Cytometric Data Segmentation via Density-Based Hierarchical Clustering
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Solution Overview
Problem
Current cytometry techniques face challenges in automated data analysis, particularly in achieving reliable, reproducible, and understandable segmentation of complex multidimensional datasets, due to issues with correlation with manual segmentation, simultaneous consideration of all dimensions, hierarchical segmentation, and explainability and robustness of algorithms.
Innovation Solution
A computer-implemented method for analyzing cytometric data that determines density inversely proportional to the sum of distances between points, segments the data into modal segments, and constructs a hierarchical structure based on persistence, allowing for robust and interpretable segmentation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated segmentation algorithms are used, then productivity is improved, but reliability deteriorates due to lack of correlation with manual segmentation
Solution Approach 1:
The method incorporates feedback mechanisms where the automated segmentation results are continuously compared with manual segmentation ground truth, and the algorithm parameters are adjusted based on this feedback to improve correlation and reliability while maintaining high productivity
Solution Approach 2:
The patent replaces manual mechanical segmentation processes with automated computational algorithms that use density-based clustering and hierarchical organization, achieving both higher productivity and improved reliability through deterministic mathematical operations
2Reliability
If manual segmentation is used, then reliability is maintained, but productivity deteriorates due to time-consuming analysis
Solution Approach 1:
The automated system performs self-service by executing deterministic density calculations and hierarchical clustering operations independently, eliminating the need for manual intervention while maintaining segmentation quality through mathematically rigorous algorithms
3Measurement precision
If all dimensions are considered simultaneously, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The method segments the high-dimensional data space into hierarchical levels, where at each level a subset of dimensions is considered. This segmentation of the analysis process allows precise multidimensional measurement while reducing algorithmic complexity by processing dimensions in organized stages rather than simultaneously
4Measurement precision
If complex algorithms are used, then measurement precision is improved, but ease of operation deteriorates due to lack of explainability
Solution Approach 1:
The patent introduces a hierarchical dimension that organizes algorithmic operations into interpretable levels. Each level represents a stage of dimension consideration, making the complex algorithm transparent and explainable while maintaining high measurement precision through systematic processing of all dimensions
Data Source
AI summary
A device and a computer-implemented method for analyzing a dataset associated with a plurality of biological objects selected from cells, cell-derived vesicles, acellular microorganisms, and/or biofunctionalized materials; the dataset including N cytometric events, each associated with a biological object, each cytometric event being defined by at least two cytometric parameters measured for the corresponding biological object so that the dataset is represented by a cloud of N points in a D-dimensional space; the device and method being configured to output at least the hierarchical structure representing the different classes of biological objects and their mutual relationships.