Mercurial > repos > recetox > ramclustr_define_experiment

annotate macros.xml @ 6:bc237895081d draft

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planemo upload for repository https://github.com/RECETOX/galaxytools/tree/master/tools/ramclustr commit 4b8a43b863ff8a0ff1d5a08e516068853adf358d
author recetox
date Tue, 16 Apr 2024 11:30:20 +0000
parents 7bbdcce50541
children 7c50666b88e8
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1 <macros>
2
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2 <token name="@TOOL_VERSION@">1.3.0</token>
0
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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" />
2
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26 <person
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27 givenName="Zargham"
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28 familyName="Ahmad"
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29 url="https://github.com/zargham-ahmad"
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30 identifier="0000-0002-6096-224X" />
0
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31 <organization
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32 url="https://www.recetox.muni.cz/"
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33 email="GalaxyToolsDevelopmentandDeployment@space.muni.cz"
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34 name="RECETOX MUNI"/>
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35 </creator>
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36 </xml>
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37
3
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38 <xml name="annotation">
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39 <edam_topics>
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40 <edam_topic>topic_3520</edam_topic>
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41 </edam_topics>
5
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42 </xml>
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43
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44 <xml name="refs">
3
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45 <xrefs>
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46 <xref type="bio.tools">ramclustr</xref>
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47 </xrefs>
5
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48 </xml>
3
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49
0
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50 <xml name="parameters_csv">
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51 <section name="ms_csv" title="Input MS Data as CSV" expanded="true">
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52 <param label="Input CSV" name="ms" type="data" format="csv"
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53 help="Features as columns, rows as samples. Column header in format mz_rt."/>
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54 <param label="idMSMS" name="idmsms" type="data" format="csv" optional="true"
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55 help="Optional idMSMS / MSe csv data. Same dimension and names as in input CSV are required."/>
2
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56 <param label="phenoData" name="csv_phenoData" type="data" format="csv" optional="true"
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57 help="Optional csv containing phenoData."/>
0
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58 </section>
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59 </xml>
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60
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61 <xml name="parameters_xcms">
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62 <section name="xcms" title="Input MS Data as XCMS" expanded="true">
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63 <param name="input_xcms" label="Input XCMS" type="data" format="rdata.xcms.fillpeaks"
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64 help="Grouped feature data for clustering." />
2
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65 </section>
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66 </xml>
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67
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68 <xml name="parameters_recetox_aplcms">
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69 <section name="ms_dataframe" title="Input MS Data as parquet (output from recetox-aplcms)" expanded="true">
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70 <param label="Input MS1 featureDefinitions" name="ms1_featureDefinitions" type="data" format="parquet"
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71 help="Metadata with columns: mz, rt, feature names containing MS data."/>
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72 <param label="Input MS1 featureValues" name="ms1_featureValues" type="data" format="parquet"
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73 help="data with rownames = sample names, colnames = feature names containing MS data."/>
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74 <param label="phenoData" name="df_phenoData" type="data" format="tsv,csv" optional="true"
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75 help="CSV/TSV file containing phenoData (optional)."/>
0
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76 </section>
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77 </xml>
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78
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79 <xml name="parameters_required">
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80 <param label="Sigma r" name="sr" type="float" value="0.5" help="Correlational similarity between features."/>
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81 <param label="Correlation method" name="cor_method" type="select" display="radio"
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82 help="Choose correlational method to be used - see [1] for details.">
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83 <option value="pearson" selected="true">pearson</option>
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84 <option value="spearman">spearman</option>
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85 <option value="kendall">kendall</option>
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86 </param>
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87 <param label="Maximum RT difference" name="maxt" value="60" type="float"
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88 help="Maximum difference to calculate RT similarity - values beyond this are assigned zero similarity."/>
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89 </xml>
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90
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91 <xml name="main_parameters">
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92 <section name="clustering" title="Clustering" expanded="true">
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93 <param label="Clustering linkage method" name="linkage" type="select" display="radio"
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94 help="Choose hierarchical clustering linkage method - see [2] for details.">
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95 <option value="average" selected="true">average</option>
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96 <option value="ward.D">ward.D</option>
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97 <option value="ward.D2">ward.D2</option>
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98 <option value="single">single</option>
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99 <option value="complete">complete</option>
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100 <option value="mcquitty">mcquitty</option>
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101 <option value="median">median</option>
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102 <option value="centroid">centroid</option>
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103 </param>
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104 <param label="Minimal cluster size" name="minModuleSize" type="integer" value="2"
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105 help="Minimal size (number of features) of a cluster."/>
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106 <param label="Maximal tree height" name="hmax" type="float" value="0.3"
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107 help="Cut the Hierarchical Cluster Analysis tree at this height, see [3] for details."/>
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108 <param label="Use deepSplit" name="deepSplit" type="boolean" truevalue="TRUE" falsevalue="FALSE" checked="false"
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109 help="Check to produce more smaller clusters, uncheck for fewer bigger clusters, see [3] for details."/>
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110 </section>
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111
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112 <section name="normalisation" title="Normalisation" expanded="true">
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113 <conditional name="normalisation_method">
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114 <param label="Normalisation method" name="normalize" type="select" display="radio"
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115 help="Choose method for normalization of feature intensities.">
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116 <option value="none" selected="true">none</option>
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117 <option value="TIC">TIC</option>
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118 <option value="quantile">quantile</option>
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119 <option value="batch.qc">batch.qc</option>
2
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120 <option value="qc">qc</option>
0
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121 </param>
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122 <when value="batch.qc">
2
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123 <param label="Metadata details" name="batch_order_qc" type="data" format="csv"
0
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124 help="CSV with sample names (or indices, currently not handled) on rows and columns with:
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125 batch number ('batch'), position in sequence ('order'), and whether it is a QC sample or not
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126 ('qc' with true/false OR 'sampleType' with 'sample/qc/blank')."/>
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127 <param label="QC injection range" name="qc_inj_range" type="integer" value="20"
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128 help="How many injections around each injection are to be scanned for presence of QC samples?
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129 A good rule of thumb is between 1 and 3 times the typical
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130 injection span between QC injections. i.e. if you inject QC ever 7 samples, set this to
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131 between 7 and 21. Smaller values provide more local precision but make normalization sensitive
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132 to individual poor outliers (though these are first removed using the boxplot function outlier
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133 detection), while wider values provide less local precision in normalization but better
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134 stability to individual peak areas."/>
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135 </when>
2
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136 <when value="qc">
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137 <param label="Metadata details" name="batch_order_qc" type="data" format="csv" optional="true"
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138 help="CSV with sample names (or indices, currently not handled) on rows and columns with:
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139 batch number ('batch'), position in sequence ('order'), and whether it is a QC sample or not
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140 ('qc' with true/false OR 'sampleType' with 'sample/qc/blank')."/>
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141 <param label="p.cut" name="p_cut" type="float" value="0.05"
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142 help="Numeric when run order correction is applied, only features showing a run order vs
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143 signal with a linear p-value (after FDR correction) &lt; p.cut will be adjusted. also requires
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144 r-squared &lt; rsq.cut."/>
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145 <param label="rsq.cut" name="rsq_cut" type="float" value="0.1"
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146 help="Numeric when run order correction is applied, only features showing a run order vs signal
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147 with a linear r-squared &gt; rsq.cut will be adjusted. also requires p values &lt; p.cut."/>
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148 <param label="p.adjust" name="p_adjust" type="text" value="none"
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149 help="Which p-value adjustment should be used? one of ['holm', 'hochberg', 'hommel', 'bonferroni', 'BH',
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150 'BY', 'fdr', 'none']"/>
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151 </when>
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152 <when value="none"/>
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153 <when value="TIC"/>
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154 <when value="quantile"/>
0
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155 </conditional>
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156 </section>
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157
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158 <section name="performance" title="Performance">
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159 <param label="Blocksize" name="blocksize" type="integer" value="2000"
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160 help="Number of features processed in one block."/>
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161 <param label="Blocksize factor" name="mult" type="integer" value="5"
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162 help="Factor to scale blocksize to influence processing speed."/>
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163 </section>
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164
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165 <section name="msp_output_details" title="MSP output">
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166 <param label="Merge MSP Files" name="merge_msp" type="boolean" truevalue="TRUE" falsevalue="FALSE"
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167 checked="true" help="Merge all MSP in one file or export one MSP per spectra."/>
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168 <param label="m/z decimal places" name="mzdec" type="integer" value="6"
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169 help="Number of decimal places used in printing m/z values."/>
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170 <!--
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171 Currently not forwarded because the MSP is exported always manually afterwards
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172 <param label="mspout" name="mspout" type="boolean" truevalue="TRUE" falsevalue="FALSE" checked="true" help="write msp formatted spectra to file?" />
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173 -->
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174 </section>
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175
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176 <section name="extras" title="Extras">
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177 <param label="RT only low n" name="rt_only_low_n" type="boolean" truevalue="TRUE" falsevalue="FALSE"
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178 checked="true"
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179 help="At low injection numbers, correlational relationships of peak intensities may be unreliable.
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180 By default, RAMClustR will simply ignore the correlational Sigma r value and cluster on retention time alone.
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181 If you wish to use correlation with at n less than 5, set this value to FALSE."/>
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182 <param label="Replace zeros" name="replace_zeros" type="boolean" truevalue="TRUE" falsevalue="FALSE"
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183 checked="true"
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184 help="NA, NaN, and Inf values are replaced with zero, and zero values are sometimes returned from
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185 peak peaking. When TRUE, zero values will be replaced with a small amount of noise, with noise level
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186 set based on the detected signal intensities for that feature."/>
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187 <param label="Experimental design metadata" name="ExpDes" type="data" format="csv" optional="true"
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188 help="Definition of experimental design in CSV format." />
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189 </section>
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190 </xml>
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191
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192 <xml name="output_msp">
2
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193 <collection label="Mass spectra from ${tool.name} on ${on_string} list" name="mass_spectra_collection" type="list">
0
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194 <discover_datasets pattern="__name_and_ext__" directory="spectra" recurse="true" ext="msp"/>
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195 <filter>not msp_output_details['merge_msp']</filter>
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196 </collection>
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197 <data label="Mass spectra from ${tool.name} on ${on_string}" name="mass_spectra_merged" format="msp">
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198 <filter>msp_output_details['merge_msp']</filter>
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199 </data>
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200 </xml>
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201
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202 <xml name="citations">
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203 <citations>
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204 <!-- Example of annotating a citation using a BibTex entry. -->
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205 <citation type="bibtex">
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206 @article{Broeckling2014e,
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207 abstract = {Metabolomic data are frequently acquired using chromatographically coupled mass spectrometry
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208 (MS) platforms. For such datasets, the first step in data analysis relies on feature detection, where a
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209 feature is defined by a mass and retention time. While a feature typically is derived from a single
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210 compound, a spectrum of mass signals is more a more-accurate representation of the mass spectrometric
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211 signal for a given metabolite. Here, we report a novel feature grouping method that operates in an
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212 unsupervised manner to group signals from MS data into spectra without relying on predictability of the
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213 in-source phenomenon. We additionally address a fundamental bottleneck in metabolomics, annotation of MS
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214 level signals, by incorporating indiscriminant MS/MS (idMS/MS) data implicitly: feature detection is
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215 performed on both MS and idMS/MS data, and feature-feature relationships are determined simultaneously
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216 from the MS and idMS/MS data. This approach facilitates identification of metabolites using in-source MS
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217 and/or idMS/MS spectra from a single experiment, reduces quantitative analytical variation compared to
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218 single-feature measures, and decreases false positive annotations of unpredictable phenomenon as novel
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219 compounds. This tool is released as a freely available R package, called RAMClustR, and is sufficiently
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220 versatile to group features from any chromatographic-spectrometric platform or feature-finding software.
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221 {\textcopyright} 2014 American Chemical Society.},
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222 author = {Broeckling, C. D. and Afsar, F. A. and Neumann, S. and Ben-Hur, A. and Prenni, J. E.},
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223 doi = {10.1021/ac501530d},
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224 issn = {15206882},
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225 journal = {Analytical Chemistry},
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226 number = {14},
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227 pages = {6812--6817},
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228 pmid = {24927477},
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229 title = {{RAMClust: A novel feature clustering method enables spectral-matching-based annotation for
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230 metabolomics data}},
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231 volume = {86},
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232 year = {2014}
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233 }
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234 </citation>
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235 </citations>
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236 </xml>
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237
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238 <token name="@HELP@">
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239 <![CDATA[
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240 Documentation
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241 For documentation on the tool see https://github.com/cbroeckl/RAMClustR/blob/master/vignettes/RAMClustR.Rmd
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242
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243 Upstream Tools
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244 +------------------------------+-------------------------------+----------------------+---------------------+
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245 | Name | Output File | Format | Parameter |
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246 +==============================+===============================+======================+=====================+
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247 | xcms | xset.fillPeaks.RData | rdata.xcms.fillpeaks | xcmsObj |
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248 +------------------------------+-------------------------------+----------------------+---------------------+
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249 | RAMClustR define experiment | Table with experiment details | csv | Experimental design |
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250 +------------------------------+-------------------------------+----------------------+---------------------+
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251
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252 The tool takes an **xcmsSet** object as input and extracts all relevant information.
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253
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254 +-------+------------------------+--------+------------+
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255 | Name | Output File | Format | Parameter |
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256 +=======+========================+========+============+
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257 | ??? | Feature Table with MS1 | csv | ms |
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258 +-------+------------------------+--------+------------+
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259 | ??? | Feature Table with MS2 | csv | idmsms |
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260 +-------+------------------------+--------+------------+
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261
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262 Alternatively, the tool takes a **csv** table as input which has to fulfill the following requirements
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263
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264 (1) no more than one sample (or file) name column and one feature name row;
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265 (2) feature names that contain the mass and retention times, separated by a constant delimiter; and
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266 (3) features in columns and samples in rows.
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267
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268 +----------------------+-------------------+-------------------+--------------------+--------------------+
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269 | sample | 100.88_262.464 | 100.01_423.699 | 100.003_128.313 | 100.0057_154.686 |
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270 +======================+===================+===================+====================+====================+
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271 | 10_qc_16x_dil_milliq | 0 | 195953.6376 | 0 | 0 |
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272 +----------------------+-------------------+-------------------+--------------------+--------------------+
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273 | 11_qc_8x_dil_milliq | 0 | 117742.1828 | 4247300.664 | 0 |
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274 +----------------------+-------------------+-------------------+--------------------+--------------------+
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275 | 12_qc_32x_dil_milliq | 4470859.38 | 0 | 2206092.112 | 0 |
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276 +----------------------+-------------------+-------------------+--------------------+--------------------+
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277 | 15_qc_16x_dil_milliq | 0 | 0 | 2767477.481 | 0 |
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278 +----------------------+-------------------+-------------------+--------------------+--------------------+
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279
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280
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281 Downstream Tools
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282 The output is a msp file or a collection of msp files, with additional Spec Abundance file.
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283
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284 +---------+--------------+----------------------+
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285 | Name | Output File | Format |
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286 +=========+==============+======================+
4
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287 | matchms | Mass Spectra | collection (tgz/msp) |
0
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288 +---------+--------------+----------------------+
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289
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290 @GENERAL_HELP@
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291 ]]>
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292 </token>
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293
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294 <token name="@GENERAL_HELP@">
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295 Background
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296 Metabolomics
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297 Metabolomics is frequently performed using chromatographically coupled mass spectrometry, with gas
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298 chromatography, liquid chromatography, and capillary electrophoresis being the most frequently utilized
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299 methods of separation. The coupling of chromatography to mass spectrometry is enabled with an
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300 appropriate ionization source - electron impact (EI) for gas phase separations and electrospray
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301 ionization (ESI) for liquid phase separations. XCMS is a commonly used tool to detect all the signals
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302 from a metabolomics dataset, generating aligned features, where a feature is represented by a mass and
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303 retention time. Each feature is presumed to derive from a single compound. However, each compound is
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304 represented by several features. With any ionization method, isotopic peaks will be observed reflective
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305 of the elemental composition of the analyte. In EI, fragmentation is a byproduct of ionization, and has
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306 driven the generation of large mass spectral libraries. In ESI, in-source fragmentation frequently
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307 occurs, the magnitude of which is compound dependent, with more labile compounds being more prone to
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308 in-source fragmentation. ESI can also product multiple adduct forms (protonated, potassiated, sodiated,
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309 ammoniated...), and can produce multimers (i.e. [2M+H]+, [3M+K]+, etc) and multiple charged species
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310 ([M+2H]++). This can become further complicated by considering combinations of these phenomena. For
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311 example [2M+3H]+++ (triply charged dimer) or an in-source fragment of a dimer.
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312
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313 RAMClustR approach
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314 RAMClustR was designed to group features designed from the same compound using an approach which is
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315 **1.** unsupervised, **2.** platform agnostic, and **3.** devoid of curated rules, as the depth of
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316 understanding of these processes is insufficient to enable accurate curation/prediction of all phenomenon
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317 that may occur. We achieve this by making two assumptions. The first is that two features derived
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318 from the same compound with have (approximately) the same retention time. The second is that two
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319 features derived from the same compound will have (approximately) the same quantitative trend across
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320 all samples in the xcms sample set. From these assumptions, we can calculate a retention time
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321 similarity score and a correlational similarity score for each feature pair. A high similarity score
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322 for both retention time and correlation indicates a strong probability that two features derive from
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323 the same compound. Since both conditions must be met, the product of the two similarity scores provides
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324 the best approximation of the total similarity score - i.e. a feature pair with retention time similarity
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325 of 1 and correlational similarity of 0 is unlikely to derive from one compound - 1 x 0 = 0, the final
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326 similarity score is zero, indicating the two features represent two different compounds. Similarly, a
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327 feature pair with retention time similarity of 0 and correlational similarity of 1 is unlikely to derive
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328 from one compound - 0 x 1 = 0. Alternatively - a feature pair with retention time similarity of 1 and
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329 correlational similarity of 1 is likely to derive from one compound - 1 x 1 = 1.
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330
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331 The RAMClustR algorithm is built on creating similarity scores for all pairs of features, submitting
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332 this score matrix for hierarchical clustering, and then cutting the resulting dendrogram into neat
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333 chunks using the dynamicTreeCut package - where each 'chunk' of the dendrogram results in a group of
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334 features likely to be derived from a single compound. Importantly, this is achieved without looking for
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335 specific phenomenon (i.e. sodiation), meaning that grouping can be performed on any dataset, whether it
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336 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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337 adduction event, and predictable or unpredictable signals.
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338 </token>
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339
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340 <token name="@HELP_experiment@">
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341 <![CDATA[
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342 Create an Experimental Design specification for RAMClustR experiment.
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343
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344 Downstream Tools
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345 +-----------+-----------------------+--------+
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346 | Name | Output File | Format |
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347 +===========+=======================+========+
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348 | RAMClustR | Experiment definition | csv |
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349 +-----------+-----------------------+--------+
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350
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351 ]]>
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352 </token>
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353 </macros>