to biomarker identification, please consider the W4M 'biosigner' tool (Rinuardo *et al.* 2016), which applies three different identification metrics to the selection process. 366 For a more systematic approach to biomarker identification, please consider the W4M 'biosigner' tool (Rinuardo *et al.* 2016), which applies three different identification metrics to the selection process.
348 367
349 Regardless of how any potential biomarker is identified, further validation analysis (e.g., independent confirmatory experiments) is needed before it is recommended for general application. 368 Regardless of how any potential biomarker is identified, further validation analysis (e.g., independent confirmatory experiments) is needed before it is recommended for general application.
350 369
417 436
418 [IN] Level-name matching 437 [IN] Level-name matching
419 | Indicator of **how levels are to be specified generically** (if at all) - wild cards, regular expressions, or none (no generic matching). 438 | Indicator of **how levels are to be specified generically** (if at all) - wild cards, regular expressions, or none (no generic matching).
420 | 439 |
421 440
441 [IN] Label how many extreme features
442 | Specify the number of features at each of the loading-extremes that should be labelled (with the name of the feature) on the covariance-vs.-correlation plot; specify 'ALL' to label all features; this choice has no effect on the OPLS-DA loadings plot.
443 |
444
445 [IN] Label features with extreme loado
446 | If the previous parameter has limited the the number of features to be labelled at each of the loading-extremes, then the extreme values for both loado and loadp will be labelled when this parameter is set to 'yes'; otherwise (in the default case) only extreme values for loadp will be lableld. The default was chosen to make the plot less cluttered.
447 |
448
422 [OUT] Contrast-detail output PDF 449 [OUT] Contrast-detail output PDF
423 | Several plots for each two-projection OPLS-DA analysis: 450 | Several plots for each two-projection OPLS-DA analysis:
424 451
425 - (top-left) **correlation-versus-covariance plot** of OPLS-DA results (a work-alike for the S-PLOT, computed using formula in Supplement to Wiklund, *op. cit.*); point-color becomes saturated as the "variable importance in projection to the predictive components" (VIP\ :subscript:`4,p` from Galindo-Prieto *et al.* 2014) ranges from 0.83 and 1.21 (Mehmood *et al.* 2012) 452 - (top-left) **correlation-versus-covariance plot** of OPLS-DA results (a work-alike for the S-PLOT, computed using formula in Supplement to Wiklund, *op. cit.*); point-color becomes saturated as the "variable importance in projection to the predictive components" (VIP\ :subscript:`4,p` from Galindo-Prieto *et al.* 2014) ranges from 0.83 and 1.21 (Mehmood *et al.* 2012)
426 - (bottom-left) **model-overview plot** for the two projections; grey bars are the correlation coefficient for the fitted data; black bars indicate performance in cross-validation tests (Thévenot, 2017) 453 - (bottom-left) **model-overview plot** for the two projections; grey bars are the correlation coefficient for the fitted data; black bars indicate performance in cross-validation tests (Thévenot, 2017)
433 | This file has the following columns: 460 | This file has the following columns:
434 461
435 - **featureID** - feature-identifier 462 - **featureID** - feature-identifier
436 - **factorLevel1** - factor-level 1 463 - **factorLevel1** - factor-level 1
437 - **factorLevel2** - factor-level 2 (or "other" when contrasting factor-level 1 with all other levels) 464 - **factorLevel2** - factor-level 2 (or "other" when contrasting factor-level 1 with all other levels)
438 - **correlation** - correlation of the features projection explaining the difference between the features, < 0 when intensity for level 1 is greater (from formula in Supplement to Wiklund, *op. cit.*) 465 - **correlation** - correlation of the features projection explaining the difference between the features, < 0 when intensity for level 1 is greater (from formula in Supplement to Wiklund, *op. cit.*). Note that, for a given contrast, there is a linear relationship between 'loadp' and 'correlation'.
439 - **covariance** - covariance of the features projection explaining the difference between the features, < 0 when intensity for level 1 is greater (from formula in *ibid.*) 466 - **covariance** - covariance of the features projection explaining the difference between the features, < 0 when intensity for level 1 is greater (from formula in *ibid.*)
440 - **vip4p** - "variable importance in projection" to the predictive projection, VIP\ :subscript:`4,p` (Galindo-Prieto *op. cit.*) 467 - **vip4p** - "variable importance in projection" to the predictive projection, VIP\ :subscript:`4,p` (Galindo-Prieto *op. cit.*)
441 - **vip4o** - "variable importance in projection" to the orthogonal projection, VIP\ :subscript:`4,o` (*ibid.*) 468 - **vip4o** - "variable importance in projection" to the orthogonal projection, VIP\ :subscript:`4,o` (*ibid.*)
442 - **loadp** - variable loading for the predictive projection (Wiklund *op. cit.*) 469 - **loadp** - variable loading for the predictive projection (Wiklund *op. cit.*)
443 - **loado** - variable loading for the orthogonal projection (*ibid.*) 470 - **loado** - variable loading for the orthogonal projection (*ibid.*)
622 649
623 650
624 Release notes 651 Release notes
625 ------------- 652 -------------
626 653
654 0.98.5
655
656 - bug fix: fit feature-labels within clipping region of cor-vs.cov plot
657 - new feature: optionally (and by default) suppress labels for features with extreme orthogonal loadings
658
627 0.98.3 659 0.98.3
628 660
629 - add support for two-level factors 661 - add support for two-level factors
630 - add adjusted mz and rt to output tables 662 - add adjusted mz and rt to output tables
631 - allow explicitly setting the number of features with extreme loadings to be labeled on the correlation vs. covariance plot 663 - allow explicitly setting the number of features with extreme loadings to be labelled on the correlation vs. covariance plot
632 - add loadings to corcov table 664 - add loadings to corcov table
633 665
634 0.98.2 666 0.98.2
635 667
636 - first release 668 - first release
637 669
638 670
639 ]]></help> 671 ]]></help>
640 <citations> 672 <citations>
673 <citation type="doi">10.5281/zenodo.1034784</citation>
641 <!-- Galindo_Prieto_2014 Variable influence on projection (VIP) for OPLS --> 674 <!-- Galindo_Prieto_2014 Variable influence on projection (VIP) for OPLS -->
642 <citation type="doi">10.1002/cem.2627</citation> 675 <citation type="doi">10.1002/cem.2627</citation>
643 <!-- Giacomoni_2014 W4M 2.5 --> 676 <!-- Giacomoni_2014 W4M 2.5 -->
644 <citation type="doi">10.1093/bioinformatics/btu813</citation> 677 <citation type="doi">10.1093/bioinformatics/btu813</citation>
645 <!-- Guitton_2017 W4M 3.0 --> 678 <!-- Guitton_2017 W4M 3.0 -->