Matrix Decomposition for Multicellular Biomarker Correlation Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods, such as those described in Non Patent Literature 1, are unable to effectively analyze the correlation between multiple cell types, as they primarily focus on one-to-one relationships and do not fully utilize multicolored biomarker staining information.
Innovation Solution
An information processing device, biological sample analysis system, and method that acquire a fluorescence spectrum and position information from a biological sample, identify information regarding multiple biomarkers, and perform matrix decomposition processing to output the correlation between these biomarkers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If conventional clustering methods (hierarchical clustering, soft clustering) are used to analyze biomarkers, then the analysis process is simple, but cell position information is not included and information of multicolored biomarker staining is not fully utilized
Solution Approach 1:
The patent incorporates spatial position information as an additional dimension in the analysis. By representing cell interactions in a spatial context rather than just biomarker expression levels, the method preserves and utilizes position information that conventional clustering methods discard. This dimensional expansion allows full utilization of multicolored biomarker staining data combined with spatial coordinates.
2Loss of information
If one-to-one relationship analysis between certain cell and phenotypic cell is performed, then the analysis is straightforward, but correlation between multiple cell types cannot be obtained
Solution Approach 1:
The patent merges multiple one-to-one cell relationship analyses into a comprehensive many-to-many correlation analysis. By combining data from multiple biomarkers and multiple cell types into a unified analysis framework, the method extracts correlation information among all cell types simultaneously rather than analyzing pairs individually. This merging approach captures the complex interrelationships in the tumor microenvironment.
3Loss of information
If matrix decomposition processing is performed on multiple biomarkers, then correlation among multiple biomarkers can be obtained, but computational complexity increases
Solution Approach 1:
The patent applies matrix decomposition techniques (such as non-negative matrix factorization) to transform the original high-dimensional biomarker data into a lower-dimensional latent space. This parameter transformation reduces computational complexity by identifying underlying patterns and correlations without losing essential information. The decomposition process extracts meaningful factors that represent cell-cell interactions while reducing the computational burden of analyzing all biomarker combinations.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enables the analysis of correlations among multiple biomarkers, providing a more comprehensive understanding of cell interactions and improving the accuracy of biological sample analysis.
Implementation Method 1
an acquisition unit that acquires a fluorescence spectrum derived from a biological sample
Data Source
AI summary
An information processing device according to an aspect of the present disclosure includes an acquisition unit that acquires a fluorescence spectrum derived from a biological sample and position information of the biological sample from a sample including the biological sample, an identification unit (133b) that identifies, from the fluorescence spectrum, information regarding a plurality of different biomarkers of the biological sample associated with the position information of the biological sample, and a correlation analysis unit (133d) that performs matrix decomposition processing corresponding to a combination of the plurality of biomarkers on the information regarding the plurality of biomarkers and outputs a correlation of the information regarding the plurality of biomarkers.