Probabilistic State Index for Multidimensional Cytometry Data
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Solution Overview
Problem
Current methods for analyzing multidimensional data, particularly in cytometry, face limitations such as information loss during visualization, scalability issues, gating errors, and difficulties in integrating data from multiple samples, leading to challenges in understanding cellular differentiation and maturation.
Innovation Solution
The introduction of a state index based on probabilities allows for the parametric definition of all data parameters, eliminating the need for gating and enabling the display of all parameter correlations through simple stacked graphics, while accounting for population overlap and handling multiple samples coherently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If conventional visualization methods (histograms and bivariate displays) are used to analyze multidimensional cytometry data, then the data can be displayed in lower dimensional views, but information is lost in an uncontrollable manner
Solution Approach 1:
The patent introduces a state index as an additional dimension that organizes multidimensional cytometry data. Instead of projecting data into lower dimensions (which causes information loss), the invention adds a state index dimension that captures the probabilistic state of each cell across multiple parameters, allowing full-dimensional information to be preserved while enabling structured visualization and analysis.
2Productivity
If gating methods are used to classify data into populations, then hierarchical classification can be achieved, but gating errors compound and reduce reliability
Solution Approach 1:
The patent replaces the mechanical gating approach (manual or automated threshold-based classification) with a probabilistic state model. Instead of using rigid gates that create binary in/out classifications prone to compounding errors, the invention uses probability distributions to model cell states, allowing for more reliable and nuanced classification that accounts for biological variability and measurement uncertainty.
3Quantity of substance
If multiple samples are analyzed using conventional methods, then data from different samples can be collected, but integrating and visualizing data from multiple samples becomes difficult
Solution Approach 1:
The patent creates a universal state index framework that can accommodate multiple samples within a single coherent model. The state index provides a common reference system that works across different samples, allowing data from multiple samples to be integrated and compared systematically. This universal approach eliminates the need for separate analysis pipelines for each sample and enables coherent multi-sample visualization.
4Adaptability or versatility
If parameter scalability is increased to accommodate more cytometry parameters, then more cellular characteristics can be analyzed, but the number of required two-parameter histograms increases as m*(m-1)/2
Solution Approach 1:
The patent introduces the state index as an additional dimension that reorganizes the visualization of multidimensional data. Instead of requiring m*(m-1)/2 separate two-parameter histograms to capture all parameter relationships, the state index provides a unified framework where all parameters are integrated into a single probabilistic model, dramatically reducing visualization complexity while maintaining full parameter scalability.
Data Source
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AI summary
The present invention provides a method of analyzing multidimensional data using a computer as well as methods of displaying multidimensional data to a user for further analysis. In other aspects the present invention provides for a system for state model fitting, the system comprising a detector, and a computer operably connected to the detector, wherein the computer accesses one or more logic instructions for receiving raw data from the computer, and generating a state model of the raw data.