Radial Density Histograms for Automated Cluster Boundary Definition
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Solution Overview
Problem
Current methods for identifying clusters in multidimensional data, particularly in flow cytometry, face challenges in accurately and automatically defining cluster boundaries in high-dimensional spaces, often resulting in discontinuous gates and requiring manual intervention.
Innovation Solution
The method of radial gating, which segments the data space into radial segments, generates radial density histograms, and constructs a connected polygonal gate around clusters, allowing for automated identification of populations in multidimensional data without user interaction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated gating methods are used to identify clusters in multidimensional data, then productivity is improved, but measurement precision deteriorates due to discontinuous gates and inaccurate boundary definition
Solution Approach 1:
The patent divides the multidimensional data space into multiple 2D projection planes, allowing automated gating to be performed independently on each plane. This segmentation enables the system to process complex high-dimensional data through simpler 2D analyses while maintaining accuracy through the integration of results across multiple projections.
Solution Approach 2:
The patent transforms the cluster boundary identification problem from high-dimensional space into multiple 2D projection planes. By projecting data onto 2D planes and performing gating operations in these lower-dimensional spaces, the system achieves accurate boundary definition while maintaining automation. The intersection of gates from multiple projections defines the final cluster boundaries in the original high-dimensional space.
2Measurement precision
If manual gating methods are used to define cluster boundaries, then measurement precision is improved, but productivity deteriorates due to requiring manual intervention
Solution Approach 1:
The patent implements an automated system that performs cluster identification and gate definition without requiring manual user input for each data set. The algorithm automatically projects data onto 2D planes, identifies clusters, defines gates, and integrates results across multiple projections. This self-service capability enables batch processing of multiple data sets while maintaining measurement precision equivalent to manual gating.
3Productivity
If traditional automated gating methods are used, then productivity is improved, but device complexity increases due to handling high-dimensional data
Solution Approach 1:
The patent segments the complex high-dimensional data processing task into manageable 2D projection operations. By dividing the multidimensional space into multiple 2D planes and performing gating independently on each plane, the system reduces the computational complexity of handling high-dimensional data while maintaining the ability to process complex data sets automatically.
Solution Approach 2:
The patent simplifies the processing of high-dimensional data by transforming it into multiple 2D projection planes. This dimensionality reduction approach converts a complex high-dimensional problem into several simpler 2D problems that can be solved using well-established automated gating algorithms, thereby reducing overall system complexity while maintaining automation.
Data Source
AI summary
Apparatuses and methods for determining population boundaries are described. In one embodiment, population boundaries are determined using radial density histograms.