Adaptive Data Sub-sampling for Flow Cytometry Analysis
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Solution Overview
Problem
Performing analysis on large, multi-parameter datasets from biological experiments, such as flow cytometry data, is computationally costly and time-consuming, making it difficult to provide real-time results, especially when iterative adjustments are needed, and existing methods for reducing dataset size often lead to biased or inaccurate results.
Innovation Solution
Adaptive sub-sampling of event data based on a determined data sub-sampling ratio to reduce analysis computation time, allowing for a representative analysis that can be performed quickly while maintaining accuracy by selecting a subset of events rather than omitting whole samples or parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the full dataset is analyzed, then analysis accuracy is maintained, but analysis computation time increases significantly
Solution Approach 1:
The patent applies partial action by analyzing only a sub-sample of events rather than the complete dataset. The system determines an appropriate sub-sampling ratio and selects a representative subset of events for analysis, achieving acceptable accuracy with reduced computational burden. This allows iterative analysis to be performed quickly while maintaining sufficient representativeness of the full dataset.
2Loss of time
If existing dataset reduction methods are applied, then analysis computation time is reduced, but analysis results become biased and unrepresentative
Solution Approach 1:
The patent changes the parameter of data selection from complete inclusion to selective sub-sampling based on a determined ratio. By adjusting the sub-sampling ratio parameter and using appropriate selection methods, the system maintains the representativeness of results while reducing dataset size. The sub-sample is carefully selected to preserve the statistical properties and distribution characteristics of the full dataset.
3Productivity
If data sub-sampling is applied, then analysis computation time is reduced, but analysis accuracy may deteriorate
Solution Approach 1:
The patent implements dynamic adjustment of the sub-sampling ratio based on performance criteria. The system can adaptively determine the appropriate level of sub-sampling to achieve, balancing speed and accuracy requirements. This dynamic approach allows the analysis to be performed at the optimal point where sufficient accuracy is maintained while maximizing computational efficiency.
Data Source
AI summary
The multi-parameter data produced via flow cytometry and other biological analyses techniques can generate enormous amounts of data, which can take extensive time and/or computational resources to complete. Embodiments provided herein allow for adaptive sub-sampling of such data prior to analysis, allowing for such analyses to be performed while satisfying certain performance criteria. Such performance criteria may include, for example, keeping the latency of the analysis below a specified duration. This can allow analysis of data to be performed in real time as the data is generated, e.g., as flow cytometry data is generated by a cell counter or other flow cytometry instrument. This can also permit for data analyses to be iteratively developed or improved in less time by adaptively sub-sampling the data prior to re-analysis, so that the total time between iterations is reduced.