Relational Graph Convolutional Network for Robust Mass Spectrum Classification

IF 2.7 2区 化学 Q2 BIOCHEMICAL RESEARCH METHODS
Raphaël La Rocca, , , Anthony Cioppa, , , Enrico Ferrarini, , , Monica Höfte, , , Marc Van Droogenbroeck, , , Edwin De Pauw, , , Gauthier Eppe, , and , Loïc Quinton*, 
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引用次数: 0

Abstract

Supervised machine learning methods have shown impressive performance in interpreting mass signals and automatically segmenting spatially meaningful regions in Mass Spectrometry Imaging (MSI). Such segmentation generates maps that provide researchers with valuable insights into sample composition and serve as a foundation for downstream statistical analyses. However, these models often require data set-specific preprocessing and do not fully exploit the rich mass features available in high-resolution mass spectrometry (HRMS). Unlike low-resolution mass spectrometry, HRMS reveals additional features such as mass defects and repeated mass differences that carry important chemical information. In this work, we propose a novel deep learning architecture based on a Relational Graph Convolutional Network (R-GCN) that captures and leverages those HRMS mass features. Our model explicitly encodes structural features such as mass defects and known mass differences to represent each spectrum as a graph, enabling the learning of associations between chemically related ion families. To the best of our knowledge, no existing deep learning models for MSI classification incorporate this level of chemically informed mass structure. Most existing methods treat spectra as flat vectors or image-like inputs, thereby ignoring the underlying mass relationships. We evaluate our R-GCN approach against several conventional machine learning and deep learning baselines across diverse MSI data sets, demonstrating its robustness to common signal variations (e.g., mass shift, ion loss). Finally, we integrate Class Activation Mapping (CAM) to enhance model interpretability, enabling the identification of ion families that are relevant to specific biological or spatial regions.

Abstract Image

鲁棒质谱分类的关系图卷积网络。
有监督机器学习方法在质谱成像(MSI)中解释质量信号和自动分割空间有意义区域方面表现出令人印象深刻的性能。这种分割生成的地图为研究人员提供了对样本组成的有价值的见解,并作为下游统计分析的基础。然而,这些模型通常需要特定于数据集的预处理,并且不能充分利用高分辨率质谱(HRMS)中丰富的质量特征。与低分辨率质谱法不同,HRMS揭示了附加的特征,如质量缺陷和重复的质量差异,这些特征携带着重要的化学信息。在这项工作中,我们提出了一种基于关系图卷积网络(R-GCN)的新型深度学习架构,该架构捕获并利用了这些HRMS质量特征。我们的模型明确地编码结构特征,如质量缺陷和已知的质量差异,将每个光谱表示为一个图,从而能够学习化学相关离子族之间的关联。据我们所知,目前还没有MSI分类的深度学习模型包含这种水平的化学信息质量结构。大多数现有方法将光谱视为平面矢量或类似图像的输入,从而忽略了潜在的质量关系。我们针对不同MSI数据集的几种传统机器学习和深度学习基线评估了我们的R-GCN方法,证明了其对常见信号变化(例如质量位移、离子损失)的鲁棒性。最后,我们整合了类激活映射(CAM)来增强模型的可解释性,从而能够识别与特定生物或空间区域相关的离子家族。
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来源期刊
CiteScore
5.50
自引率
9.40%
发文量
257
审稿时长
1 months
期刊介绍: The Journal of the American Society for Mass Spectrometry presents research papers covering all aspects of mass spectrometry, incorporating coverage of fields of scientific inquiry in which mass spectrometry can play a role. Comprehensive in scope, the journal publishes papers on both fundamentals and applications of mass spectrometry. Fundamental subjects include instrumentation principles, design, and demonstration, structures and chemical properties of gas-phase ions, studies of thermodynamic properties, ion spectroscopy, chemical kinetics, mechanisms of ionization, theories of ion fragmentation, cluster ions, and potential energy surfaces. In addition to full papers, the journal offers Communications, Application Notes, and Accounts and Perspectives
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