Biological Particle Sorting via Data Compression and Statistical Models
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Solution Overview
Problem
Sorting apparatuses, such as cell sorters, face challenges in rapidly and accurately determining whether to sort particles based on measurement data from flow cytometers, particularly due to increased complexity with multiple fluorescent substances, leading to lengthy calculation times and difficulties in real-time analysis.
Innovation Solution
An information processing system that applies data compression to light emission data from biological particles, uses statistical models trained on compressed data to sort particles, and controls the sorting apparatus based on these models, enabling swift decision-making independent of the number of fluorescent substances.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the number of fluorescent substances is increased to enable more detailed particle analysis, then measurement capability is improved, but data complexity and analysis difficulty increase
Solution Approach 1:
The patent transforms the complex multi-dimensional fluorescence data into a simplified one-dimensional pulse waveform representation. By changing the data representation parameter from multi-dimensional intensity values to a temporal waveform, the system maintains the ability to analyze multiple fluorescent substances while dramatically reducing data complexity and enabling real-time processing.
2Measurement precision
If traditional analysis methods are used on multi-dimensional fluorescence data, then measurement precision is maintained, but calculation time increases and real-time sorting becomes difficult
Solution Approach 1:
The patent replaces traditional mechanical computation methods with optical signal processing. By using the temporal characteristics of fluorescence emission to generate pulse waveforms, the system performs analysis at the speed of light rather than through sequential computational operations, achieving real-time processing without sacrificing measurement precision.
Solution Approach 2:
The system performs data transformation and analysis preparation in advance by converting fluorescence signals into pulse waveforms during the measurement process itself. This preliminary transformation enables rapid classification and sorting decisions to be made without requiring complex real-time calculations, thus reducing calculation time while maintaining accuracy.
3Manufacturing precision
If detailed analysis of multi-dimensional data is performed, then sorting accuracy is improved, but processing speed decreases
Solution Approach 1:
The patent extracts the essential classification information from complex multi-dimensional fluorescence data by transforming it into a simplified pulse waveform representation. This extraction process isolates the critical temporal characteristics needed for accurate sorting while discarding redundant information, thereby maintaining sorting accuracy while dramatically improving processing speed.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach allows for rapid determination of which biological particles to sort, reducing calculation time and simplifying the analysis process, enabling real-time sorting without being hindered by the complexity of multi-dimensional data from flow cytometers.
Implementation Method 1
A flow cytometer is an apparatus that measures properties of each particle by applying rays of light to particles, such as flowing cells or beads, and detecting fluorescence, etc., that is emitted by the particles.
Data Source
AI summary
Techniques for sorting biological particles are described. The techniques may include applying a data compression process to data indicating light emitted from biological particles and outputting, based on a result of the data compression process, one or more groups of the biological particles to sort into additional groups of the biological particles. The techniques may further include using at least some of the data corresponding to the one or more groups of the biological particles in training at least one statistical model, wherein an output of the at least one statistical model specifies an indication to sort one or more of the biological particles.