Flow Cytometry Data Analysis Using Multidimensional Image Cube
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Solution Overview
Problem
Current flow cytometry data analysis methods face challenges in efficiently analyzing multiple features or markers without pre-analytical feature selection, leading to decreased performance as the number of vectors or features increases, particularly in supervised learning applications.
Innovation Solution
The implementation of a multidimensional cube engine and machine-vision function, combined with a genetic algorithm, allows for the analysis of multiple features or markers in a single training session, eliminating the need for pre-analytical feature selection and enhancing the understanding and prediction of multiple parameter data sets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional flow cytometry data analysis methods are used, then the analysis process is simpler, but the performance decreases as the number of vectors or features increases
Solution Approach 1:
The patent transforms flow cytometry data into a multidimensional image cube representation, adding spatial dimensions to the feature space. This dimensional transformation allows the system to handle high-dimensional data more effectively by organizing it in a structured visual format that preserves relationships between features while enabling more efficient analysis.
Solution Approach 2:
The patent replaces conventional statistical and machine learning analysis methods with a machine vision-based approach. By substituting traditional computational algorithms with image processing techniques, the system achieves better performance in analyzing multiple features simultaneously without the performance degradation that occurs in conventional methods.
2Measurement precision
If pre-analytical feature selection is performed, then the analysis complexity is reduced, but important features may be lost and accuracy decreased
Solution Approach 1:
The patent creates a universal analysis framework that can handle all features simultaneously without requiring pre-selection. The multidimensional image cube representation and machine vision approach are designed to process the complete feature set, making the system universally applicable to different flow cytometry datasets without needing to tailor feature selection for each case.
Solution Approach 2:
The patent performs preliminary organization of all features into a structured multidimensional image cube before analysis begins. This pre-organization preserves all features in a manageable format, allowing the subsequent machine vision analysis to efficiently process the complete dataset without needing to select or discard any features.
3Loss of information
If multiple features are analyzed simultaneously, then comprehensive understanding is achieved, but computational requirements and complexity increase
Solution Approach 1:
The patent organizes multiple features into a multidimensional image cube structure, where features are arranged in spatial dimensions. This transformation converts complex high-dimensional data into a structured visual representation that can be processed more efficiently by machine vision algorithms, preserving information while reducing computational burden.
Solution Approach 2:
The patent creates a visual copy or representation of the flow cytometry data in the form of an image cube. This visual copy allows the system to analyze all features simultaneously through image processing techniques, which are computationally more efficient than traditional methods for handling high-dimensional data.
Data Source
AI summary
Disclosed are various embodiments for interpretation of flow cytometry data. Flow cytometry data sets are combined to form a multidimensional image cube. The machine learning functions and genetic algorithm interpret the multidimensional image cube to produce a confidence value that is assigned to a function for the targeted condition. The confidence value determines the relative degree to which a targeted condition is present or absent in a flow cytometry data set. Such confidence value can be used to diagnose and interpret results from flow cytometry data.