Mercurial > repos > recetox > matchms

annotate matchms_similarity_wrapper.py @ 2:67ed0d4f3724 draft

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planemo upload for repository https://github.com/RECETOX/galaxytools/tree/master/tools/matchms commit b974ff354fa375975050026482e1b6ded8005682
author recetox
date Thu, 08 Sep 2022 14:48:21 +0000
parents f680068b7863
children 9f8532c99845
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1 import argparse
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2 import sys
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4 import numpy as np
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5 from matchms import calculate_scores
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6 from matchms.importing import load_from_mgf, load_from_msp
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f680068b7863 planemo upload for repository https://github.com/RECETOX/galaxytools/tree/master/tools/matchms commit ca44513d50b29a4706e2a2db96c23ef6688b7c2d
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7 from matchms.similarity import CosineGreedy, CosineHungarian, MetadataMatch, ModifiedCosine, NeutralLossesCosine
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8 from pandas import DataFrame
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11 def convert_precursor_mz(spectrum):
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12 """
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13 Check the presence of precursor m/z since it is needed for ModifiedCosine similarity metric. Convert to float if
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14 needed, raise error if missing.
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15 """
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17 if "precursor_mz" in spectrum.metadata:
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18 metadata = spectrum.metadata
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19 metadata["precursor_mz"] = float(metadata["precursor_mz"])
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20 spectrum.metadata = metadata
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21 return spectrum
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22 else:
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23 raise ValueError("Precursor_mz missing. Apply 'add_precursor_mz' filter first.")
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26 def main(argv):
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27 parser = argparse.ArgumentParser(description="Compute MSP similarity scores")
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28 parser.add_argument("-r", dest="ri_tolerance", type=float, help="Use RI filtering with given tolerance.")
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29 parser.add_argument("-s", dest="symmetric", action='store_true', help="Computation is symmetric.")
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30 parser.add_argument("--ref", dest="references_filename", type=str, help="Path to reference spectra library.")
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31 parser.add_argument("--ref_format", dest="references_format", type=str, help="Reference spectra library file format.")
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32 parser.add_argument("queries_filename", type=str, help="Path to query spectra.")
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33 parser.add_argument("queries_format", type=str, help="Query spectra file format.")
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34 parser.add_argument("similarity_metric", type=str, help='Metric to use for matching.')
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35 parser.add_argument("tolerance", type=float, help="Tolerance to use for peak matching.")
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36 parser.add_argument("mz_power", type=float, help="The power to raise mz to in the cosine function.")
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37 parser.add_argument("intensity_power", type=float, help="The power to raise intensity to in the cosine function.")
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38 parser.add_argument("output_filename_scores", type=str, help="Path where to store the output .tsv scores.")
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39 parser.add_argument("output_filename_matches", type=str, help="Path where to store the output .tsv matches.")
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40 args = parser.parse_args()
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42 if args.queries_format == 'msp':
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43 queries_spectra = list(load_from_msp(args.queries_filename))
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44 elif args.queries_format == 'mgf':
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45 queries_spectra = list(load_from_mgf(args.queries_filename))
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46 else:
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47 raise ValueError(f'File format {args.queries_format} not supported for query spectra.')
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49 if args.symmetric:
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50 reference_spectra = []
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51 else:
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52 if args.references_format == 'msp':
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53 reference_spectra = list(load_from_msp(args.references_filename))
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54 elif args.references_format == 'mgf':
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55 reference_spectra = list(load_from_mgf(args.references_filename))
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56 else:
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57 raise ValueError(f'File format {args.references_format} not supported for reference spectra library.')
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59 if args.similarity_metric == 'CosineGreedy':
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60 similarity_metric = CosineGreedy(args.tolerance, args.mz_power, args.intensity_power)
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61 elif args.similarity_metric == 'CosineHungarian':
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62 similarity_metric = CosineHungarian(args.tolerance, args.mz_power, args.intensity_power)
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63 elif args.similarity_metric == 'ModifiedCosine':
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64 similarity_metric = ModifiedCosine(args.tolerance, args.mz_power, args.intensity_power)
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65 reference_spectra = list(map(convert_precursor_mz, reference_spectra))
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66 queries_spectra = list(map(convert_precursor_mz, queries_spectra))
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67 elif args.similarity_metric == 'NeutralLossesCosine':
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68 similarity_metric = NeutralLossesCosine(args.tolerance, args.mz_power, args.intensity_power)
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69 reference_spectra = list(map(convert_precursor_mz, reference_spectra))
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70 queries_spectra = list(map(convert_precursor_mz, queries_spectra))
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71 else:
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72 return -1
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73
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74 print("Calculating scores...")
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75 scores = calculate_scores(
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76 references=queries_spectra if args.symmetric else reference_spectra,
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77 queries=queries_spectra,
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78 similarity_function=similarity_metric,
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79 is_symmetric=args.symmetric
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80 )
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81
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82 if args.ri_tolerance is not None:
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83 print("RI filtering with tolerance ", args.ri_tolerance)
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84 ri_matches = calculate_scores(reference_spectra, queries_spectra, MetadataMatch("retention_index", "difference", args.ri_tolerance)).scores
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85 scores._scores["score"] = np.where(ri_matches, scores.scores["score"], 0.0)
0
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87 write_outputs(args, scores)
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88 return 0
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91 def write_outputs(args, scores):
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92 print("Storing outputs...")
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93 query_names = [spectra.metadata['compound_name'] for spectra in scores.queries]
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94 reference_names = [spectra.metadata['compound_name'] for spectra in scores.references]
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95
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96 # Write scores to dataframe
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97 dataframe_scores = DataFrame(data=[entry["score"] for entry in scores.scores], index=reference_names, columns=query_names)
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98 dataframe_scores.to_csv(args.output_filename_scores, sep='\t')
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99
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100 # Write number of matches to dataframe
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101 dataframe_matches = DataFrame(data=[entry["matches"] for entry in scores.scores], index=reference_names, columns=query_names)
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102 dataframe_matches.to_csv(args.output_filename_matches, sep='\t')
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103
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104
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105 if __name__ == "__main__":
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106 main(argv=sys.argv[1:])
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107 pass