Flow Cytometry Analyte Data Classification via Regression
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Solution Overview
Problem
Current flow cytometry data classification methods are prone to human error, leading to incorrect classification of singlets and variability between users, which hinders reproducibility and accuracy, especially in distinguishing debris and multiplets.
Innovation Solution
An automated method using a computer-implemented process that applies a regression model to determine relationships between analyte features and a cluster criterion, generating a sparse set of features to classify data into clusters, employing algorithms like Gaussian mixture models, DBSCAN, BIRCH, or K-means clustering.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual gating methods are used for classifying flow cytometry data, then users can visually inspect and classify data points, but human error leads to incorrect classification of singlets and variability between users
Solution Approach 1:
The patent replaces the manual mechanical gating process with an automated computational classification system. The system uses algorithms to automatically classify flow cytometry events into singlet and non-singlet clusters, eliminating human variability and error while improving both classification accuracy and reproducibility across different users and laboratories.
Solution Approach 2:
The classification system performs self-service by automatically determining cluster criteria and classifying events without requiring manual user intervention. The system independently identifies singlet and non-singlet populations based on the learned cluster criteria, making the classification process autonomous and consistent.
2Productivity
If manual gating is used to distinguish singlets from debris and multiplets, then classification can be performed, but it is time-consuming and prone to user variability
Solution Approach 1:
The system performs preliminary action by pre-learning the cluster criteria from training data before actual classification. This pre-processing step establishes the decision boundaries for singlet and non-singlet classification, enabling rapid and accurate automated classification without manual gating during the actual analysis phase.
3Reliability
If automated classification algorithms are implemented, then reproducibility and accuracy improve, but the complexity of the system increases
Solution Approach 1:
The patent introduces an intermediary computational layer that bridges the raw flow cytometry data and the final classification results. This intermediary system includes components for feature extraction, cluster criterion determination, and event classification, which collectively manage the complexity while providing reproducible and accurate results.
4Measurement precision
If more features are used in classification, then accuracy improves, but the computational complexity and data processing requirements increase
Solution Approach 1:
The system extracts and utilizes multiple relevant features from flow cytometry data including forward scatter, side scatter, and fluorescence intensities across multiple channels. By extracting and analyzing these specific features, the system achieves high classification accuracy while managing computational complexity through focused feature selection rather than processing all possible data dimensions.
Data Source
AI summary
Computer-implemented methods of classifying analyte data are provided. Methods of interest include applying a regression model to determine a relationship between an initial set of analyte features and a cluster criterion, generating a sparse set from at most a portion of the initial set of the analyte features based on the relationship, generating a classification model based on the sparse set, and applying the classification model to classify the analyte data into the clusters. Systems and non-transitory computer-readable storage media configured to carry out the subject methods are also provided.