Comparative Analysis of Binary Similarity Measures for Compound Identification in Mass Spectrometry-Based Metabolomics
Compound identification is a critical step in untargeted metabolomics. Its most important procedure is to calculate the similarity between experimental mass spectra and either predicted mass spectra or mass spectra in a mass spectral library. Unlike the continuous similarity measures, there is no st...
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| Format: | Article |
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MDPI AG
2022-07-01
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| Series: | Metabolites |
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| Online Access: | https://www.mdpi.com/2218-1989/12/8/694 |
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| author | Seongho Kim Ikuko Kato Xiang Zhang |
| author_facet | Seongho Kim Ikuko Kato Xiang Zhang |
| author_sort | Seongho Kim |
| collection | DOAJ |
| description | Compound identification is a critical step in untargeted metabolomics. Its most important procedure is to calculate the similarity between experimental mass spectra and either predicted mass spectra or mass spectra in a mass spectral library. Unlike the continuous similarity measures, there is no study to assess the performance of binary similarity measures in compound identification, even though the well-known Jaccard similarity measure has been widely used without proper evaluation. The objective of this study is thus to evaluate the performance of binary similarity measures for compound identification in untargeted metabolomics. Fifteen binary similarity measures, including the well-known Jaccard, Dice, Sokal–Sneath, Cosine, and Simpson measures, were selected to assess their performance in compound identification. using both electron ionization (EI) and electrospray ionization (ESI) mass spectra. Our theoretical evaluations show that the accuracy of the compound identification was exactly the same between the Jaccard, Dice, 3W-Jaccard, Sokal–Sneath, and Kulczynski measures, between the Cosine and Hellinger measures, and between the McConnaughey and Driver–Kroeber measures, which were practically confirmed using mass spectra libraries. From the mass spectrum-based evaluation, we observed that the best performing similarity measures were the McConnaughey and Driver–Kroeber measures for EI mass spectra and the Cosine and Hellinger measures for ESI mass spectra. The most robust similarity measure was the Fager–McGowan measure, the second-best performing similarity measure in both EI and ESI mass spectra. |
| first_indexed | 2024-03-09T12:58:22Z |
| format | Article |
| id | doaj.art-ee80612bfeb14494b61d0404b203e9b7 |
| institution | Directory Open Access Journal |
| issn | 2218-1989 |
| language | English |
| last_indexed | 2024-03-09T12:58:22Z |
| publishDate | 2022-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Metabolites |
| spelling | doaj.art-ee80612bfeb14494b61d0404b203e9b72023-11-30T21:57:58ZengMDPI AGMetabolites2218-19892022-07-0112869410.3390/metabo12080694Comparative Analysis of Binary Similarity Measures for Compound Identification in Mass Spectrometry-Based MetabolomicsSeongho Kim0Ikuko Kato1Xiang Zhang2Biostatistics and Bioinformatics Core, Karmanos Cancer Institute, Department of Oncology, School of Medicine, Wayne State University, Detroit, MI 48201, USADepartment of Oncology and Pathology, School of Medicine, Wayne State University, Detroit, MI 48201, USADepartment of Chemistry, University of Louisville, Louisville, KY 40292, USACompound identification is a critical step in untargeted metabolomics. Its most important procedure is to calculate the similarity between experimental mass spectra and either predicted mass spectra or mass spectra in a mass spectral library. Unlike the continuous similarity measures, there is no study to assess the performance of binary similarity measures in compound identification, even though the well-known Jaccard similarity measure has been widely used without proper evaluation. The objective of this study is thus to evaluate the performance of binary similarity measures for compound identification in untargeted metabolomics. Fifteen binary similarity measures, including the well-known Jaccard, Dice, Sokal–Sneath, Cosine, and Simpson measures, were selected to assess their performance in compound identification. using both electron ionization (EI) and electrospray ionization (ESI) mass spectra. Our theoretical evaluations show that the accuracy of the compound identification was exactly the same between the Jaccard, Dice, 3W-Jaccard, Sokal–Sneath, and Kulczynski measures, between the Cosine and Hellinger measures, and between the McConnaughey and Driver–Kroeber measures, which were practically confirmed using mass spectra libraries. From the mass spectrum-based evaluation, we observed that the best performing similarity measures were the McConnaughey and Driver–Kroeber measures for EI mass spectra and the Cosine and Hellinger measures for ESI mass spectra. The most robust similarity measure was the Fager–McGowan measure, the second-best performing similarity measure in both EI and ESI mass spectra.https://www.mdpi.com/2218-1989/12/8/694binary similarity measurecompound identificationEIESImass spectrometryuntargeted metabolomics |
| spellingShingle | Seongho Kim Ikuko Kato Xiang Zhang Comparative Analysis of Binary Similarity Measures for Compound Identification in Mass Spectrometry-Based Metabolomics Metabolites binary similarity measure compound identification EI ESI mass spectrometry untargeted metabolomics |
| title | Comparative Analysis of Binary Similarity Measures for Compound Identification in Mass Spectrometry-Based Metabolomics |
| title_full | Comparative Analysis of Binary Similarity Measures for Compound Identification in Mass Spectrometry-Based Metabolomics |
| title_fullStr | Comparative Analysis of Binary Similarity Measures for Compound Identification in Mass Spectrometry-Based Metabolomics |
| title_full_unstemmed | Comparative Analysis of Binary Similarity Measures for Compound Identification in Mass Spectrometry-Based Metabolomics |
| title_short | Comparative Analysis of Binary Similarity Measures for Compound Identification in Mass Spectrometry-Based Metabolomics |
| title_sort | comparative analysis of binary similarity measures for compound identification in mass spectrometry based metabolomics |
| topic | binary similarity measure compound identification EI ESI mass spectrometry untargeted metabolomics |
| url | https://www.mdpi.com/2218-1989/12/8/694 |
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