Automated Flow Cytometry Gate Labels from Population Metrics
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Solution Overview
Problem
Current flow cytometry data analysis methods struggle with unintuitive and uninformative gate and population labels, leading to difficulties in data interpretation, longer analysis times, and confusion when sharing data between users.
Innovation Solution
Automated processes for generating clear, informative, and descriptive flow cytometry gate and particle population labels by calculating metrics based on parameter magnitudes and predetermined thresholds, using geometric and data distribution approaches.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated gating methods are used to identify particle populations, then analysis time is reduced, but the generated labels are uninformative and non-descriptive
Solution Approach 1:
The system calculates metrics (minimum, maximum, mean, geometric mean) for each parameter within gated populations and uses these metrics to generate descriptive labels. This feedback loop transforms raw data into meaningful information that describes population characteristics
Solution Approach 2:
The invention changes the parameter representation by computing statistical metrics (min, max, mean, geometric mean) for each parameter dimension and using these transformed parameters to create informative labels that reflect population characteristics
2Loss of information
If manual gate labeling is performed to ensure descriptive labels, then label quality improves, but analysis time increases significantly
Solution Approach 1:
The system performs self-service by automatically generating descriptive labels through algorithmic processing of gate metrics. The computer automatically calculates parameters, determines population characteristics, and creates labels without human intervention, eliminating the time cost of manual labeling
3Device complexity
If default sequential labeling is used for gates, then device complexity is minimized, but data interpretation becomes difficult
Solution Approach 1:
The system transforms simple sequential labels into descriptive labels by incorporating parameter metrics (min, max, mean, geometric mean) and population characteristics. This parameter enrichment makes labels self-explanatory while maintaining automated generation
Solution Approach 2:
The invention introduces an intermediary processing layer that calculates statistical metrics between the raw gate data and the final label. This intermediary step translates complex multidimensional data into intuitive descriptive labels that facilitate interpretation
Data Source
Figure 1A~1B
Figure 2
Figure 3A~3B
AI summary
The present disclosure provides methods of labeling flow cytometry data. Methods of interest include: receiving a flow cytometry gate input including a portion of the cytometry data; identifying a plurality of parameters, each parameter associated with a dimension of a data space of the portion of the cytometry data; calculating a metric for each parameter of at least a portion of the plurality of parameters based on a magnitude of the cytometry data associated with the respective parameter's dimension; and generating a label for the flow cytometry gate input based on at least one metric and a predetermined magnitude threshold associated with the metric. The subject methods may be implemented automatically via computer. Systems and non-transitory computer-readable storage media for carrying out the subject methods are also provided.