Mass spectrometry (MS) is a powerful analytical technique in metabolomics, enabling the identification and quantification of metabolites. However, analyzing MS data poses challenges such as batch effects, outliers, and high-dimensional data. In this paper, we propose a comprehensive framework for MS data analysis. The framework integrates data calibration, outlier detection, and automatic classification modules. Data calibration is performed using a deep autoencoder to remove batch effects. Outlier detection combines multiple algorithms through ensemble learning to identify and remove outliers. Automatic classification utilizes a transformer model to handle high-dimensional data and capture global feature relationships. Experimental results on myocardial infarction (MI) and coronary heart disease (CHD) datasets demonstrate the effectiveness of the framework. It outperforms traditional classification models and achieves higher accuracy. The proposed framework provides a robust solution for MS data analysis, facilitating more accurate classification and enabling reliable biological insights in metabolomics research.

中文翻译：

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增强质谱数据分析：用于校准、异常值检测和分类的新颖框架

质谱 (MS) 是代谢组学中一种强大的分析技术，可以对代谢物进行识别和定量。然而，分析 MS 数据会带来批次效应、异常值和高维数据等挑战。在本文中，我们提出了 MS 数据分析的综合框架。该框架集成了数据校准、异常值检测和自动分类模块。使用深度自动编码器执行数据校准以消除批次效应。异常值检测通过集成学习结合多种算法来识别和去除异常值。自动分类利用变压器模型来处理高维数据并捕获全局特征关系。心肌梗死（MI）和冠心病（CHD）数据集的实验结果证明了该框架的有效性。它优于传统的分类模型并达到更高的准确率。所提出的框架为 MS 数据分析提供了强大的解决方案，促进更准确的分类并在代谢组学研究中实现可靠的生物学见解。