Flow Cytometry Singlet Discrimination Using Density-Based Clustering
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Solution Overview
Problem
Current flow cytometry data analysis methods for singlet discrimination are subjective and prone to user-to-user variability, leading to reproducibility issues and increased workflow complexity, particularly in distinguishing singlets from multiplets and debris.
Innovation Solution
A multiparameter analysis using a distance-based classification model and density-based clustering algorithm, such as DBSCAN, is employed to objectively and automatically distinguish singlets from multiplets and debris by analyzing multiple features like light loss, axis moments, and radial moments, enabling consistent and accurate classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual gating methods are used for singlet discrimination, then user flexibility in data analysis is maintained, but user-to-user variability increases leading to reduced reproducibility
Solution Approach 1:
The system performs automatic singlet discrimination using computational algorithms that analyze multiple parameters simultaneously. The algorithm independently identifies and classifies singlets without requiring manual user intervention or gating, thereby eliminating user-to-user variability while maintaining analytical rigor through objective, reproducible computational criteria
Solution Approach 2:
The patent replaces the manual mechanical gating process with an automated computational algorithm. Instead of users manually drawing gates on plots to distinguish singlets from multiplets, the system uses computer-based algorithms that automatically analyze multiple parameters and classify events, substituting human manual operation with automated computational processing to improve reproducibility
2Device complexity
If manual gating is used to distinguish singlets from multiplets, then workflow simplicity is maintained, but workflow complexity increases due to subjective analysis requirements
Solution Approach 1:
The algorithm transitions from traditional two-dimensional manual gating to multi-dimensional automated analysis by simultaneously evaluating multiple parameters including light loss, axis moments, and radial moments. This dimensional expansion enables more precise classification of singlets from multiplets and debris through comprehensive multi-parameter assessment rather than limited two-dimensional visual inspection
Solution Approach 2:
The system changes from manual visual assessment to automated computational analysis by implementing algorithms that objectively evaluate multiple parameters. The algorithm processes light loss, axis moments, and radial moments through computational operations, transforming subjective manual gating into objective parameter-based classification that improves precision while managing workflow complexity
3Measurement precision
If traditional flow cytometry analysis is used, then processing speed is maintained, but accuracy of singlet discrimination is reduced due to subjective manual gating
Solution Approach 1:
The automated algorithm processes flow cytometry data continuously without interruption by automatically analyzing each event as it is acquired. The computational algorithm operates in real-time, continuously evaluating multiple parameters and classifying singlets without requiring pauses for manual gating adjustments, thereby maintaining processing speed while improving discrimination accuracy through consistent automated analysis
Solution Approach 2:
The patent replaces manual gating operations with automated computational processing that occurs at high speed. The algorithm rapidly evaluates multiple parameters for each event and immediately classifies singlets, replacing the slower manual visual inspection process with high-speed computational analysis that improves both accuracy and processing efficiency
Data Source
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AI summary
Aspects of the present disclosure include methods for classifying analyte data. Methods according to the disclosure 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.