Automated Flow Cytometry Classification Beyond Manual Gates
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Solution Overview
Problem
Existing flow cytometry data analysis methods lack reproducibility and generalizability due to varying user-defined gates, often leading to the misclassification of debris and multiplets, which are undesirable analytes.
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 based on analyte features like size, scatter, and fluorescence.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual gate methods are used for flow cytometry data classification, then user flexibility in defining classification criteria is improved, but classification accuracy and reproducibility deteriorate due to varying user-defined gates
Solution Approach 1:
The system performs automated classification of flow cytometry data using machine learning models that independently analyze analyte features without requiring manual gate definition by users. The classifier automatically identifies debris, single cells, and multiplets based on trained patterns from training datasets, eliminating human variability while maintaining operational simplicity through automated pipelines.
Solution Approach 2:
The patent replaces the mechanical/manual process of gate drawing and classification with an automated computational system. Machine learning classifiers (including random forest, support vector machines, and neural networks) substitute for manual user operations, using algorithms to automatically classify particles based on optical properties and feature patterns learned from training data.
2Ease of operation
If manual gate methods are used for flow cytometry data classification, then ease of operation is improved, but reliability deteriorates due to misclassification of debris and multiplets
Solution Approach 1:
The automated classification system independently and consistently applies learned patterns to classify flow cytometry data without human intervention. The system reliably identifies debris, single cells, and multiplets by analyzing optical properties and comparing them against trained classification models, ensuring reproducible results across different users and experiments.
Solution Approach 2:
The system uses training datasets with known ground truth classifications to train and validate classification models. Performance metrics are calculated by comparing automated classifications against reference standards, providing feedback for model optimization and ensuring high reliability in identifying target analytes while excluding debris and multiplets.
3Measurement precision
If automated classification using machine learning models is implemented, then classification accuracy is improved, but device complexity increases
Solution Approach 1:
The patent implements a universal automated classification platform that handles multiple flow cytometry data types and classification scenarios through a single machine learning system. The same computational infrastructure processes various analyte features (optical properties, fluorescence intensities, particle sizes) and applies appropriate classification algorithms, eliminating the need for separate manual gating procedures for different particle types.
Solution Approach 2:
The system introduces computational intermediaries (software modules, data processing pipelines, and algorithmic layers) that bridge the gap between raw flow cytometry data and final classifications. These intermediary computational components automatically perform feature extraction, normalization, and classification, masking the underlying complexity from end users while delivering high accuracy results.
4Productivity
If automated classification using machine learning models is implemented, then productivity is improved through automation, but device complexity increases due to computational requirements
Solution Approach 1:
The automated classification system operates independently without requiring manual gate definition or iterative adjustment by users. The machine learning models automatically process flow cytometry datasets, apply learned classification rules, and generate results in high-throughput mode, dramatically increasing productivity while the computational complexity is encapsulated within the automated software pipeline.
Solution Approach 2:
The system performs preliminary training of classification models using training datasets before actual classification tasks. This preliminary action establishes the computational framework and learned patterns in advance, allowing the automated system to efficiently process subsequent datasets without requiring complex real-time computations during data analysis, thus improving productivity.
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
Improves classification accuracy and efficiency by up to 20% compared to manual gate methods, enhancing the reproducibility and reliability of flow cytometry data analysis.
Implementation Method 1
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
Implementation Method 3
The drops containing particles of interest are electrically charged and deflected into a collection tube by passage through an electric field
Data Source
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.