Compound identification is a critical process in metabolomics. The widely used approach for compound identification in gas chromatography–mass spectrometry‐based metabolomics is spectrum matching, in which the mass spectral similarity between an experimental mass spectrum and each mass spectrum in a reference library is calculated. While various similarity measures have been developed to improve the overall accuracy of compound identification, little attention has been paid to reducing the false discovery rate. We, therefore, develop an approach for controlling the false identification rate using the distribution of the difference between the first and second highest spectral similarity scores. We further propose a model‐based approach to achieving a desired true positive rate. The developed method is applied to the National Institute of Standards and Technology mass spectral library, and its performance is compared with that of the conventional approach that uses only the maximum spectral similarity score. The results show that the developed method achieves a significantly higher F1 score and positive predictive value than did the conventional approach. Copyright © 2014 John Wiley & Sons, Ltd.

中文翻译：

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在基于 GC-MS 的代谢组学中使用相似性差异发现错误识别

化合物鉴定是代谢组学中的一个关键过程。在基于气相色谱-质谱的代谢组学中，广泛使用的化合物鉴定方法是光谱匹配，其中计算实验质谱与参考库中每个质谱之间的质谱相似性。虽然已经开发了各种相似性措施来提高化合物识别的整体准确性，但很少有人关注降低错误发现率。因此，我们开发了一种使用第一和第二高光谱相似度分数之间的差异分布来控制错误识别率的方法。我们进一步提出了一种基于模型的方法来实现所需的真阳性率。将所开发的方法应用于美国国家标准与技术研究院质谱库，并将其性能与仅使用最大光谱相似度得分的常规方法进行比较。结果表明，与传统方法相比，所开发的方法实现了显着更高的 F1 分数和阳性预测值。版权所有 © 2014 John Wiley & Sons, Ltd.