Flow Cytometry Analyte Classification with Decision Trees and k-NN
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Solution Overview
Problem
Existing flow cytometry data analysis methods lack consistency and reproducibility due to varying user-defined classification processes, often leading to the removal of desirable data points such as single cells and multiplets, which are considered 'junk' or outliers.
Innovation Solution
Implementing a computer-implemented method using a decision tree ensemble and a distance-based classification model, such as a random forest classification model combined with a k-nearest neighbors classifier, to automate the classification of analyte data, refining predicted classes using various distance metrics to improve accuracy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual user-defined classification processes are used for flow cytometry data analysis, then users can customize classification criteria, but classification consistency and reproducibility deteriorate due to varying user approaches
Solution Approach 1:
The system performs automated classification of flow cytometry data using machine learning models that independently analyze and categorize particles without requiring manual user intervention. The algorithm automatically identifies and removes debris and multiplets while preserving single cells, eliminating the variability introduced by different user classification approaches while maintaining adaptability through configurable parameters.
2Ease of manufacture
If manual classification methods are used to remove debris and multiplets, then data cleanup can be performed, but desirable data points such as single cells and multiplets are incorrectly removed
Solution Approach 1:
The patent replaces manual visual inspection and gate drawing with an automated machine learning classification system. The algorithm uses multiple features including pulse width, area, height, and intensity ratios to computationally distinguish single cells from debris and multiplets, significantly improving identification accuracy while maintaining ease of data cleanup through automated processing.
3Measurement precision
If comprehensive feature analysis is performed to improve classification accuracy, then classification precision increases, but processing time and computational complexity increase
Solution Approach 1:
The system performs preliminary feature extraction and normalization before classification, pre-processing the data to identify key discriminative features. The machine learning model is trained in advance on representative datasets, allowing it to quickly classify new data using learned patterns from multiple features without requiring exhaustive real-time analysis of all possible parameters.
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
Enhances classification accuracy by 1% or more, increases classification speed and efficiency, and provides a standardized approach for data cleanup, minimizing the removal of events of interest.
Implementation Method 1
The parameters measured using a flow cytometer typically include light at the excitation wavelength scattered by the particle in a narrow angle along a mostly forward direction, referred to as forward-scatter (FSC), the excitation light that is scattered by the particle in an orthogonal direction to the excitation laser, referred to as side-scatter (SSC)
Implementation Method 2
the light emitted from fluorescent molecules in one or more detectors that measure signal over a range of spectral wavelengths, or by the fluorescent dye that is primarily detected in that specific detector or array of detectors
Data Source
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AI summary
Computer-implemented methods of classifying analyte data are provided. Methods of interest include categorizing the analyte data based on analyte features associated therewith by generating a predicted class for the analyte data using a decision tree ensemble, and refining the categorized analyte data based on the analyte features and the predicted class using a distance-based classification model to classify the analyte data. Systems and non-transitory computer-readable storage media for carrying out the subject methods are also provided.