Mass Spectrometric Data Analyzer Iterative Marker Identification
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Solution Overview
Problem
Current methods for differential analysis of mass spectrometric data are inefficient and inaccurate in identifying marker candidates contributing to the separation between groups, often requiring manual confirmation and risking errors due to noise peaks and complex sample compositions.
Innovation Solution
A mass spectrometric data analyzer and program that generate a peak matrix, apply multivariate analysis, and allow users to visually confirm and exclude peaks, providing graphical representations for identifying and verifying marker candidates through a user-friendly interface.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional multivariate analysis (PCA/PLS-DA) is applied to peak matrix, then group separation can be visualized, but manual confirmation of marker candidates is required which reduces efficiency and increases error risk
Solution Approach 1:
The system performs automatic marker candidate identification and validation through iterative multivariate analysis. The analysis unit automatically excludes identified marker peaks from subsequent analysis, enabling the system to self-validate markers without manual intervention while maintaining high accuracy through the iterative verification process
Solution Approach 2:
The system implements feedback mechanisms where multivariate analysis results are used to identify marker candidates, which are then excluded from subsequent analysis rounds. This feedback loop automatically validates markers by confirming that their exclusion degrades group separation, eliminating the need for manual confirmation while maintaining accuracy
2Productivity
If all peaks are included in multivariate analysis, then comprehensive marker search is performed, but noise peaks and complex sample compositions reduce analysis accuracy
Solution Approach 1:
The system performs preliminary multivariate analysis on the complete peak matrix to identify potential marker candidates before conducting detailed validation. This preliminary screening separates promising peaks from noise peaks through statistical analysis, allowing subsequent focused analysis on high-probability candidates while maintaining comprehensive search coverage
Solution Approach 2:
The analysis unit extracts and excludes identified marker peaks from the peak matrix for subsequent analysis rounds. This extraction process separates true marker signals from noise by iteratively removing confirmed markers, allowing the system to maintain comprehensive search while improving accuracy through progressive elimination of validated findings
3Measurement precision
If iterative exclusion of marker peaks is performed, then validation accuracy is improved, but computational complexity increases
Solution Approach 1:
The system dynamically adjusts the peak matrix by excluding identified marker peaks from subsequent analysis iterations. This dynamic modification of the data structure enables iterative validation where each round focuses on remaining unvalidated peaks, improving accuracy while managing computational complexity through progressive refinement rather than exhaustive analysis
Data Source
AI summary
When a user inputs samples per group, a sample tree and a peak matrix are generated. Peak lists per group are shown in the sample tree, and m/z values and signal strength values from the peak lists are coordinates in the peak matrix. A multivariate analysis is applied to the generated peak matrix. The sample tree, peak matrix, score plot, and loading plot are displayed. When the user clicks a plotted point on the loading plot, a row indicating a corresponding peak on the peak matrix is discriminated. When the user deletes a checkmark corresponding to the discriminated row, the multivariate analysis is applied to the peak matrix from which the peak has been excluded. The score plot and other data are updated. When separation between groups is known from the score plot as failure, the excluded peak may be visually determined as a marker contributing to the group separation.