Dynamic Sample Grouping for Mass Spectrometry Differential Analysis
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Solution Overview
Problem
Conventional differential analysis methods for mass spectrometric data require manual re-grouping of samples each time the empirical criterion changes, leading to inefficiency and potential errors, especially when dealing with large datasets or complex sample classifications.
Innovation Solution
A data analyzing device and program that allows operators to dynamically change grouping criteria based on various empirical information, automatically re-grouping samples and performing differential analysis, enabling efficient detection of markers related to specific properties or features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual re-grouping of samples is performed each time the empirical criterion changes, then the differential analysis can be performed on different grouping methods, but the analysis efficiency decreases and potential errors increase
Solution Approach 1:
The system pre-processes and stores empirical information for all samples in advance. When a user selects a different grouping criterion, the system automatically retrieves the pre-stored empirical information and performs re-grouping without manual intervention, thus maintaining adaptability while improving analysis efficiency
Solution Approach 2:
The patent introduces an automated computational system as an intermediary between the empirical criterion selection and the differential analysis. This intermediary automatically performs the re-grouping operation based on the selected criterion, eliminating manual errors and significantly improving productivity while preserving the ability to analyze multiple grouping methods
2Adaptability or versatility
If manual re-grouping of samples is performed each time the empirical criterion changes, then the differential analysis can be performed on different grouping methods, but the potential errors increase
Solution Approach 1:
The system is designed to automatically perform re-grouping operations based on empirically stored information without requiring manual intervention. The automated process eliminates human errors in sample classification while maintaining the flexibility to analyze multiple grouping methods, thus improving reliability without sacrificing adaptability
Solution Approach 2:
The system incorporates automated verification mechanisms that check the consistency and accuracy of sample grouping after re-grouping. This feedback loop ensures that the automated process maintains high reliability by detecting and correcting any potential errors in the grouping operation
3Ease of manufacture
If conventional differential analysis methods are used, then the process follows a standard procedure, but the operation complexity increases when dealing with large datasets or complex sample classifications
Solution Approach 1:
The patent divides the complex analysis process into distinct automated modules: empirical information retrieval, sample re-grouping based on selected criteria, and differential analysis execution. This segmentation allows the system to handle large datasets and complex classifications systematically, reducing operational complexity while maintaining standardization
Solution Approach 2:
The system creates a universal automated platform that can handle various grouping criteria and dataset sizes through a single integrated workflow. The multi-functional design eliminates the need for separate manual procedures for different analysis scenarios, reducing operational complexity while preserving procedural standardization
Data Source
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Figure 3A~3B
AI summary
A sample group forming section 24 classifies samples derived from microorganisms into groups according to empirical information showing the species or strain of each sample. A differential analysis section 27 performs a differential analysis using a peak matrix created based on the result of the grouping. An operator enters group rearrangement conditions concerning the drug resistance of microorganisms. Under the entered conditions, a sample group rearranging/rearrangement-cancelling section 25 rearranges the already formed groups by selecting or merging groups using another kind of previously registered empirical information which shows the drug resistance of each group. The differential analysis section 27 performs a differential analysis using a peak matrix newly created based on the result of the rearrangement of the groups. Thus, differential analysis results concerning the resistance to different drugs can be sequentially acquired as the group rearrangement condition is successively changed.