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Poster Presentations
Day 4, June 25（Wed.）
Room P (Maesato East, Foyer, Ocean Wing)
- 4P-PM-49
Ensuring Depth and Completeness in m/z Selection: A Methodological Approach to Mass Spectrometry Imaging Data Summarization Using UMAP
(1Shimadzu Corp., 2Doshisha Univ.)
oShinichi Yamaguchi1, Masaya Ikegawa2
In this study, we propose an effective summarization method for mass spectrometry imaging (MSI) data and demonstrate its efficacy. MSI data is highly information-rich, presenting challenges for effective summarization. To address this, we explored a method to organize and visualize the data by spatial partitioning or by mass-to-charge ratio (m/z). Specifically, we utilized Uniform Manifold Approximation and Projection (UMAP) to project m/z data into three dimensions, followed by k-means clustering to create multiple clusters. This approach facilitates a detailed and comprehensive representation of diverse features within the data.
In our experiments, we applied UMAP to reduce the dimensionality of MSI data obtained from animal experiments, generating pseudo-color images that visually represent the data. The results demonstrate the effectiveness of the proposed method, providing new insights into the analysis of mass spectrometry imaging. Additionally, this approach reduces the risk of missing features among thousands of m/z data points and alleviates the overall effort required by analysts. The objective of this study is to achieve efficient analysis and offer new perspectives in MSI data analysis through this innovative method, enhancing the understanding of complex MSI datasets.