annotate matchms_spectral_similarity.xml @ 10:dbe94781524a draft

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author recetox
date Mon, 05 Feb 2024 10:38:13 +0000
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1 <tool id="matchms_spectral_similarity" name="matchms spectral similarity" version="@TOOL_VERSION@+galaxy0" profile="21.09">
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2 <description>matchms spectral similarity calculation</description>
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4 <macros>
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5 <import>macros.xml</import>
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6 <import>help.xml</import>
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7 </macros>
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8 <expand macro="creator"/>
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9 <expand macro="bio.tools"/>
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11 <requirements>
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12 <requirement type="package" version="@TOOL_VERSION@">matchms</requirement>
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13 </requirements>
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15 <command detect_errors="exit_code"><![CDATA[
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16 python3 ${python_wrapper}
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17 ]]> </command>
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18
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19 <environment_variables>
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20 <environment_variable name="MPLCONFIGDIR">\$_GALAXY_JOB_TMP_DIR</environment_variable>
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21 </environment_variables>
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23 <configfiles>
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24 <configfile name="python_wrapper">
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25 @init_logger@
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27 from matchms.similarity import (CosineGreedy, CosineHungarian, MetadataMatch,
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28 ModifiedCosine, NeutralLossesCosine)
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30 similarity = ${similarity_metric}(
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31 ${algorithm.tolerance},
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32 ${algorithm.mz_power},
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33 ${algorithm.intensity_power}
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34 )
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35 name="${similarity_metric}_${algorithm.tolerance}_${algorithm.mz_power}_${algorithm.intensity_power}"
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37 @init_scores@
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38
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39 #if $scores.use_scores == "True"
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40 layer = similarity.sparse_array(
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41 references=scores.references,
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42 queries=scores.queries,
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43 idx_row = scores._scores.row,
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44 idx_col = scores._scores.col,
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45 is_symmetric=False)
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46 scores._scores.add_sparse_data(scores._scores.row, scores._scores.col, layer, name)
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47 #else
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48 scores._scores = similarity.matrix(
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49 references=scores.references,
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50 queries=scores.queries,
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51 array_type="sparse",
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52 is_symmetric=False)
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53 scores._scores.data.dtype.names = [name+"_scores", name+"_matches"]
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54 #end if
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55
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56 scores.filter_by_range(name=name+"_matches", low=0)
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57 scores.to_json("$similarity_scores")
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58 </configfile>
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59 </configfiles>
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60 <inputs>
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61 <expand macro="input_param"/>
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62 <param label="Similarity metric" name="similarity_metric" type="select" display="radio"
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63 help="Similarity metric to use for score computation.">
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64 <expand macro="similarity_metrics"/>
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65 </param>
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66 <expand macro="similarity_algorithm_params"/>
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67 </inputs>
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68
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69 <outputs>
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70 <data label="$similarity_metric scores of ${on_string}" name="similarity_scores" format="json"/>
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71 </outputs>
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72 <tests>
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73 <test> <!-- TEST #1: Test scoring of the same file formats. -->
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74 <param name="references" value="similarity/RECETOX_Exposome_pesticides_HR_MS_20220323.msp" ftype="msp"/>
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75 <param name="queries" value="similarity/fill2.msp" ftype="msp"/>
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76 <param name="similarity_metric" value="CosineGreedy"/>
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77 <output name="similarity_scores" file="spectral_similarity/test1.json" ftype="json"/>
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78 </test>
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79 <test> <!-- TEST #2: Test scoring with scores as json. -->
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80 <param name="use_scores" value="True"/>
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81 <param name="scores_in" value="metadata_match/ri_match_60.json" ftype="json"/>
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82 <param name="similarity_metric" value="CosineHungarian"/>
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83 <output name="similarity_scores" file="spectral_similarity/test2.json" ftype="json"/>
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84 </test>
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85 </tests>
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86
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87 <help>
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88 Description
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89 To compute the spectral similarity, only peaks matching another within the specified tolerance are used.
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90 Non-overlapping peaks do not contribute to the score computation.
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91 This spectrum vectorization can be interpreted as taking the intersection of spectra A and B.
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92
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93 This is different from the NIST scoring algorithm which computes a weighted score computed for both spaces, A and B.
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94 For more details see this `galaxy training`_.
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95
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96 .. rubric:: **Footnotes**
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97 .. [1] SQL join types explained on W3School_.
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98
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99 .. _W3School: https://www.w3schools.com/sql/sql_join.asp
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100 .. _galaxy training: https://training.galaxyproject.org/training-material/topics/metabolomics/tutorials/gc_ms_with_xcms/tutorial.html
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101
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102 @HELP_matchms@
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103 </help>
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104
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105 <expand macro="citations"/>
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106 </tool>