Reconstructing Multidimensional Particle Clusters from 2D Projections
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Solution Overview
Problem
Classifying particle clusters in multidimensional feature spaces is challenging due to complex statistical distributions and high dimensions, leading to inaccurate results in 2D projections, especially for abnormal biological samples with overlapping particles.
Innovation Solution
A method and system for reconstructing multidimensional clusters by obtaining segmented 2D projections, identifying cross-relations among them, and grouping data points to form accurate multidimensional clusters, which helps recover data points obscured by overlap or shift in 2D projections.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If particle clusters are classified directly in multidimensional feature space, then comprehensive particle population classification is achieved, but classification accuracy deteriorates due to complex statistical distribution and high dimensions
Solution Approach 1:
The patent segments the multidimensional classification problem into multiple 2D projection analyses. Each 2D projection independently classifies particle clusters in that specific view, then the results are integrated through cross-relation analysis to achieve comprehensive multidimensional classification while maintaining accuracy in each segment.
Solution Approach 2:
The patent transforms the high-dimensional classification problem into multiple 2D projections by selecting different feature pairs. This dimensionality reduction allows accurate classification in each 2D space while the combination of multiple projections recovers the comprehensive multidimensional particle population information.
2Ease of operation
If 2D projections are used for particle population classification, then classification simplicity is improved, but classification accuracy deteriorates due to particle overlapping in the projection
Solution Approach 1:
The patent introduces cross-relations as an intermediary that connects multiple 2D projection results. The cross-relation analysis identifies which 2D clusters correspond to the same particle population across different projections, mediating between the simple 2D classifications and the accurate multidimensional population identification.
3Ease of operation
If multiple 2D projections are analyzed independently, then individual projection analysis simplicity is maintained, but global information loss occurs including cross-relations among 2D clusters
Solution Approach 1:
The patent merges the results from multiple independent 2D projection analyses by establishing cross-relations among the 2D clusters. This combination integrates the simple individual projections with the global multidimensional information, recovering particle populations that may be obscured in any single projection.
Data Source
AI summary
Systems and methods for multidimensional particle analysis data cluster mapping and reconstruction are provided. In one embodiment, a method for reconstructing multidimensional particle analysis data clusters is provided. The method includes obtaining a set of segmented two-dimensional projections corresponding to multidimensional particle analysis data associated with a biological sample of particles. Each segmented two-dimensional projection has two-dimensional clusters associated with particle populations in the biological sample. The method also includes reconstructing one or more multidimensional clusters based on the two-dimensional clusters in the segmented two-dimensional projections.