Flow Cytometry Singlet Classification With Density Threshold Gating
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Solution Overview
Problem
Current flow cytometry data analysis methods for distinguishing singlets from multiplets and debris are subjective, user-dependent, and prone to errors, leading to reduced reproducibility and increased workflow complexity.
Innovation Solution
Implement a multiparameter analysis using a distance-based classification model and density-based clustering algorithm, such as DBSCAN, to automatically distinguish singlets from multiplets and debris based on size-based analyte features, enhancing the accuracy and reproducibility of data classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual gating methods are used for singlet discrimination, then user flexibility is maintained, but accuracy and reproducibility deteriorate due to subjectivity and user-dependency
Solution Approach 1:
The patent replaces the manual mechanical gating process with an automated computational classification system. The flow cytometry data is processed using computer-implemented algorithms that automatically classify events as singlets, multiplets, or debris based on measured parameters, eliminating subjective user dependency while improving accuracy and reproducibility.
Solution Approach 2:
The classification system performs self-service by automatically analyzing flow cytometry data without requiring manual user intervention for gating. The system independently processes the data, applies classification algorithms, and generates results, thereby reducing workflow complexity while maintaining or improving discrimination accuracy.
2Reliability
If automated classification is implemented, then reproducibility and accuracy improve, but device complexity increases due to additional processing requirements
Solution Approach 1:
The patent implements a universal classification system that can handle multiple types of flow cytometry data and parameters through a single automated platform. The system is designed to process various analyte types and measurement parameters using the same computational framework, improving reproducibility without proportionally increasing complexity through standardized multi-functional processing.
Solution Approach 2:
The system improves reliability by systematically analyzing multiple measured parameters simultaneously rather than relying on single-parameter manual gating. The automated classification evaluates combinations of parameters (such as area, height, width, circularity) to distinguish singlets from multiplets and debris, enhancing reproducibility through consistent multi-parameter assessment.
3Measurement precision
If multiparameter analysis is used, then discrimination accuracy improves, but computational requirements and processing time increase
Solution Approach 1:
The patent applies preliminary action by pre-defining classification thresholds and decision rules based on parameter combinations before data analysis. The system establishes criteria for singlet, multiplet, and debris classification in advance, allowing rapid automated processing of flow cytometry data without requiring complex real-time computational decisions, thus improving accuracy while minimizing processing time.
Data Source
AI summary
Aspects of the present disclosure include methods for classifying analyte data. Methods according to certain embodiments include applying a distance-based classification model to determine a density distinguishing threshold in a size-based analyte feature space, applying a density-based clustering algorithm to separate the analyte data into a high-density cluster and a low-density cluster based on the density threshold and classifying the analyte data based on the high-density cluster and the low-density cluster based on the size-based analyte feature space. Systems and non-transitory computer-readable storage media configured to carry out the subject methods are also provided.