Earth Mover's Distance for Multivariate Flow Cytometry Analysis
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Solution Overview
Problem
Current methods for analyzing high-dimensional multi-sample experiments, such as flow cytometry, are limited by subjective, error-prone, and time-consuming manual gating techniques, which do not scale well to high-throughput settings and fail to accurately identify non-convex subpopulations.
Innovation Solution
The method employs a combination of density-based merging and Earth Mover's Distance algorithms to automatically and objectively analyze multivariate data by summarizing distributions into clusters and measuring differences using a cost factor indicative of separation between signatures, allowing for quantitative comparison of sample populations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual gating techniques are used to analyze flow cytometry data, then the analysis can be performed with simple tools and basic algorithms, but the process becomes subjective, error-prone, and time-consuming
Solution Approach 1:
The system performs automated gating by having the algorithm independently identify and characterize cell subpopulations without requiring manual intervention. The computational method automatically processes flow cytometry data, identifies clusters representing cell populations, and generates analysis results autonomously, eliminating the time-consuming manual gating process while maintaining or improving accuracy through objective, reproducible algorithms
Solution Approach 2:
The patent replaces the manual mechanical process of drawing gates on plots with a computational algorithm that automatically identifies cell subpopulations. The system uses computational clustering methods to detect and characterize cell populations in multivariate flow cytometry data, substituting human manual operation with automated computational analysis that is both faster and more consistent
2Extent of automation
If automated gating methods using k-means or mixture models are employed, then the analysis becomes objective and reproducible, but these methods fail to identify non-convex subpopulations
Solution Approach 1:
The patent employs density-based clustering methods that change the fundamental parameters used for cluster identification from centroid-based (k-means) or distribution-based (mixture models) to density-based approaches. This allows the algorithm to identify clusters of arbitrary shape by detecting regions of high data point density, enabling the identification of non-convex subpopulations that traditional methods miss while maintaining full automation
Solution Approach 2:
The system uses dynamic clustering algorithms that can adapt to various data distributions and subpopulation shapes. The density-based approach dynamically identifies clusters based on local data density patterns rather than assuming fixed geometric shapes or distribution types, allowing the automated gating to accommodate diverse and complex cell population structures
3Measurement precision
If manual gating is performed by experts, then complex non-convex subpopulations can be identified, but the process is difficult to reproduce and does not scale to high-throughput settings
Solution Approach 1:
The automated computational method performs the complex task of identifying non-convex subpopulations independently without requiring expert manual intervention. The algorithm processes flow cytometry data, identifies cell subpopulations of any shape, and generates results automatically, achieving both the precision of expert analysis and the high throughput needed for large-scale experiments
Solution Approach 2:
The patent extracts and codifies the expert knowledge and patterns used in manual gating into an automated computational algorithm. By capturing the essential logic of expert analysis in software, the system reproduces expert-level subpopulation identification at scale, eliminating the bottleneck of manual analysis while preserving the ability to detect complex patterns
Data Source
AI summary
A method and apparatus for quantitatively measuring differences between portions of a multivariate, multi-dimensional sample distribution, may comprise summarizing the data by dividing the data into clusters each having a signature representative of a position of the cluster and a fraction of the entire distribution within the cluster; matching a plurality of first supplier signatures to a respective one of a plurality of second receiver signatures using a cost factor indicative of the separation between first signature elements and second signature elements; and determining a measurement of the work required to transform the first signature to the second signature. The step of determining a measurement of the work may comprise applying the earth mover distance (“EMD”) algorithm between the first signature or elements of the first signature and the respective second signatures or elements of the respective second signature.