Principal Component Analysis Variable Grouping for Mass Spectrometry Data
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Solution Overview
Problem
The high dimensionality of mass spectrometry data from a large number of samples poses challenges for data processing, limiting the use of techniques like independent component analysis and linear discriminant analysis and requiring significant computer resources.
Innovation Solution
The implementation of principal component analysis (PCA) followed by variable grouping, which reduces dimensionality and identifies correlated variables, allowing for efficient processing and interpretation of mass spectrometry data through the creation of group representations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If principal component analysis followed by variable grouping is applied, then dimensionality is reduced and processing efficiency is improved, but information loss may occur during dimensionality reduction
Solution Approach 1:
The patent segments the high-dimensional variable space into multiple clusters or groups of correlated variables. Each cluster represents a subset of variables that are highly correlated with each other, allowing the system to process data in manageable segments rather than as a single high-dimensional entity, thus improving processing efficiency while preserving the correlation structure within each segment
Solution Approach 2:
The patent merges highly correlated variables into representative variables or clusters. By combining variables that convey similar information, the system reduces dimensionality while maintaining the essential information content, as the merged variables capture the collective behavior of the original correlated variables
2Measurement precision
If high dimensionality data is processed using traditional methods, then comprehensive analysis is achieved, but significant computer resources are required and processing time increases
Solution Approach 1:
The patent extracts and removes redundant information from the high-dimensional data by identifying and eliminating highly correlated variables. By extracting only the essential, non-redundant variables that contribute uniquely to the analysis, the system maintains comprehensive analysis capability while significantly reducing the computational burden on processing systems
3Measurement precision
If high dimensionality data is processed using traditional methods, then complete variable analysis is possible, but processing time increases and timely processing becomes difficult
Solution Approach 1:
The patent performs preliminary variable grouping and dimensionality reduction before conducting the main analysis. By pre-organizing variables into correlated clusters and selecting representative variables in advance, the system prepares the data structure to enable faster subsequent processing while ensuring that no important variable relationships are lost, thus achieving both completeness and timeliness
Data Source
AI summary
Groups of correlated representations of variables are identified from a large amount of spectrometry data. A plurality of samples is analyzed and a plurality of measured variables is obtained from a spectrometer. A processor executes a number of steps. The plurality of measured variables is divided into a plurality of measured variable subsets. Principal component analysis followed by variable grouping (PCVG) is performed on each measured variable subset, producing one or more group representations for each measured variable subset and a plurality of group representations for the plurality of measured variable subsets. While the total number of the plurality of group representations is greater than a maximum number, the plurality of group representations is divided into a plurality of representative subsets and PCVG is performed on each subset. PCVG is performed on the remaining the plurality of group representations, producing a plurality of groups of correlated representations of variables.