Automated Cell Classification Using Vector Coefficients
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for analyzing cell responses to specific markers in flow cytometry are subjective and difficult to reproduce, making results from different users or laboratories hard to compare.
Innovation Solution
An automated analysis method that determines a vector coefficient based on a reference sample and a sample to be analyzed, allowing for the calculation of a rate of false positives and classification of cells into positive and negative sets, ensuring reproducibility and comparability across different experts and laboratories.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual visual judgment is used to identify positive cells, then the analysis can be performed with simple equipment, but the results are subjective and difficult to reproduce
Solution Approach 1:
The patent replaces the mechanical/visual system of manual expert judgment with an automated computational system. The method uses mathematical algorithms to calculate a score for each cell based on marker expression levels, automatically classifying cells as positive or negative without requiring manual visual inspection. This substitution eliminates subjectivity while maintaining analytical capability.
Solution Approach 2:
The patent transforms the classification problem from a qualitative visual assessment into a quantitative parameter-based system. By defining a score calculated from marker expression levels and comparing it against a threshold, the method converts subjective visual judgment into an objective parameter-driven classification, improving reproducibility.
2Ease of operation
If manual threshold drawing is used to define positive cells, then the method is easy to operate, but it is difficult to compare results from different users or laboratories
Solution Approach 1:
The patent creates a universal classification method that can be applied consistently across different users, laboratories, and experiments. The automated scoring system uses the same mathematical algorithm and threshold criteria for all samples, ensuring that the classification process produces comparable results regardless of who performs the analysis or where it is conducted.
Solution Approach 2:
The patent replaces the inconsistent manual threshold drawing process with a replicated automated scoring algorithm. Instead of each expert independently drawing thresholds based on visual inspection, the same computational algorithm is applied to all datasets, ensuring identical classification criteria are used across all analyses.
3Productivity
If expert visual judgment is used for cell classification, then the analysis process is fast and simple, but the results lack robustness and standardization
Solution Approach 1:
The patent replaces the mechanical process of manual visual inspection with an automated computational system that calculates scores based on marker expression levels. This substitution maintains the speed of analysis while eliminating the variability inherent in human judgment, producing standardized and robust classification results.
Solution Approach 2:
The patent transforms the classification from a subjective visual process to an objective parameter-based system. By defining specific parameters (marker expression levels, calculated scores, and thresholds) that govern classification, the method ensures both speed and standardization, as the algorithm can rapidly process data while maintaining consistent criteria across all analyses.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The method provides a robust and reproducible analysis of cell responses, enabling the comparison of results from different scientists and reducing the variability inherent in biological tests by standardizing the classification process.
Implementation Method 1
Each antibody (αC) is specific to a given molecule (Mo) and is coupled to a given fluorescent probe (Sf). Thus, analyzing the fluorescence associated with a cell allows us to identify which molecules were produced by that cell.
Data Source
Figure 1~2
Figure 3~4
AI summary
The present invention relates to a method for analysing sample cells reacting with at least one specific marker, comprising: - a step of providing a reference sample and the sample to be analysed; - a step of providing a set (E+) of cells declared positive from among the sample cells to be analysed or a step of providing a rate of false positives (α) in the reference sample; - a step of determining a vector coefficient (θ) from the sample to be analysed and from the set (E+), or from the reference sample and the rate of false positives (α); - a step of determining at least one set of positive cells in the reference sample or in the sample to be analysed as a function of the vector coefficient (θ); and, accordingly, - a step of calculating a rate of false positives (α) in the reference sample from the number of positive cells of the reference sample.