Imaging Mass Spectrometer ROI Subregion Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
In differential analysis of MS imaging data, compounds with insignificant differences obscure those with significant differences, making it difficult to perform accurate comparisons between samples.
Innovation Solution
Divide regions of interest into subregions on multiple samples to calculate individual and general index values, reflecting the similarity or difference in mass-to-charge ratio expression, allowing for efficient detection of compounds with differential distribution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If differential analysis is performed on the entire region of interest, then the analysis covers all areas, but compounds with insignificant differences obscure those with significant differences, reducing detection accuracy
Solution Approach 1:
The region of interest is divided into multiple subregions, allowing differential analysis to be performed on smaller, more focused areas. This segmentation enables the detection of compounds with significant differences without being obscured by compounds with insignificant differences across the entire region.
Solution Approach 2:
Different subregions are analyzed independently to identify local differences in compound distribution. This approach allows the analysis to focus on specific areas where differential compounds are present, improving detection accuracy by considering local characteristics rather than treating the entire region uniformly.
2Reliability
If the entire region of interest is analyzed, then comprehensive coverage is achieved, but the analysis complexity and computational burden increase
Solution Approach 1:
By dividing the region of interest into subregions, the analysis is broken down into smaller, more manageable units. This reduces the computational complexity of each individual analysis while maintaining comprehensive coverage through the aggregation of results from all subregions.
Solution Approach 2:
The method performs differential analysis on each subregion separately rather than analyzing the entire region as a single unit. This partial action approach simplifies each analysis step while the combination of subregion results provides the comprehensive coverage needed for reliable detection.
Data Source
AI summary
In an imaging mass spectrometer for analyzing the same kind of samples using results of imaging mass spectrometric analysis performed on those samples, a measurement section (1) acquires mass spectrometric data by performing an analysis on each of the micro areas on a sample. A region-of-interest setter (32) sets an ROI on each sample, and divides each ROI into the same number of subregions each including the micro areas so that the subregions correspond to each other on the samples respectively covering roughly identical sites on the samples. An individual-index-value calculator (33) calculates an individual index value for each subregion, using mass spectrometric data acquired at the micro areas in the subregion, the individual index value reflecting a similarity or difference among the samples in terms of a degree of expression of each m/z value. A general-index-value calculator (34) calculates a general index value for each m/z value among the ROIs of the samples, using the individual index values calculated for the ink values for each subregion included in each ROI.