Mass Spectrometry Data Clustering for Isotope Peak Identification
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Solution Overview
Problem
Current mass spectrometry imaging data analysis methods are inefficient in identifying monoisotopic and isotope peaks from the same substance, requiring manual assessment and being prone to errors due to reliance on signal intensity and varying measurement conditions, leading to redundant data and high analysis time.
Innovation Solution
A method and device for analyzing mass spectrometry data that includes peak detection, intensity standardization, clustering, and identification of monoisotopic and isotope peaks by classifying spatial intensity distributions, eliminating the need for manual assessment and improving precision across varying measurement conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If peak detection is performed based on signal intensity alone, then peaks exceeding a certain intensity level are selected, but monoisotopic peaks and isotope peaks cannot be differentiated, resulting in redundant data
Solution Approach 1:
The patent changes the parameter basis for peak selection from signal intensity alone to a combination of signal intensity and spatial distribution characteristics. By introducing spatial distribution as an additional parameter, the system can differentiate between monoisotopic peaks and isotope peaks while maintaining efficient automated processing.
Solution Approach 2:
The patent adds a spatial dimension to peak analysis by examining the distribution of peak intensities across multiple measurement points. This transforms the problem from one-dimensional (intensity-based) to two-dimensional (intensity + spatial distribution), enabling differentiation of peaks that have similar intensities but different spatial patterns.
2Measurement precision
If conventional peak detection methods are used on mass spectrometry imaging data, then analysis time increases enormously due to processing each micro area separately, but this approach is necessary to handle the enormous amount of data
Solution Approach 1:
The patent merges the analysis of multiple micro area spectra by examining spatial distribution patterns across all measurement points simultaneously. Instead of processing each spectrum independently and then coordinating results, the method combines information from all spectra to identify peaks based on their spatial characteristics, significantly reducing processing time while maintaining accuracy.
Solution Approach 2:
The patent creates a universal peak identification approach that works across all micro areas by identifying spatial distribution patterns. The method uses clustering analysis to find peaks with similar spatial patterns across the entire imaging region, making the peak detection process applicable to the whole dataset rather than requiring separate processing for each location.
3Measurement precision
If intensity ratios of isotope peaks are compared with theoretical values, then monoisotopic and isotope peaks can be identified, but this method cannot be applied when measured intensity ratios do not match theoretical predictions
Solution Approach 1:
Instead of comparing measured intensity ratios with theoretical values, the patent inverts the approach by comparing spatial distribution patterns of peaks. Rather than asking whether the intensity ratio matches theory, the method asks whether the spatial distribution pattern is consistent across measurements, which remains valid even when intensity ratios deviate from theoretical predictions.
4Measurement precision
If manual assessment of mapping image similarity is performed to identify isotope peak groups, then isotope peaks can be differentiated, but the process involves individual differences and lacks stability and objectivity
Solution Approach 1:
The patent replaces manual visual assessment with automated computational methods. Instead of relying on human analysts to visually compare mapping images, the system uses clustering algorithms to automatically identify peaks with similar spatial distribution patterns, eliminating subjectivity and individual differences while maintaining the ability to differentiate isotope peaks.
Data Source
AI summary
A data matrix in which pixel numbers are assigned to the vertical direction, m/z values are assigned to the horizontal direction, and intensity values are used as terms is generated from data obtained by peak detection (S3), and after the standardization of the data is executed so that the norms of the intensities of the pixel space are set to 1 for each m/z, the peaks (m/z values) are classified into a plurality of clusters by performing clustering in the m/z direction (S5 and S6). Since the probability of isotope peaks or adduct ion peaks derived from the same substance being consolidated into the same cluster increases, unnecessary peaks can be accurately removed by removing unnecessary isotope ion peaks and the like using m/z differences or intensities in the clusters (S7 and S8).