Imaging Mass Spectrometer Using Manifold Learning for Cluster Discrimination
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Solution Overview
Problem
Current imaging mass spectrometry techniques, such as hierarchical clustering analysis, are computationally expensive and struggle to accurately distinguish between clusters, making it difficult to efficiently find and compare the distribution of substances across multiple samples.
Innovation Solution
The implementation of a non-linear dimension reduction method using manifold learning, specifically the UMAP technique, to process mass spectrometric data, reducing dimensions to three dimensions and assigning primary colors to each axis, allowing for the creation of segmentation images that clearly differentiate between distribution patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If hierarchical clustering analysis is used for image classification, then the data can be classified into clusters, but the calculation time becomes excessively long and the clusters cannot be accurately distinguished
Solution Approach 1:
The patent extracts only the essential features from the mass spectrometry data to create a reduced feature set that preserves the most important information for cluster discrimination. This extraction process removes redundant dimensions while maintaining the discriminative power needed for accurate classification, thereby reducing calculation time without sacrificing reliability.
Solution Approach 2:
The patent transforms the high-dimensional mass spectrometry data into a lower-dimensional feature space by identifying and retaining only the most discriminative features. This dimensionality reduction creates a simplified representation that maintains cluster separation while enabling faster computational processing and more efficient visualization.
2Reliability
If visual inspection of MS imaging graphics is performed to find substances of interest, then the operator can identify compounds, but the process requires extreme time and labor and cannot ensure sufficient reliability
Solution Approach 1:
The patent implements automatic image classification and segmentation that enables the system to identify and highlight regions of interest without requiring manual inspection of each MS imaging graphic. The automated algorithms process the data independently, providing reliable identification of substance distributions while dramatically improving analysis efficiency and eliminating human fatigue-related errors.
3Ease of operation
If automatic image classification is used to reduce workload, then the user workload is reduced to some extent, but the average images from different clusters are not independent and the differences between clusters are difficult to discern
Solution Approach 1:
The patent extracts the most discriminative features from the mass spectrometry data to create a reduced feature set that preserves the essential differences between clusters. This extraction ensures that the automated classification maintains clear separation between clusters while reducing the computational complexity and user workload for interpretation.
Solution Approach 2:
The patent employs color-coded visualization where different colors represent different clusters or substance distributions. This visual encoding makes it easy for users to distinguish between clusters and understand the spatial distribution of different substances without having to manually analyze complex data sets, thereby maintaining both ease of operation and reliable cluster discrimination.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach significantly reduces calculation time and enhances the ability to accurately locate and compare areas with similar or different distributions of substances, improving the efficiency and reliability of substance analysis across multiple samples.
Implementation Method 1
an ion source employing a matrix-assisted laser desorption/ionization method
Implementation Method 2
matrix-assisted laser desorption/ionization method
Data Source
AI summary
A measurement section (1) performs a mass spectrometric analysis for each micro area within a measurement area on a sample. A dimension reduction processor (23) performs data processing by non-linear dimension reduction using manifold learning on mass spectrometric data for each micro area, to obtain, for each micro area, a set of data reduced to three dimensions from the dimensions corresponding to the number of mass-to-charge-ratio values. A display color determiner (24) determines a color for each of the points corresponding to the data of the micro areas after the dimension reduction, by arranging those points within a three-dimensional space having three axes representing the three dimensions, with three primary colors respectively assigned to the three axes. A segmentation image creator (25) creates a segmentation image corresponding to the measurement area or a partial area in the measurement area, by arranging, on two dimensions, pixels which respectively correspond to the points within the three-dimensional space, where each pixel has a color given to the point corresponding to the pixel and is located according to the position within the measurement area of the micro area corresponding to the point.