Mercurial > repos > recetox > ramclustr_define_experiment

annotate macros.xml @ 0:42c2a25ff197 draft

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planemo upload for repository https://github.com/RECETOX/galaxytools/tree/master/tools/ramclustr commit c321421a07bfdcc9ed423e9ed2ee794157984ba1
author recetox
date Wed, 29 Jun 2022 10:00:43 +0000
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children 25625114618e
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42c2a25ff197 planemo upload for repository https://github.com/RECETOX/galaxytools/tree/master/tools/ramclustr commit c321421a07bfdcc9ed423e9ed2ee794157984ba1
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1 <macros>
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2 <token name="@TOOL_VERSION@">1.2.4</token>
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3
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4 <xml name="creator">
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5 <creator>
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6 <person
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7 givenName="Helge"
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8 familyName="Hecht"
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9 url="https://github.com/hechth"
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10 identifier="0000-0001-6744-996X" />
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11 <person
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12 givenName="Maksym"
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13 familyName="Skoryk"
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14 url="https://github.com/maximskorik"
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15 identifier="0000-0003-2056-8018" />
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16 <person
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17 givenName="Matej"
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18 familyName="Troják"
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19 url="https://github.com/xtrojak"
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20 identifier="0000-0003-0841-2707" />
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21 <person
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22 givenName="Martin"
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23 familyName="Čech"
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24 url="https://github.com/martenson"
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25 identifier="0000-0002-9318-1781" />
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26 <organization
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27 url="https://www.recetox.muni.cz/"
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28 email="GalaxyToolsDevelopmentandDeployment@space.muni.cz"
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29 name="RECETOX MUNI"/>
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30 </creator>
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31 </xml>
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32
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33 <xml name="parameters_csv">
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34 <section name="ms_csv" title="Input MS Data as CSV" expanded="true">
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35 <param label="Input CSV" name="ms" type="data" format="csv"
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36 help="Features as columns, rows as samples. Column header in format mz_rt."/>
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37 <param label="idMSMS" name="idmsms" type="data" format="csv" optional="true"
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38 help="Optional idMSMS / MSe csv data. Same dimension and names as in input CSV are required."/>
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39 </section>
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40 </xml>
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41
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42 <xml name="parameters_xcms">
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43 <section name="xcms" title="Input MS Data as XCMS" expanded="true">
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44 <param name="input_xcms" label="Input XCMS" type="data" format="rdata.xcms.fillpeaks"
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45 help="Grouped feature data for clustering." />
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46 <param label="Preserve phenotype" name="usePheno" type="boolean" truevalue="TRUE" falsevalue="FALSE" checked="true"
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47 help="Transfer phenotype data from XCMS object to Spec abundance file."/>
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48 </section>
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49 </xml>
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50
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51 <xml name="parameters_required">
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52 <param label="Sigma r" name="sr" type="float" value="0.5" help="Correlational similarity between features."/>
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53 <param label="Correlation method" name="cor_method" type="select" display="radio"
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54 help="Choose correlational method to be used - see [1] for details.">
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55 <option value="pearson" selected="true">pearson</option>
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56 <option value="everything">everything</option>
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57 <option value="spearman">spearman</option>
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58 <option value="kendall">kendall</option>
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59 </param>
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60 <param label="Maximum RT difference" name="maxt" value="60" type="float"
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61 help="Maximum difference to calculate RT similarity - values beyond this are assigned zero similarity."/>
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62 </xml>
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63
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64 <xml name="main_parameters">
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65 <section name="clustering" title="Clustering" expanded="true">
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66 <param label="Clustering linkage method" name="linkage" type="select" display="radio"
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67 help="Choose hierarchical clustering linkage method - see [2] for details.">
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68 <option value="average" selected="true">average</option>
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69 <option value="ward.D">ward.D</option>
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70 <option value="ward.D2">ward.D2</option>
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71 <option value="single">single</option>
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72 <option value="complete">complete</option>
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73 <option value="mcquitty">mcquitty</option>
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74 <option value="median">median</option>
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75 <option value="centroid">centroid</option>
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76 </param>
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77 <param label="Minimal cluster size" name="minModuleSize" type="integer" value="2"
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78 help="Minimal size (number of features) of a cluster."/>
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79 <param label="Maximal tree height" name="hmax" type="float" value="0.3"
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80 help="Cut the Hierarchical Cluster Analysis tree at this height, see [3] for details."/>
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81 <param label="Use deepSplit" name="deepSplit" type="boolean" truevalue="TRUE" falsevalue="FALSE" checked="false"
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82 help="Check to produce more smaller clusters, uncheck for fewer bigger clusters, see [3] for details."/>
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83 </section>
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84
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85 <section name="normalisation" title="Normalisation" expanded="true">
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86 <conditional name="normalisation_method">
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87 <param label="Normalisation method" name="normalize" type="select" display="radio"
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88 help="Choose method for normalization of feature intensities.">
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89 <option value="none" selected="true">none</option>
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90 <option value="TIC">TIC</option>
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91 <option value="quantile">quantile</option>
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92 <option value="batch.qc">batch.qc</option>
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93 </param>
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94 <when value="batch.qc">
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95 <param label="Metadata details" name="batch_order_qc" type="data" format="csv" optional="true"
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96 help="CSV with sample names (or indices, currently not handled) on rows and columns with:
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97 batch number ('batch'), position in sequence ('order'), and whether it is a QC sample or not
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98 ('qc' with true/false OR 'sampleType' with 'sample/qc/blank')."/>
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99 <param label="QC injection range" name="qc_inj_range" type="integer" value="20"
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100 help="How many injections around each injection are to be scanned for presence of QC samples?
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101 A good rule of thumb is between 1 and 3 times the typical
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102 injection span between QC injections. i.e. if you inject QC ever 7 samples, set this to
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103 between 7 and 21. Smaller values provide more local precision but make normalization sensitive
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104 to individual poor outliers (though these are first removed using the boxplot function outlier
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105 detection), while wider values provide less local precision in normalization but better
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106 stability to individual peak areas."/>
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107 </when>
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108 </conditional>
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109 </section>
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110
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111 <section name="performance" title="Performance">
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112 <param label="Blocksize" name="blocksize" type="integer" value="2000"
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113 help="Number of features processed in one block."/>
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114 <param label="Blocksize factor" name="mult" type="integer" value="5"
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115 help="Factor to scale blocksize to influence processing speed."/>
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116 </section>
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117
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118 <section name="msp_output_details" title="MSP output">
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119 <param label="Merge MSP Files" name="merge_msp" type="boolean" truevalue="TRUE" falsevalue="FALSE"
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120 checked="true" help="Merge all MSP in one file or export one MSP per spectra."/>
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121 <param label="m/z decimal places" name="mzdec" type="integer" value="6"
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122 help="Number of decimal places used in printing m/z values."/>
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123 <!--
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124 Currently not forwarded because the MSP is exported always manually afterwards
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125 <param label="mspout" name="mspout" type="boolean" truevalue="TRUE" falsevalue="FALSE" checked="true" help="write msp formatted spectra to file?" />
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126 -->
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127 </section>
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128
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129 <section name="extras" title="Extras">
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130 <param label="RT only low n" name="rt_only_low_n" type="boolean" truevalue="TRUE" falsevalue="FALSE"
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131 checked="true"
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132 help="At low injection numbers, correlational relationships of peak intensities may be unreliable.
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133 By default, RAMClustR will simply ignore the correlational Sigma r value and cluster on retention time alone.
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134 If you wish to use correlation with at n less than 5, set this value to FALSE."/>
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135 <param label="Replace zeros" name="replace_zeros" type="boolean" truevalue="TRUE" falsevalue="FALSE"
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136 checked="true"
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137 help="NA, NaN, and Inf values are replaced with zero, and zero values are sometimes returned from
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138 peak peaking. When TRUE, zero values will be replaced with a small amount of noise, with noise level
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139 set based on the detected signal intensities for that feature."/>
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140 <param label="Experimental design metadata" name="ExpDes" type="data" format="csv" optional="true"
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141 help="Definition of experimental design in CSV format." />
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142 </section>
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143 </xml>
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144
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145 <xml name="output_msp">
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146 <collection label="Mass spectra from ${tool.name} on ${on_string}" name="mass_spectra_collection" type="list">
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147 <discover_datasets pattern="__name_and_ext__" directory="spectra" recurse="true" ext="msp"/>
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148 <filter>not msp_output_details['merge_msp']</filter>
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149 </collection>
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150 <data label="Mass spectra from ${tool.name} on ${on_string}" name="mass_spectra_merged" format="msp">
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151 <filter>msp_output_details['merge_msp']</filter>
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152 </data>
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153 </xml>
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154
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155 <xml name="citations">
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156 <citations>
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157 <!-- Example of annotating a citation using a BibTex entry. -->
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158 <citation type="bibtex">
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159 @article{Broeckling2014e,
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160 abstract = {Metabolomic data are frequently acquired using chromatographically coupled mass spectrometry
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161 (MS) platforms. For such datasets, the first step in data analysis relies on feature detection, where a
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162 feature is defined by a mass and retention time. While a feature typically is derived from a single
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163 compound, a spectrum of mass signals is more a more-accurate representation of the mass spectrometric
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164 signal for a given metabolite. Here, we report a novel feature grouping method that operates in an
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165 unsupervised manner to group signals from MS data into spectra without relying on predictability of the
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166 in-source phenomenon. We additionally address a fundamental bottleneck in metabolomics, annotation of MS
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167 level signals, by incorporating indiscriminant MS/MS (idMS/MS) data implicitly: feature detection is
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168 performed on both MS and idMS/MS data, and feature-feature relationships are determined simultaneously
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169 from the MS and idMS/MS data. This approach facilitates identification of metabolites using in-source MS
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170 and/or idMS/MS spectra from a single experiment, reduces quantitative analytical variation compared to
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171 single-feature measures, and decreases false positive annotations of unpredictable phenomenon as novel
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172 compounds. This tool is released as a freely available R package, called RAMClustR, and is sufficiently
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173 versatile to group features from any chromatographic-spectrometric platform or feature-finding software.
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174 {\textcopyright} 2014 American Chemical Society.},
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175 author = {Broeckling, C. D. and Afsar, F. A. and Neumann, S. and Ben-Hur, A. and Prenni, J. E.},
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176 doi = {10.1021/ac501530d},
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177 issn = {15206882},
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178 journal = {Analytical Chemistry},
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179 number = {14},
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180 pages = {6812--6817},
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181 pmid = {24927477},
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182 title = {{RAMClust: A novel feature clustering method enables spectral-matching-based annotation for
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183 metabolomics data}},
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184 volume = {86},
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185 year = {2014}
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186 }
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187 </citation>
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188 </citations>
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189 </xml>
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190
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191 <token name="@HELP@">
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192 <![CDATA[
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193 Documentation
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194 For documentation on the tool see https://github.com/cbroeckl/RAMClustR/blob/master/vignettes/RAMClustR.Rmd
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195
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196 Upstream Tools
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197 +------------------------------+-------------------------------+----------------------+---------------------+
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198 | Name | Output File | Format | Parameter |
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199 +==============================+===============================+======================+=====================+
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200 | xcms | xset.fillPeaks.RData | rdata.xcms.fillpeaks | xcmsObj |
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201 +------------------------------+-------------------------------+----------------------+---------------------+
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202 | RAMClustR define experiment | Table with experiment details | csv | Experimental design |
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203 +------------------------------+-------------------------------+----------------------+---------------------+
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204
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205 The tool takes an **xcmsSet** object as input and extracts all relevant information.
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206
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207 +-------+------------------------+--------+------------+
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208 | Name | Output File | Format | Parameter |
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209 +=======+========================+========+============+
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210 | ??? | Feature Table with MS1 | csv | ms |
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211 +-------+------------------------+--------+------------+
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212 | ??? | Feature Table with MS2 | csv | idmsms |
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213 +-------+------------------------+--------+------------+
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214
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215 Alternatively, the tool takes a **csv** table as input which has to fulfill the following requirements
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216
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217 (1) no more than one sample (or file) name column and one feature name row;
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218 (2) feature names that contain the mass and retention times, separated by a constant delimiter; and
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219 (3) features in columns and samples in rows.
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220
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221 +----------------------+-------------------+-------------------+--------------------+--------------------+
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222 | sample | 100.88_262.464 | 100.01_423.699 | 100.003_128.313 | 100.0057_154.686 |
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223 +======================+===================+===================+====================+====================+
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224 | 10_qc_16x_dil_milliq | 0 | 195953.6376 | 0 | 0 |
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225 +----------------------+-------------------+-------------------+--------------------+--------------------+
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226 | 11_qc_8x_dil_milliq | 0 | 117742.1828 | 4247300.664 | 0 |
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227 +----------------------+-------------------+-------------------+--------------------+--------------------+
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228 | 12_qc_32x_dil_milliq | 4470859.38 | 0 | 2206092.112 | 0 |
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229 +----------------------+-------------------+-------------------+--------------------+--------------------+
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230 | 15_qc_16x_dil_milliq | 0 | 0 | 2767477.481 | 0 |
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231 +----------------------+-------------------+-------------------+--------------------+--------------------+
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232
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233
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234 Downstream Tools
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235 The output is a msp file or a collection of msp files, with additional Spec Abundance file.
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236
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237 +---------+--------------+----------------------+
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238 | Name | Output File | Format |
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239 +=========+==============+======================+
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240 | matchMS | Mass Spectra | collection (tgz/msp) |
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241 +---------+--------------+----------------------+
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242
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243 @GENERAL_HELP@
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244 ]]>
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245 </token>
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246
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247 <token name="@GENERAL_HELP@">
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248 Background
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249 Metabolomics
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250 Metabolomics is frequently performed using chromatographically coupled mass spectrometry, with gas
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251 chromatography, liquid chromatography, and capillary electrophoresis being the most frequently utilized
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252 methods of separation. The coupling of chromatography to mass spectrometry is enabled with an
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253 appropriate ionization source - electron impact (EI) for gas phase separations and electrospray
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254 ionization (ESI) for liquid phase separations. XCMS is a commonly used tool to detect all the signals
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255 from a metabolomics dataset, generating aligned features, where a feature is represented by a mass and
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256 retention time. Each feature is presumed to derive from a single compound. However, each compound is
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257 represented by several features. With any ionization method, isotopic peaks will be observed reflective
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258 of the elemental composition of the analyte. In EI, fragmentation is a byproduct of ionization, and has
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259 driven the generation of large mass spectral libraries. In ESI, in-source fragmentation frequently
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260 occurs, the magnitude of which is compound dependent, with more labile compounds being more prone to
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261 in-source fragmentation. ESI can also product multiple adduct forms (protonated, potassiated, sodiated,
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262 ammoniated...), and can produce multimers (i.e. [2M+H]+, [3M+K]+, etc) and multiple charged species
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263 ([M+2H]++). This can become further complicated by considering combinations of these phenomena. For
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264 example [2M+3H]+++ (triply charged dimer) or an in-source fragment of a dimer.
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265
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266 RAMClustR approach
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267 RAMClustR was designed to group features designed from the same compound using an approach which is
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268 **1.** unsupervised, **2.** platform agnostic, and **3.** devoid of curated rules, as the depth of
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269 understanding of these processes is insufficient to enable accurate curation/prediction of all phenomenon
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270 that may occur. We achieve this by making two assumptions. The first is that two features derived
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271 from the same compound with have (approximately) the same retention time. The second is that two
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272 features derived from the same compound will have (approximately) the same quantitative trend across
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273 all samples in the xcms sample set. From these assumptions, we can calculate a retention time
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274 similarity score and a correlational similarity score for each feature pair. A high similarity score
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275 for both retention time and correlation indicates a strong probability that two features derive from
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276 the same compound. Since both conditions must be met, the product of the two similarity scores provides
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277 the best approximation of the total similarity score - i.e. a feature pair with retention time similarity
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278 of 1 and correlational similarity of 0 is unlikely to derive from one compound - 1 x 0 = 0, the final
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279 similarity score is zero, indicating the two features represent two different compounds. Similarly, a
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280 feature pair with retention time similarity of 0 and correlational similarity of 1 is unlikely to derive
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281 from one compound - 0 x 1 = 0. Alternatively - a feature pair with retention time similarity of 1 and
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282 correlational similarity of 1 is likely to derive from one compound - 1 x 1 = 1.
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283
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284 The RAMClustR algorithm is built on creating similarity scores for all pairs of features, submitting
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285 this score matrix for hierarchical clustering, and then cutting the resulting dendrogram into neat
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286 chunks using the dynamicTreeCut package - where each 'chunk' of the dendrogram results in a group of
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287 features likely to be derived from a single compound. Importantly, this is achieved without looking for
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288 specific phenomenon (i.e. sodiation), meaning that grouping can be performed on any dataset, whether it
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289 is positive or negative ionization mode, EI or ESI, LC-MS GC-MS or CE-MS, in-source fragment or complex
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290 adduction event, and predictable or unpredictable signals.
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291 </token>
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292
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293 <token name="@HELP_experiment@">
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294 <![CDATA[
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295 Create an Experimental Design specification for RAMClustR experiment.
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296
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297 Downstream Tools
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298 +-----------+-----------------------+--------+
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299 | Name | Output File | Format |
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300 +===========+=======================+========+
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301 | RAMClustR | Experiment definition | csv |
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302 +-----------+-----------------------+--------+
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303
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304 ]]>
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305 </token>
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306 </macros>