An Effective Approach to Mass Spectrometry Imaging Data Partitioning Using UMAP and k-Means Clustering.

In this study, we propose an effective summarization method for mass spectrometry imaging (MSI) data and demonstrate its efficacy. The MSI data used in this study were obtained from thoracic tissue sections of mice, including the thymus. The thymus is a multi-lobed organ composed of cortical and medullary areas, playing a crucial role in T-cell differentiation. By applying MSI to the thoracic region, including the thymus, this study aims to comprehensively visualize changes in molecular localization and metabolic patterns across thoracic organs. MSI data are highly information-rich, making effective summarization and organization challenging. Therefore, we explored a method to organize and visualize the data based on either spatial or values. Specifically, we employed Uniform Manifold Approximation and Projection (UMAP) to project data into 3-dimensional space, followed by k-means clustering to divide it into multiple clusters. This approach enables detailed and comprehensive representation of diverse features. The objective of this study is to identify molecular localizations and patterns that conventional methods may overlook. Furthermore, experimental results demonstrated that the pseudo-color images generated using UMAP highlighted specific values that significantly influence image characteristics. When focusing on thoracic data, spatial segmentation resulted in clearer color differentiation; however, molecular localizations corresponding to blood vessels were not observed. This finding confirms that segmentation is more effective than spatial segmentation in discovering new molecular localizations.