Mass Spectrometry Imaging Data Clustering for Spatial Distribution
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Solution Overview
Problem
Conventional methods for analyzing mass spectrometric imaging data are labor-intensive and require specialized knowledge, as they do not guarantee that selected peaks correspond to spatially specific distributions, and multivariate analyses can be difficult to interpret, leading to inefficient data processing and limited understanding of tissue structure.
Innovation Solution
A mass analysis data processing method that extracts the mass-to-charge ratio for maximum intensity in each micro area, clusters these areas based on their ratios, and creates a colored two-dimensional image to visually represent the spatial distribution of substances, allowing for intuitive understanding of tissue structure without the need for repeated peak selection or complex multivariate analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional peak selection methods are used to display intensity spatial distribution, then the analysis operator can obtain spatial distribution information, but the operator must repeat trial-and-error operations for each peak and spend large amounts of labor and time
Solution Approach 1:
The patent performs preliminary clustering analysis on all peaks before the operator selects any peak. The score image displaying cluster assignments is generated in advance, so when the operator selects a peak, the spatial distribution is immediately available without trial-and-error. This preliminary clustering action resolves the contradiction by preparing all possible answers beforehand.
Solution Approach 2:
The patent creates a score image that copies and visualizes the cluster assignment information for all peaks simultaneously. Instead of generating one spatial distribution at a time through repeated operations, the system creates a comprehensive copy of all spatial patterns in a single display, eliminating the need for repeated trial-and-error operations.
2Productivity
If multivariate analysis such as PCA is used to analyze mass spectrometric imaging data, then the analysis can handle large amounts of data, but the results are difficult to interpret and require specialized knowledge
Solution Approach 1:
The patent uses color-coded cluster assignment images to represent different substance groups spatially. Each cluster is assigned a distinct color, making the spatial distribution of different substances immediately visible and intuitive. This visual encoding transforms complex multivariate analysis results into easily interpretable color maps that do not require specialized knowledge.
Solution Approach 2:
The patent transforms the complex multivariate data into a two-dimensional spatial visualization with color encoding. By adding the color dimension to represent cluster assignments, the system converts high-dimensional spectral data into an intuitive 2D+color representation that is easy to interpret while maintaining the productivity benefits of multivariate analysis.
3Quantity of substance
If integrated mass spectrum is displayed to show overall composition, then the operator can see all peaks, but it does not guarantee that selected peaks correspond to spatially specific distributions
Solution Approach 1:
The patent provides immediate feedback by displaying the cluster assignment score image alongside the integrated mass spectrum. When the operator selects a peak, the system instantly shows which cluster it belongs to and displays the corresponding spatial distribution, providing feedback that confirms whether the peak has spatially specific distribution characteristics.
Solution Approach 2:
The patent performs preliminary clustering analysis before peak selection, organizing all peaks into clusters based on their spatial distribution patterns. This preliminary action groups peaks by their spatial characteristics, so when the operator selects a peak from the integrated spectrum, the spatial specificity is already determined and can be immediately displayed without uncertainty.
Data Source
AI summary
The present invention aims at providing a method and apparatus for presenting, based on an enormous amount of data collected by an imaging mass analysis, information which is significant for understanding the tissue structure and other information of a biological sample and which is intuitively easy to understand to analysis operator. For each pixel 8b on a sample 8, the mass-to-charge ratio m/z (i) corresponding to the maximum intensity MI(i) in the mass spectrum is extracted, and all the pixels are grouped into clusters in accordance with their m/z (i). One cluster corresponds to one substance. Then, the largest maximum intensity MI(i) among the maximum intensities of the pixels included in a cluster is extracted as the representative maximum intensity MI(cj) for each cluster, and these representative maximum intensities MI(cj) are displayed with cluster number cj. When an operator specifies one or more clusters to be displayed by reference to these MI(cj), different colors respectively are assigned to the specified clusters, and a cluster image in which the pixels included in each cluster are colored is created and displayed. On the cluster image, the spatial distributions of a plurality of substances are shown in different colors. Simultaneously, an integrated mass spectrum of all the pixels is displayed, in which the peaks corresponding to the selected clusters are colored in the same color as in the cluster image.