Unraveling Spatial Heterogeneity in Mass Spectrometry Imaging Data with GraphMSI.

ABSTRACT: Mass spectrometry imaging (MSI) provides valuable insights into metabolic heterogeneity by capturing in situ molecular profiles within organisms. One challenge of MSI heterogeneity analysis is performing an objective segmentation to differentiate the biological tissue into distinct regions with unique characteristics. However, current methods struggle due to the insufficient incorporation of biological context and high computational demand. To address these challenges, a novel deep learning-based approach is proposed, GraphMSI, which integrates metabolic profiles with spatial information to enhance MSI data analysis. Our comparative results demonstrate GraphMSI outperforms commonly used segmentation methods in both visual inspection and quantitative evaluation. Moreover, GraphMSI can incor

SUBMITTER: Guo L 

PROVIDER: S-EPMC11848592 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature