Mass Spectrometric Data Analyzer Marker Peak Identification
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Solution Overview
Problem
The existing methods for identifying microorganisms using mass spectrometry are burdensome and time-consuming, especially when dealing with a large number of groups, as they require extensive sample preparation and analysis for each sample.
Innovation Solution
A mass spectrometric data analyzer and program that group peak-intensity values and determine significant differences by analyzing peak-intensity distributions for conformity to probability distributions, such as normal or lognormal distributions, to identify marker peaks with high accuracy, even with a small number of samples per group.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional difference analysis procedures are used to identify marker peaks, then measurement precision is improved, but loss of time and productivity deteriorate due to extensive sample preparation and analysis requirements
Solution Approach 1:
The invention extracts only the essential information needed for marker peak identification by directly analyzing peak intensity distributions from mass spectrometric data, eliminating unnecessary intermediate steps such as extensive sample preparation and conventional univariate analysis procedures. The system focuses specifically on comparing peak intensity distributions between control and test samples to identify significant differences.
Solution Approach 2:
The invention replaces the mechanical/manual process of conventional difference analysis with an automated information processing system that directly compares peak intensity distributions using statistical methods. The system automatically calculates p-values and identifies marker peaks without requiring manual sample preparation or step-by-step conventional analysis procedures, thereby reducing time loss while maintaining measurement precision.
2Reliability
If the number of samples per group is increased to improve statistical reliability, then reliability of difference analysis is improved, but productivity and ease of operation deteriorate due to increased burden of sample preparation and analysis
Solution Approach 1:
The invention performs preliminary organization of mass spectrometric data into peak intensity distributions before statistical analysis. By pre-processing the data to group peak intensities by mass-to-charge ratio and sample group, the system prepares the information in a format ready for immediate statistical comparison, eliminating the need to process additional samples to achieve reliable results.
Solution Approach 2:
The invention creates a virtual representation of the sample groups through peak intensity distributions and statistical models (p-value calculations). Instead of requiring physical replication of numerous samples to ensure reliability, the system uses statistical copying through distribution analysis to achieve reliable difference detection with fewer physical samples, thereby maintaining reliability while improving productivity.
3Measurement precision
If conventional univariate analysis procedures are used for each peak, then measurement precision is improved, but device complexity and ease of operation worsen due to the complex and time-consuming analysis process
Solution Approach 1:
The invention merges the analysis of multiple peaks by organizing peak intensities into distributions grouped by mass-to-charge ratio and sample group. Instead of analyzing each peak independently through separate univariate tests, the system combines peak intensity data into comprehensive distributions that can be statistically compared between control and test samples, simplifying the analytical process while maintaining detection accuracy.
Solution Approach 2:
The invention creates a universal analysis framework where peak intensity distributions serve multiple functions: organizing data by mass-to-charge ratio, enabling statistical comparison between groups, and identifying marker peaks. This multi-functional approach replaces the need for separate univariate analysis procedures for each peak, reducing device complexity and improving ease of operation while preserving measurement precision through consistent statistical evaluation.
Data Source
AI summary
[Problem to be Solved]To select a marker peak which characterizes a difference between groups, even when the number of samples belonging to each group is small.[Solution]A peak matrix is created based on the peaks detected from mass spectra of a plurality of samples belonging to a plurality of groups (S1-S3). Each row of the peak matrix represents a peak-intensity distribution for a large number of samples at one mass-to-charge-ratio value. If there is no difference between the groups at a certain mass-to-charge-ratio value, the peak-intensity distribution at that mass-to-charge-ratio value should be a lognormal distribution (or normal distribution). Accordingly, a hypothesis test for the conformity of the peak-intensity distribution to the lognormal distribution is performed for each mass-to-charge-ratio value (S5). A mass-to-charge-ratio value at which a significant difference has been found is selected as a candidate of the marker peak (S6).