Kim Seongho, Kato Ikuko, Zhang Xiang
Biostatistics and Bioinformatics Core, Karmanos Cancer Institute, Department of Oncology, School of Medicine, Wayne State University, Detroit, MI 48201, USA.
Department of Oncology and Pathology, School of Medicine, Wayne State University, Detroit, MI 48201, USA.
Metabolites. 2022 Jul 26;12(8):694. doi: 10.3390/metabo12080694.
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.
化合物鉴定是非靶向代谢组学中的关键步骤。其最重要的过程是计算实验质谱与预测质谱或质谱库中的质谱之间的相似度。与连续相似度度量不同，尽管著名的杰卡德相似度度量在未经过适当评估的情况下被广泛使用，但目前尚无研究评估二元相似度度量在化合物鉴定中的性能。因此，本研究的目的是评估二元相似度度量在非靶向代谢组学中化合物鉴定的性能。我们选择了十五种二元相似度度量，包括著名的杰卡德、迪西、索卡尔 - 斯内斯、余弦和辛普森度量，以评估它们在化合物鉴定中的性能，使用电子电离（EI）和电喷雾电离（ESI）质谱。我们的理论评估表明，在杰卡德、迪西、3W - 杰卡德、索卡尔 - 斯内斯和库尔钦斯基度量之间，在余弦和赫林格度量之间，以及在麦康纳希和德赖弗 - 克罗伯度量之间，化合物鉴定的准确性完全相同，这在使用质谱库时得到了实际证实。从基于质谱的评估中，我们观察到，对于EI质谱，性能最佳的相似度度量是麦康纳希和德赖弗 - 克罗伯度量；对于ESI质谱，性能最佳的相似度度量是余弦和赫林格度量。最稳健的相似度度量是法格 - 麦高恩度量，它在EI和ESI质谱中都是性能第二好的相似度度量。
