Enhancing mass spectrometry data analysis: A novel framework for calibration, outlier detection, and classification

Abstract

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.