深度学习在质谱成像数据分析中的应用研究进展

Research progress of deep learning applications in mass spectrometry imaging data analysis

质谱成像(MSI)是一种用于表征化合物空间分布特征的方法。随着采集方式的多样化发展和灵敏度等的不断提高,该方法产生的数据总量和分析复杂度呈指数增长,给数据后处理带来了诸多挑战。深度学习(DL)是一种在数据分析和图像识别中广泛应用的强大工具,对于质谱成像数据分析具有巨大潜力。本文综述了深度学习在质谱成像数据分析中的研究现状、应用进展和面临的挑战,重点涵盖数据预处理、图像重构、聚类分析和多模式融合4个核心阶段;还列举说明了深度学习与质谱成像技术相结合在肿瘤区域划分和亚型诊断等研究中的高效应用。本综述对该研究方向未来的发展趋势进行了展望,旨在促进人工智能与质谱分析更好的结合。

Mass spectrometry imaging(MSI)is a promising method for characterizing the spatial distribution of compounds.Given the diversified development of acquisition methods and continuous improvements in the sensitivity of this technology,both the total amount of generated data and complexity of analysis have exponentially increased,rendering increasing challenges of data postprocessing,such as large amounts of noise,background signal interferences,as well as image registration deviations caused by sample position changes and scan deviations,and etc.Deep learning(DL)is a powerful tool widely used in data analysis and image reconstruction.This tool enables the automatic feature extraction of data by building and training a neural network model,and achieves comprehensive and in-depth analysis of target data through transfer learning,which has great potential for MSI data analysis.This paper reviews the current research status,application progress and challenges of DL in MSI data analysis,focusing on four core stages:data preprocessing,image reconstruction,cluster analysis,and multimodal fusion.The application of a combination of DL and mass spectrometry imaging in the study of tumor diagnosis and subtype classification is also illustrated.This review also discusses trends of development in the future,aiming to promote a better combination of artificial intelligence and mass spectrometry technology.